CN116956554A - Traffic simulation processing method and device, electronic equipment and storage medium - Google Patents

Traffic simulation processing method and device, electronic equipment and storage medium Download PDF

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Publication number
CN116956554A
CN116956554A CN202310743007.1A CN202310743007A CN116956554A CN 116956554 A CN116956554 A CN 116956554A CN 202310743007 A CN202310743007 A CN 202310743007A CN 116956554 A CN116956554 A CN 116956554A
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vehicle
traffic
ramp
traffic density
target
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杜海宁
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Tencent Technology Shenzhen Co Ltd
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Tencent Technology Shenzhen Co Ltd
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Priority to CN202310743007.1A priority Critical patent/CN116956554A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing

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  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
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  • General Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Traffic Control Systems (AREA)

Abstract

The embodiment of the application discloses a traffic simulation processing method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: dividing a target area which is not covered by sensing equipment in a traffic simulation scene into a plurality of subareas; acquiring a first traffic density of a reference area in a traffic simulation scene, and predicting a second traffic density of a sub-area with a ramp according to the first traffic density, wherein the reference area is covered by sensing equipment; determining a target traffic density of the ramp according to the second traffic density and the first proportional coefficient; and determining the average distance between vehicles on the ramp according to the target traffic density, and initializing virtual vehicles in the ramp according to the average distance between vehicles. The embodiment of the application can effectively initialize the virtual vehicles in the ramp, so that the traffic state of the road displayed in the target area in the visualization process has better continuity in time and space, the visualization effect is improved, and the method and the device can be widely applied to the technical fields of traffic simulation, automatic driving, intelligent traffic and the like.

Description

Traffic simulation processing method and device, electronic equipment and storage medium
Technical Field
The present application relates to the field of traffic simulation technologies, and in particular, to a traffic simulation processing method, a device, an electronic device, and a storage medium.
Background
Traffic simulation is a technology for researching traffic behavior by using simulation technology, and describes the change of traffic movement along with time and space by establishing a mathematical model of real-time movement of a traffic transportation system in a certain period. Before the traffic simulation method is perceived and reproduced, the initial state of the perceived coverage area of the traffic simulation scene is set by utilizing data acquired by the perception equipment.
At present, for a perception blind area which is not covered by a perception device, as no data is acquired by the perception device, the continuity of the traffic state of a road displayed in the visual process of the perception blind area in time and space is poor, and the visual effect is poor.
Disclosure of Invention
The following is a summary of the subject matter of the detailed description of the application. This summary is not intended to limit the scope of the claims.
The embodiment of the application provides a traffic simulation processing method, a traffic simulation processing device, electronic equipment and a storage medium, which can effectively initialize virtual vehicles in a ramp and improve the visual effect.
In one aspect, an embodiment of the present application provides a traffic simulation processing method, including:
dividing a target area which is not covered by sensing equipment in a traffic simulation scene into a plurality of subareas, wherein a ramp exists in at least one subarea;
Acquiring a first traffic density of a reference area in the traffic simulation scene, and predicting a second traffic density of the sub-area where the ramp exists according to the first traffic density, wherein the reference area is covered by the sensing equipment;
determining the target traffic density of the ramp according to the second traffic density and a preset first proportional coefficient;
and determining the average distance between vehicles of the ramp according to the target traffic density, and initializing a virtual vehicle in the ramp according to the average distance between vehicles.
In another aspect, an embodiment of the present application further provides a vehicle processing apparatus, including:
the system comprises a dividing module, a sensing module and a judging module, wherein the dividing module is used for dividing a target area which is not covered by sensing equipment in a traffic simulation scene into a plurality of subareas, and a ramp exists in at least one subarea;
the prediction module is used for obtaining a first traffic density of a reference area in the traffic simulation scene, and predicting a second traffic density of the sub-area with the ramp according to the first traffic density, wherein the reference area is covered by the sensing equipment;
the density determining module is used for determining the target traffic density of the ramp according to the second traffic density and a preset first proportion coefficient;
And the initialization module is used for determining the average distance between vehicles of the ramp according to the target traffic density and initializing a virtual vehicle in the ramp according to the average distance between the vehicles.
Further, the prediction module is specifically configured to:
determining the ordering positions of the subareas with the ramp in a plurality of subareas;
and interpolating traffic densities corresponding to the sorting positions according to the first traffic densities of the reference areas positioned at the upstream and downstream of the target area to obtain a second traffic density of the subarea with the ramp.
Further, the prediction module is specifically configured to:
when the first traffic density of the reference area positioned at the upstream of the target area is smaller than or equal to the first traffic density of the reference area positioned at the downstream of the target area, determining a traffic density difference value between the two reference areas, determining a first interpolation item according to the traffic density difference value and the sorting position, and obtaining a second traffic density of the subarea with the ramp according to the sum of the first interpolation item and the first traffic density of the reference area positioned at the upstream of the target area;
Or when the first traffic density of the reference area positioned at the upstream of the target area is larger than the first traffic density of the reference area positioned at the downstream of the target area, determining a traffic density difference value between the two reference areas, determining a first interpolation item according to the traffic density difference value and the sorting position, and obtaining a second traffic density of the subarea with the ramp according to the sum of the first interpolation item and the first traffic density of the reference area positioned at the downstream of the target area.
Further, the initialization module is specifically configured to:
taking the direction from one end of the ramp to the other end of the ramp as an initialization direction, and sequentially determining initial positions of a plurality of virtual vehicles in the ramp along the initialization direction according to the average vehicle spacing;
initializing the virtual vehicle in each of the initial positions.
Further, the initialization module is specifically configured to:
constructing normal distribution by taking the average vehicle spacing as a mean value, and sequentially and randomly acquiring a plurality of initial vehicle spacing from the normal distribution;
and sequentially determining initial positions of a plurality of virtual vehicles in the ramp according to the initial intervals of the vehicles along the initialization direction.
Further, the initialization module is further configured to:
determining the vehicle speed of each initial position according to the initial vehicle distance;
the vehicle speed is determined as an initial speed of the virtual vehicle at the initial position.
Further, the ramp is an up ramp, and the initialization module is specifically configured to:
traversing each initial position, and calculating the reciprocal of the initial distance between the current initial position and the previous initial position of the vehicle to obtain a third traffic density of the current initial position;
and acquiring a traffic basic diagram for representing a mapping relation between the vehicle speed and the traffic density, and searching the vehicle speed at the current initial position from the traffic basic diagram according to the third traffic density.
Further, the ramp is a down ramp, and the initialization module is specifically configured to:
traversing each initial position, if the current initial position is not the last traversed to the initial position, calculating the reciprocal of the initial distance between the current initial position and the previous initial position of the vehicle to obtain a fourth traffic density of the current initial position, obtaining a traffic basic diagram for representing the mapping relation between the vehicle speed and the traffic density, and searching the vehicle speed of the previous initial position from the traffic basic diagram according to the fourth traffic density;
And if the current initial position is the last traversed to the initial position, acquiring the highest speed limit of the ramp, and taking the highest speed limit as the vehicle speed of the current initial position.
Further, the initialization module is specifically configured to:
determining the ordering positions of the subareas with the ramp in a plurality of subareas;
acquiring first vehicle type duty ratios of various candidate vehicle types in the reference areas at the upstream and downstream of the target area, and interpolating the vehicle type duty ratios corresponding to the sequencing positions according to the first vehicle type duty ratios to obtain second vehicle type duty ratios of various candidate vehicle types in the subarea with the ramp;
initializing a virtual vehicle in the ramp according to the second vehicle type duty ratio and the vehicle average distance.
Further, the initialization module is specifically configured to:
when the first vehicle type duty ratio of the reference area positioned at the upstream of the target area is smaller than or equal to the first vehicle type duty ratio of the reference area positioned at the downstream of the target area, determining a vehicle type duty ratio difference between the two reference areas, determining a second interpolation item according to the vehicle type duty ratio difference and the sequencing position, and obtaining a second vehicle type duty ratio of the subarea with the ramp according to the sum of the second interpolation item and the first vehicle type duty ratio of the reference area positioned at the upstream of the target area;
Or when the first vehicle type duty ratio of the reference area positioned at the upstream of the target area is larger than the first vehicle type duty ratio of the reference area positioned at the downstream of the target area, determining a vehicle type duty ratio difference between the two reference areas, determining a second interpolation item according to the vehicle type duty ratio difference and the sequencing position, and obtaining a second vehicle type duty ratio of the subarea with the ramp according to the sum of the second interpolation item and the first vehicle type duty ratio of the reference area positioned at the downstream of the target area.
Further, the initialization module is specifically configured to:
dividing a target numerical value interval into a plurality of sub-numerical value intervals according to the duty ratio of each second vehicle type;
randomly generating a target value in the target value interval, and determining a target vehicle type from a plurality of candidate vehicle types according to the attribution relation between the target value and a plurality of sub-value intervals;
initializing a virtual vehicle in the ramp according to the target vehicle type and the average vehicle distance.
Further, the ramp comprises a plurality of target lanes, and the initialization module is specifically configured to:
determining a fifth traffic density of each target lane according to the target traffic density and a preset second proportionality coefficient;
And calculating the reciprocal of each fifth traffic density to obtain the average vehicle distance of each target lane.
On the other hand, the embodiment of the application also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the traffic simulation processing method when executing the computer program.
In another aspect, an embodiment of the present application further provides a computer readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement the traffic simulation processing method described above.
In another aspect, embodiments of the present application also provide a computer program product comprising a computer program stored in a computer readable storage medium. The processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program so that the computer device executes the traffic simulation processing method described above.
The embodiment of the application at least comprises the following beneficial effects: the method comprises the steps of dividing a target area which is not covered by sensing equipment in a traffic simulation scene into a plurality of subareas, predicting second traffic density of the subareas with the ramps by using first traffic density of a reference area which is covered by the sensing equipment, wherein the first traffic density is determined by data acquired by the sensing equipment, so that the traffic density of the subareas can be accurately predicted, then determining the target traffic density of the ramps according to the second traffic density and the first proportion coefficient, which is equivalent to determining the target traffic density by the traffic density proportion of the ramps, determining the accuracy of the target traffic density is higher, then obtaining the average vehicle distance of the ramps by the target traffic density of the ramps, and determining the initial state of virtual vehicles in the ramps by the average vehicle distance, so that the virtual vehicles can accord with the traffic running rule, the virtual vehicles can be effectively initialized in the ramps, and the traffic state of the road displayed in the target area in the visualization process is good in time and space continuity, and the visualization effect is improved.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and do not limit the application.
FIG. 1 is a schematic illustration of an alternative implementation environment provided by an embodiment of the present application;
FIG. 2 is a schematic flow chart of an alternative traffic simulation processing method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an alternative topology of an expressway provided by an embodiment of the application;
FIG. 4 is a schematic view of an alternative region division of an up-ramp merge region according to an embodiment of the present application;
FIG. 5 is a schematic diagram of an alternative area division of an off-ramp split area according to an embodiment of the present application;
FIG. 6 is a schematic view of an alternative region division of a sensing region provided by an embodiment of the present application;
FIG. 7 is a schematic diagram of an alternative region division of the region where the ramp is located according to an embodiment of the present application;
FIG. 8 is a schematic diagram of an alternative region division of the region where the down-ramp is located according to an embodiment of the present application;
FIG. 9 is a schematic view of region division at the start of the simulation run provided by an embodiment of the present application;
FIG. 10 is a schematic diagram of an alternative topology of an area where an up ramp is located according to an embodiment of the present application;
FIG. 11 is a schematic diagram of an alternative topology of an area where an on-ramp is located according to an embodiment of the present application;
FIG. 12 is an alternative schematic illustration of historical traffic data provided by an embodiment of the present application;
FIG. 13 is an alternative schematic illustration of a traffic base map provided by an embodiment of the present application;
FIG. 14 is an alternative schematic view of a traffic base map provided by an embodiment of the present application;
FIG. 15 is a schematic diagram of an alternative vehicle spacing relationship for an up-ramp provided by an embodiment of the present application;
FIG. 16 is a schematic illustration of an alternative vehicle spacing relationship for an off ramp provided by an embodiment of the present application;
FIG. 17 is a schematic flow chart of an alternative embodiment of the initial state setting of the ramp according to the present application;
FIG. 18 is a schematic flow chart of an alternative embodiment of the initial state setting of the down-ramp;
FIG. 19 is a schematic view of an alternative architecture of a traffic simulation device according to an embodiment of the present application;
Fig. 20 is a partial block diagram of a terminal according to an embodiment of the present application;
fig. 21 is a partial block diagram of a server according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In the embodiments of the present application, when related processing is performed according to data related to characteristics of a target object, such as attribute information or attribute information set of the target object, permission or consent of the target object is obtained first, and related laws and regulations and standards are complied with for collection, use, processing, etc. of the data. Wherein the target object may be a user. In addition, when the embodiment of the application needs to acquire the attribute information of the target object, the independent permission or independent consent of the target object is acquired through a popup window or a jump to a confirmation page or the like, and after the independent permission or independent consent of the target object is explicitly acquired, the necessary target object related data for enabling the embodiment of the application to normally operate is acquired.
In order to facilitate understanding of the technical solution provided by the embodiments of the present application, some key terms used in the embodiments of the present application are explained here:
microcosmic traffic simulation: the details of the elements and behavior of the traffic system are described to the highest degree. For example, the description of the traffic flow by the microscopic traffic simulation model takes a single vehicle as a basic unit, and microscopic behaviors such as following, overtaking and lane changing of the vehicle on a road can be truly reflected.
Microscopic map: a map satisfying the running requirements of microscopic traffic simulation is composed of "roads" and "connection links" between roads, for example, "connection links" may be understood as intersection links between two roads. The "road" and the "connection section" in the microscopic map are each composed of a plurality of discrete points, and the information of each discrete point includes the longitude and latitude of the discrete point.
Microscopic vehicle: microscopic traffic simulation requires elaborate description of the traffic behavior of vehicles, such as lane changes of vehicles, behavior of vehicles at intersections, interactions between vehicles, and so forth. Thus, the microscopic vehicles have vehicle traffic data more specific, such as longitude and latitude of the vehicle (i.e., global coordinates of the vehicle), head orientation of the vehicle, speed of the vehicle, and the like.
At present, for a perception blind area which is not covered by a perception device, as no data is acquired by the perception device, the continuity of the traffic state of a road displayed in the visual process of the perception blind area in time and space is poor, and the visual effect is poor.
Based on the above, the embodiment of the application provides a traffic simulation processing method, a device, electronic equipment and a storage medium, which can effectively initialize virtual vehicles in a ramp, so that the traffic state of a road displayed in a target area in visualization has better continuity in time and space, and the visualization effect is improved.
Referring to fig. 1, fig. 1 is a schematic diagram of an alternative implementation environment provided in an embodiment of the present application, where the implementation environment includes a terminal 101 and a server 102, where the terminal 101 and the server 102 are connected through a communication network.
For example, the server 102 may divide a target area in the traffic simulation scene that is not covered by the sensing device into a plurality of sub-areas, wherein a ramp is present in at least one of the sub-areas; acquiring a first traffic density of a reference area in a traffic simulation scene, and predicting a second traffic density of a sub-area with a ramp according to the first traffic density, wherein the reference area is covered by sensing equipment; determining the target traffic density of the ramp according to the second traffic density and a preset first proportional coefficient; the average distance between vehicles on the ramp is determined according to the target traffic density, virtual vehicles are initialized in the ramp according to the average distance between vehicles, the initial state setting is further completed, then traffic simulation can be carried out in a perception reproduction stage, and a traffic simulation result is sent to the terminal 101.
The server 102 predicts the second traffic density of the sub-region with the ramp by dividing the target region which is not covered by the sensing device in the traffic simulation scene into a plurality of sub-regions and then using the first traffic density of the reference region which is covered by the sensing device, and as the first traffic density is determined by the data collected by the sensing device, the traffic density of the sub-region can be accurately predicted, and then the target traffic density of the ramp is determined according to the second traffic density and the first ratio coefficient, which is equivalent to the determination of the target traffic density by the traffic density ratio of the ramp, the accuracy of the target traffic density is higher, and then the average vehicle spacing of the ramp is obtained by the target traffic density of the ramp, and the initial state of the virtual vehicle in the ramp is determined by the average vehicle spacing, so that the virtual vehicle can accord with the traffic running rule, the virtual vehicle can be effectively initialized in the ramp, the traffic state of the road displayed in the visualization is better in time and space, and the visualization effect is improved.
The server 102 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or may be a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs (Content Delivery Network, content delivery networks), basic cloud computing services such as big data and artificial intelligence platforms, and the like. In addition, server 102 may also be a node server in a blockchain network.
The terminal 101 may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, etc. The terminal 101 and the server 102 may be directly or indirectly connected through wired or wireless communication, and embodiments of the present application are not limited herein.
The method provided by the embodiment of the application can be applied to various technical fields including but not limited to the technical fields of traffic simulation, automatic driving, intelligent traffic and the like.
Referring to fig. 2, fig. 2 is a schematic flow chart of an alternative traffic simulation processing method according to an embodiment of the present application, where the traffic simulation processing method may be executed by a server, or may be executed by a terminal, or may be executed by a server in conjunction with the terminal, and the traffic simulation processing method includes, but is not limited to, the following steps 201 to 204.
Step 201: dividing a target area which is not covered by sensing equipment in a traffic simulation scene into a plurality of subareas;
wherein a ramp is present in at least one of the sub-regions.
The traffic simulation scene refers to a virtual scene obtained by digitally twinning road section areas, the sensing equipment can be road side sensing equipment, for example, the sensing equipment can be a camera, a laser radar, a millimeter wave radar and the like, the road section areas covered by the sensing equipment are defined as sensing areas, the road section areas not covered by the sensing equipment are defined as sensing blind areas, the sensing areas are equivalent to the road section areas within the sensing range of any one sensing equipment, the sensing blind areas are the road section areas outside the sensing range of all sensing equipment, so that the target areas in the traffic simulation scene are the sensing blind areas, then the road section areas with larger range can be divided along the extending direction of a main road, the road section areas with smaller range can be obtained by dividing every other distance, the subareas in the traffic simulation scene can correspond to the road section areas with smaller range, the traffic state of each subarea with smaller range in the sensing blind area can be predicted more accurately through area division processing, and the continuity of the traffic state of the road in time and space can be improved.
The traffic simulation scene is a virtual scene obtained by digitally twinning road section areas of an expressway, the sensing equipment is road side sensing equipment on the expressway, the whole road section areas of the expressway are divided along the extending direction of a main line of the expressway to obtain a plurality of road section areas with smaller ranges, particularly in the traffic simulation scene, a target area can be divided to obtain a plurality of subareas, whether a ramp exists in each subarea can be determined according to the road section areas where the ramps exist in the expressway, and after the presence of the ramp in one subarea is determined, the virtual vehicle can be initialized in the ramp.
Specifically, referring to fig. 3, fig. 3 is a schematic diagram of an alternative topology of an expressway according to an embodiment of the present application.
Wherein A in the circle represents an upper ramp, B in the circle represents a lower ramp, the corresponding inter-ramp trunk line section is determined by two ramp numbers on the upstream and downstream of the inter-ramp trunk line section, such as a trunk line between the 23-section, namely the No. 2 and the No. 3-ramp, and K is used ij Representing traffic density (units: number of vehicles/km) over road segments i j, e.g. K A3 Refers to the density, K, on the No. 3 ramp 23 Refers to the density on the main line between ramp number 2 and ramp number 3.
Specifically, referring to fig. 4, fig. 4 is an optional area division schematic diagram of an up-ramp merging area according to an embodiment of the present application.
The converging region consists of three parts, namely a main line lane, an accelerating lane parallel to the main line and a ramp connected with the accelerating lane.
Specifically, referring to fig. 5, fig. 5 is a schematic diagram of an optional area division of an off-ramp shunting area according to an embodiment of the present application.
The split area consists of three parts, namely a main line lane, an auxiliary lane parallel to the main line and a ramp connected with the auxiliary lane.
The system boundary of the digital twin simulation system can be a ramp entrance and a ramp-exit, and an intersection associated with the ramp is not considered, namely, the vehicle is considered to directly enter the digital twin simulation system from the ramp-up without passing through the intersection possibly connected with the digital twin simulation system; the vehicles can be regarded as having exited the digital twin simulation system when exiting the ramp, and the vehicles can not be limited by the signal lamp of the intersection connected with the digital twin simulation system in reality.
The digital twin is a technical means for creating a virtual entity of a physical entity in a digital mode, simulating, verifying, predicting and controlling the whole life cycle process of the physical entity by means of historical data, real-time data, an algorithm model and the like. The digital twin can be realized by establishing virtual parallel world of the expressway, mapping the environment, vehicles, events and other elements of physical world of the expressway completely in real time, fully sensing and dynamically monitoring the sensor data distributed in the expressway, forming the accurate information expression and mapping of the virtual road to the physical road in the information dimension, so that the manager can master the global condition of the expressway without on-site expressway, and the problems of difficult detection, delay of event discovery, difficult event duplication and the like of the whole road section are solved. It has not only simulation capability, but also prediction and control capability.
The method comprises the steps of automatically bearing and fusing information acquired by multidimensional traffic facilities such as videos and radars in road sections which can be covered by sensing equipment, mutually proving and supplementing original incoherent target information acquired by various sensors through a target fusion algorithm to form basically complete target attribute information, and accurately describing a vehicle running track on a high-speed main line.
For example, a radar-detected target and a video-identified target are linked through an association relationship of a map. Meanwhile, the real-time detection target is overlapped on the high-precision map, so that the butt joint of the physical space and the virtual space is realized, the holographic perception of digital mapping is finished, and the high-precision map can be a micro map. The digital twin simulation system can be further subjected to real-time reproduction simulation, simulation deduction is carried out on the basis of the digital twin simulation system, and core services such as traffic hidden danger, traffic event, traffic jam and the like are described, diagnosed, predicted and decided, so that real-time efficient intelligent analysis and active management and control are achieved, and finally closed-loop control is achieved, thereby realizing the refinement, the intellectualization, the standardization and the specialization of highway management and laying a solid foundation for traffic management.
The sensing equipment can extract the state information of the structured road and the target object, so that the comprehensive sensing of elements such as vehicles, roads and events and the like, and the functions of positioning and target fusion pursuit of a centimeter-level target lane and the like are realized. By means of algorithms such as target detection, vision pursuit and track splicing in the twin technology, when the vehicle runs in a perception range, key information (such as vehicle type, speed, position, course angle and the like) of the vehicle can be solved, and real-time determination of the running track of the vehicle is realized.
Specifically, referring to fig. 6, fig. 6 is an optional area division schematic diagram of a sensing area according to an embodiment of the present application.
The sensing equipment on the main line side of the expressway has a certain coverage area, is limited by the type of equipment (such as millimeter wave radar, a camera and the like) and weather conditions and the like, the expressway main line part effectively covered by the equipment is defined as a sensing coverage area, the sensing coverage area is equivalent to the sensing area, the full vehicle information in the area can be ensured to be sensed and collected by the equipment, and the full vehicle information can be uploaded to a simulation system in real time under the condition of small delay, so that complete mapping reproduction of the information can be carried out in the simulation. If there are no other perceived coverage areas upstream of the road of one perceived coverage area, this upstream area is defined as the perceived upstream area, and if there are no other perceived coverage areas downstream of the road of one perceived coverage area, this downstream area is defined as the perceived downstream area. Since the area covered by the device may be a segmented coverage, i.e. there is an uncovered area between the two coverage areas, such an area is defined as a perceived midstream area. The boundary between the sensing coverage area and the upstream and downstream areas is defined as a sensing upper and lower boundary, a st coordinate system of a main line is constructed, under the st coordinate system, the sensing upper and lower boundary is perpendicular to the direction of the lane, and the area beyond the sensing upper and lower boundary will not sense and receive the track information of the vehicle. The simulation performed in the sensing coverage area is defined as a reproduction simulation, namely, the sensed vehicle information is completely restored into the simulation system in real time, and for the sensing upstream area, the sensing midstream area and the sensing downstream area which are sensing blind areas, a virtual simulation in space is required to be performed according to the existing information.
The perceived midstream region can be simply divided into four cases according to the road topology: 1. no ramp exists in the area, and only a main road exists; 2. the upper ramp is arranged in the area, and the lower ramp is not arranged; 3. the region is provided with an under ramp, and no upper ramp exists; 4. and an up ramp and a down ramp exist in the region at the same time. The upper ramp of the expressway is connected with the main line through a converging area, and the lower ramp of the expressway is connected with the main line through a diverging area. It is considered that if a ramp is in a blind sensing area (i.e., not covered by sensing equipment), then a junction area or a diversion area where the ramp is connected to the trunk line is also in a blind sensing area.
Based on this, because the area covered by the sensing device on the expressway is limited, the area of all road sections with ramps cannot be covered, the simulated virtual vehicle in the sensing blind area is provided with a certain randomness, when the road section of the sensing blind area is long, the continuity of the traffic state cannot be ensured by the existing traffic simulation, the existing simulation system is difficult to form accurate digital mapping to the physical space, in the prior art, a visual void can appear when the twin visual display is performed, namely, the vehicle can be displayed in the sensing area, and the vehicle is not displayed in part of the area of the sensing blind area, for example, the vehicle is not displayed in the ramp of the sensing blind area, so that the visual experience is poor, and the visual effect is poor.
Specifically, referring to fig. 7, fig. 7 is an optional area division schematic diagram of an area where an up ramp is located according to an embodiment of the present application.
Generating a virtual boundary at the nose end at the upstream of the merging region, generating a new virtual boundary at a position with a certain distance by taking the virtual boundary as a reference, and generating the virtual boundary at the position with a changed number of lanes, so that the virtual boundary is generated at the end of an acceleration lane of an upper ramp, the virtual boundary is perpendicular to the direction of the lane under a st coordinate system constructed by a main line, and then dividing a perceived midstream region to obtain a length D of each segment L The perception of the mid-trip section corresponds to dividing the target area based on each virtual boundary to obtain a plurality of sub-areas. Embodiment of the application pair D L The value of (2) is not limited, for example, D L 200 meters can be used, and the sub-area where the upper ramp is located is not segmented, namely the whole upper ramp is considered to be located in the sub-area.
Specifically, referring to fig. 8, fig. 8 is an optional area division schematic diagram of an area where a down-ramp is located according to an embodiment of the present application.
Generating a virtual boundary at a split point at the downstream of a split area, generating a new virtual boundary at a position at a certain distance by taking the virtual boundary as a reference, and generating the virtual boundary at the position of a lane number change, so that the virtual boundary is generated at the beginning of an auxiliary lane of a lower ramp, the virtual boundary is perpendicular to the lane direction under a st coordinate system constructed by a main line, and then dividing a perceived midstream area to obtain a length D of each section L The perception of the mid-trip section corresponds to dividing the target area based on each virtual boundary to obtain a plurality of sub-areas. Embodiment of the application pair D L The value of (2) is not limited, for example, D L 200 meters can be used, and the subareas where the down-ramps are located are not segmented, namely the whole down-ramps are considered to be in the subareas.
The traffic simulation processing method provided by the embodiment of the application can be a microscopic traffic simulation processing method, and microscopic simulation road condition data can be generated through microscopic traffic simulation. The microscopic traffic simulation is traffic simulation taking an individual vehicle as a visual angle, namely traffic simulation taking the individual vehicle as a research object, and mainly focuses on the detailed influence of a single vehicle on traffic. Microcosmic traffic simulation is a simulation technology for finely describing behaviors such as overtaking, following, lane changing and the like from a single vehicle view angle in millisecond level. The microscopic simulation road condition data can specifically include information such as a running path, a departure point, a destination, positions and states of the vehicles at various moments, road surrounding environments and the like of the vehicles. It can be understood that the micro-simulation road condition data includes more detailed information, such as surrounding environment of the road, and the micro-simulation road condition data is suitable for rendering and displaying.
In the micro traffic simulation system, the simulation system updates the speed, the position and the like of the virtual vehicle once every time the simulation clock is pushed, the motion behavior of the virtual vehicle can be described by a micro driving behavior model such as following, lane changing and the like, and the simulation system is divided into an initial state setting stage and a perception reproduction stage in terms of a time axis.
In the initial state setting stage, an initial state when the simulation starts to run needs to be set at the simulation starting time, wherein the initial state of the real vehicle perceived at the moment in the perception area needs to be set, and the initial state of the virtual vehicle of the perception blind area also needs to be set;
after the digital twin simulation starts to run in the perception reproduction stage, the perception equipment transmits vehicle information (such as position, speed, attitude and other states) in the coverage area back to the digital twin simulation system in real time, and the vehicle information is reproduced and displayed in the simulation system, and the virtual vehicle in the perception blind area is simulated according to a certain model.
Specifically, referring to fig. 9, fig. 9 is a schematic view of region division at the time of starting the simulation according to the embodiment of the present application.
After the simulation starts to run, the simulation system starts to receive the real-time vehicle track transmitted by the sensing equipment, and reproduces the states of the type, the position, the speed, the gesture (direction angle) and the like of the vehicle on the road section corresponding to the sensing coverage area in the simulation system in real time. Along with the continuous advancement of the simulation clock, track data are continuously injected into the simulation system, and the vehicle states of the existing tracks are reproduced one by one in the sensing coverage area. For the area outside the sensing coverage area, in order to keep the authenticity of the simulation effect, the running of the vehicle in the area is simulated by updating the longitudinal (following algorithm model) and transverse (lane changing model) speeds by the model by itself, without adopting a mode of directly modifying the speeds. After the simulation starts, the vehicle in the upstream perception coverage area passes through the perception lower boundary to enter the perception midstream region, the microscopic traffic model controls the microscopic driving behavior of the virtual vehicle, such as following, lane changing and the like, and when the virtual vehicle passes through a downstream preset vehicle receiving line, the virtual vehicle is removed from the system so as to avoid collision and visual overlapping with the vehicle in the downstream perception reproduction when the virtual vehicle enters the downstream perception coverage area.
Step 202: acquiring a first traffic density of a reference area in a traffic simulation scene, and predicting a second traffic density of a sub-area with a ramp according to the first traffic density;
wherein the reference area has been covered by the perceiving device.
Wherein the first traffic density and the second traffic density are both traffic densities, the traffic densities refer to the number of vehicles on a road section with a unit length, for example, the unit length is usually 1 km, the first traffic density of the reference area refers to the number of vehicles per km in the reference area, and the second traffic density of the sub-area refers to the number of vehicles per km in the sub-area.
When the road segment area is digitally twined to obtain a traffic simulation scene, the reference area refers to a sensing area which is covered by sensing equipment in the road segment area, namely, the sensing area refers to a road segment area which is positioned in a sensing range of any one sensing equipment, the sensing equipment can collect traffic flow parameters in the sensing area, for example, the traffic flow parameters can be traffic flow, traffic density, vehicle speed and the like, the virtual vehicles need to be initialized in the ramp of the subarea before the sensing reproduction stage, and because the reference area and the subarea are in the same traffic simulation scene, the first traffic density of the reference area and the second traffic density of the subarea are related, so that in the initial state setting stage, the second traffic density of the subarea with the ramp can be predicted through the first traffic density of the reference area, and the initial state of the virtual vehicles in the ramp can be determined through the second traffic density.
In the reference area, the sensing device may continuously collect vehicle information data in the reference area in real time, for example, vehicle type, speed, position, heading angle, etc., after collecting the real-time collected information, the simulation system may aggregate the traffic flow parameters in different space and time dimensions, so as to obtain the traffic flow parameters in time intervals in the sensing area.
For example, assuming that the reference area located upstream of the target area is the first perceived coverage area, the reference area located downstream of the target area is the second perceived coverage area, and the simulation start time is T 0 The length of the time period is set to T w Therefore, in the initial state setting phase, the first perceived coverage is set to be in the period [ T ] 0 -T w ,T 0 ) The traffic density in the network is aggregated into one of the first traffic densities, namely the upstream traffic density K up And placing the second perceived coverage area in a time period [ T 0 -T w ,T 0 ) The traffic density in the network is aggregated into another first traffic density, namely the downstream traffic density K down Then according to K up And K diwn A second traffic density of the sub-region where the ramp exists may be predicted.
Wherein T is w Setting time for initial state, the embodiment of the application sets T to w The value of (2) is not particularly limited, for example, T w And may be 5 minutes, 10 minutes, etc.
In one possible implementation manner, the second traffic density of the sub-region with the ramp is predicted according to the first traffic density, specifically, the ordering positions of the sub-region with the ramp in the plurality of sub-regions are determined, and the traffic densities corresponding to the ordering positions are interpolated according to the first traffic densities of the reference regions located at the upstream and downstream of the target region, so as to obtain the second traffic density of the sub-region with the ramp.
Based on the above, after dividing the target area into a plurality of sub-areas, determining the sorting positions of the sub-areas with the ramp in the plurality of sub-areas, and further determining the sub-area with the ramp as the ith sub-area of the target area, wherein i is a positive integer, then, taking the linear change of the traffic density in the road section as an example, determining the position relationship between the reference area and the target area, taking the first traffic density of the reference area positioned at the upstream of the target area as one of known values based on the position relationship, taking the first traffic density of the reference area positioned at the downstream of the target area as the other known value, and performing linear interpolation processing based on the two known values, so that the traffic density interpolation of the ith sub-area in the target area can be determined, namely, the second traffic density of the ith sub-area in the target area is predicted, and the reliability of the second traffic density is higher.
Specifically, when the target region is located upstream of one of the reference regions and downstream of the other reference region, i.e., the target region is located between the two reference regions, interpolation may be used for prediction.
For example, in the case where the prediction is performed using interpolation, in the case where the number of known values is two, instead of the linear interpolation process, a constant interpolation process may be employed, for example, taking an average value of the two known values as the second traffic density of each sub-region; when the number of the known values is two or more, the second traffic density can be predicted by adopting the processing modes such as the quadratic interpolation processing, the exponential interpolation processing, the Lagrange interpolation processing and the like, and more accurate second traffic density can be predicted.
In addition, extrapolation may be used to predict when the target region is upstream or downstream of all the reference regions.
In one possible implementation manner, according to the first traffic density of the reference area located at the upstream and downstream of the target area, the traffic density corresponding to the sorting position is interpolated to obtain the second traffic density of the sub-area with the ramp, specifically, when the first traffic density of the reference area located at the upstream of the target area is smaller than or equal to the first traffic density of the reference area located at the downstream of the target area, the traffic density difference between the two reference areas is determined, a first interpolation item is determined according to the traffic density difference and the sorting position, and according to the sum of the first traffic density of the first interpolation item and the first traffic density of the reference area located at the upstream of the target area, the second traffic density of the sub-area with the ramp is obtained;
Or when the first traffic density of the reference area positioned at the upstream of the target area is larger than the first traffic density of the reference area positioned at the downstream of the target area, determining a traffic density difference value between the two reference areas, determining a first interpolation item according to the traffic density difference value and the sequencing position, and obtaining the second traffic density of the subarea with the ramp according to the sum of the first interpolation item and the first traffic density of the reference area positioned at the downstream of the target area.
When the target area is located between two reference areas, a linear interpolation processing method serving as an interpolation method can be adopted for prediction, one of the reference areas is defined to be located at the upstream of the target area, the other reference area is defined to be located at the downstream of the target area, the two reference areas are adjacent to the target area, the first traffic density of the reference area located at the upstream of the target area is used as the upstream traffic density, the first traffic density of the reference area located at the downstream of the target area is used as the downstream traffic density, the size relation between the upstream traffic density and the downstream traffic density is determined first, the first interpolation item can be determined based on a linear interpolation calculation method under the two conditions of the size relation, then the second traffic density is determined through the first interpolation item, and the second traffic density of the subarea with the ramp can be calculated rapidly and accurately.
Based on this, the calculation formula of the second traffic density is as follows:
when K is up ≤K down When (1):
K i ＝K up +i*(K down -K up )I(n+1)
when K is up >K down When (1):
K i ＝K down +i*(K up -K down )I(n+1)
wherein K is i To be present inThe second traffic density of the subareas of the ramp, n is the total number of subareas divided by the target area, i is the ordering positions of the subareas with the ramp in a plurality of subareas, i is less than or equal to n, i and n are positive integers, K up K is the upstream traffic density down I (K d 。 wn -K up ) /(n+1) and i (K) up -K down ) And/(n+1) are the first interpolation terms.
It can be seen that, traffic densities of other sub-areas without ramps in the target area can be predicted by the linear interpolation processing method, specifically, the sorting positions of the sub-areas in the plurality of sub-areas need to be determined first, then, traffic densities corresponding to the sorting positions are predicted by the linear interpolation processing, and virtual vehicles in the sub-areas can be initialized by the traffic densities.
Step 203: and determining the target traffic density of the ramp according to the second traffic density and the preset first proportional coefficient.
The second traffic density of the sub-area with the ramp is predicted through the first traffic density of the reference area, the predicted second traffic density can be regarded as the traffic density of the main line lane in the sub-area, and the sub-area with the ramp can simultaneously contain the ramp and the main line lane, so that the traffic density born by the ramp needs to be determined through a first scale coefficient, the first scale coefficient is used for representing the bearing degree of the ramp on the traffic density of the main line lane, the second traffic density is multiplied with the first scale coefficient, the target traffic density of the ramp can be obtained, after the traffic density born by the ramp is determined, the second traffic density needs to be updated, and the traffic density before the update of the main line lane is subtracted by the target traffic density, so that the traffic density after the update of the main line lane is obtained.
Specifically, referring to fig. 10, fig. 10 is a schematic diagram of an alternative topology structure of an area where an up ramp is located according to an embodiment of the present application.
Wherein the first midstream section upstream at the nose end is designated as a pre-merger midstream section, assuming that the perceived midstream region including the up-ramp is divided into n perceived midstream sections,and the ith section (numbered 1 to n from upstream to downstream) in which the pre-merger midstream section is located, the second traffic density corresponding to the pre-merger midstream section is K i After calculating the second traffic density, calculating a target traffic density according to a density calculation formula of the ramp, wherein the calculation formula of the target traffic density and an update formula of the second traffic density are respectively as follows:
K on ＝K i *a on
K i '＝K i *(1-a on )
wherein a is on For the first scale factor, K on K is the target traffic density i For traffic density before main lane update, i.e. K i K, the second traffic density of the subarea with the ramp i ' is traffic density after updating the main line lane, i is the ordering position of the subarea with the ramp in a plurality of subareas, and i is a positive integer.
Specifically, referring to fig. 11, fig. 11 is a schematic diagram of an alternative topology structure of an area where an on-ramp is located according to an embodiment of the present application.
Wherein the first midstream section downstream of the split point is named as a split midstream section, the perceived midstream region including the up-ramp is assumed to be divided into n perceived midstream sections, the i-th section (numbered 1 to n from upstream to downstream) in which the split midstream section is located, and the second traffic density corresponding to the split midstream section is K i After calculating the second traffic density, calculating a target traffic density according to a density calculation formula of the down ramp, wherein the calculation formula of the target traffic density and an update formula of the second traffic density are respectively as follows:
K off ＝K i *a off
K i '＝K i *(1-a off )
wherein a is off For the first scale factor, K off K is the target traffic density i For traffic density before main lane update, i.e. K i K, the second traffic density of the subarea with the ramp i Traffic density after updating' main line laneThe degree, i, is the ordering position of the sub-region with the ramp in the plurality of sub-regions, and i is a positive integer.
The value of the first scale factor is not particularly limited, for example, the first scale factor may be set to be less than 0.1, which is equivalent to that the target traffic density of the ramp does not exceed 10% of the traffic density of the main line lane in the sub-area before updating.
Step 204: and determining the average distance between vehicles on the ramp according to the target traffic density, and initializing virtual vehicles in the ramp according to the average distance between vehicles.
The average vehicle distance is an average value of vehicle distances between each pair of adjacent virtual vehicles in the ramp, and for any road section, the relationship that the traffic density and the vehicle distance are reciprocal can be determined, and the larger the traffic density is, the smaller the vehicle distance is, otherwise, the smaller the traffic density is, and the larger the vehicle distance is; the ramp is regarded as being positioned on the same road section, when the target traffic density of the ramp is determined, the traffic density corresponding to the road section is given, the reciprocal of the traffic density of the road section is calculated, the vehicle distance of the road section can be obtained, the average vehicle distance of the ramp is obtained, the relative distance of each virtual vehicle in the ramp can be determined through the average vehicle distance, and then the virtual vehicle can be initialized in the ramp based on the relative distance of the virtual vehicles, for example, the initial states such as the initial position, the initial speed and the like of the virtual vehicle in the ramp are determined, so that the virtual vehicle is initialized in the ramp, which is equivalent to the generation of the microscopic vehicle on a microscopic map.
Based on the method, the initial state of the virtual vehicle in the ramp is determined through the average vehicle distance, so that the virtual vehicle can accord with the traffic running rule, the virtual vehicle can be effectively initialized in the ramp, the continuity of the traffic state of the road displayed in the visual process of the target area in time and space is good, the interactive fusion of the physical space and the digital space is realized, decision basis is better provided for a manager, the visual effect is greatly improved, and the visual cavity is avoided.
In one possible implementation manner, the virtual vehicles are initialized in the ramp according to the average vehicle pitch, specifically, a direction from one end of the ramp to the other end of the ramp is taken as an initialization direction, initial positions of a plurality of virtual vehicles are sequentially determined in the ramp along the initialization direction according to the average vehicle pitch, the virtual vehicles are initialized in each initial position, based on the initial positions, the vehicle pitch between each pair of adjacent virtual vehicles in the ramp can be predicted through the average vehicle pitch, then the initial positions of each virtual vehicle in the ramp are determined according to the predicted vehicle pitch, and then the corresponding virtual vehicles are initialized in each initial position.
When the virtual vehicle is filled in the initial position, the front edge of the virtual vehicle may be aligned with the initial position, the center of mass of the virtual vehicle may be aligned with the initial position, or other alignment methods may be adopted, which is not limited in the embodiment of the present application.
In one possible implementation, the vehicle distance between each pair of adjacent virtual vehicles may be predicted as the vehicle average distance, which is equivalent to that each virtual vehicle is uniformly distributed in the ramp, or the vehicle distance of other values may be predicted based on the vehicle average distance, which is not limited by the embodiment of the present application.
The upper ramp refers to a lane connected with an accelerating lane of the expressway, vehicles can enter the accelerating lane through the upper ramp and then merge into a main lane, the lower ramp refers to a lane connected with an auxiliary lane of the expressway, vehicles traveling on the main lane can enter the auxiliary lane first and then enter the lower ramp through the auxiliary lane, the initialization direction of the upper ramp can be the upstream direction of the upper ramp, namely the direction from one end far away from the accelerating lane to one end close to the accelerating lane in the upper ramp, and the initialization direction of the lower ramp can be the downstream direction of the lower ramp, namely the direction from one end close to the auxiliary lane to one end far away from the auxiliary lane in the lower ramp.
In one possible implementation manner, along the initialization direction, initial positions of a plurality of virtual vehicles are sequentially determined in the ramp according to the average vehicle pitch, specifically, a normal distribution is constructed by taking the average vehicle pitch as a mean value, the plurality of initial vehicle pitches are sequentially and randomly acquired from the normal distribution, and along the initialization direction, the initial positions of the plurality of virtual vehicles are sequentially determined in the ramp according to the initial vehicle pitches.
The vehicle interval distribution in the ramp can be determined based on the average vehicle interval, wherein the vehicle interval distribution refers to the distribution which accords with the vehicle interval under the condition that the vehicle interval between each pair of adjacent virtual vehicles in the ramp is a random variable; the vehicle spacing distribution may be expressed in a function or other form, and embodiments of the present application are not limited thereto.
Based on this, the vehicle pitch distribution may be a normal distribution N (μ, σ) with the average value of the vehicle pitch 2 ) μ=d, D is an average vehicle pitch, and a plurality of vehicle initial pitches obtained randomly from a normal distribution are subjected to the normal distribution, and the randomly obtained vehicle initial pitches have certain diversity and randomness, so that when the initial positions of virtual vehicles are determined based on the vehicle initial pitches, the initial positions of the virtual vehicles in the ramp can have certain diversity and randomness, and further virtual vehicles which are various in state and accord with traffic operation rules are generated in the ramp, thereby improving the visual effect.
In order to avoid the overlarge difference between the initial vehicle spacing, the variance of normal distribution can be reduced, and then the initial vehicle spacing with smaller difference can be obtained when the initial vehicle spacing is randomly obtained each time.
Specifically, the value of the random number in the normal distribution of the average vehicle pitch can be taken, the taken value is determined as the initial vehicle pitch, and the random numbers generated by the simulators through the computer program are considered to be pseudo random numbers, so that a random number generation mode and a random number seed can be given for keeping consistency, so that the random number sequences generated by different simulators are consistent. The way of random number generation and the choice of random number seed are not limited here, but it should be ensured that given these several parameters, whatever the user usesThe simulator can restore the parameters into a completely consistent random number sequence { y }, by using the parameters 1 ,y 2 ,y 3 ,…,y m -such that each simulator can obtain a completely consistent sequence of vehicle initial pitches { D } based on a sequence of random numbers 1 ,D 2 ,D 3 ,…,D m The method and the device for generating the random numbers are not limited, so long as the same result can be ensured to be reproduced in different simulators. Due to { y } 1 ,y 2 ,y 3 ,…,y m The generation of the virtual vehicles has the dual characteristics of randomness and consistency, so that the initial positions of the virtual vehicles in the ramp can have certain diversity and randomness.
In one possible implementation, the virtual vehicle is initialized in the ramp according to the average vehicle pitch, and the vehicle speed may be determined as the initial speed of the virtual vehicle at the initial position by determining the vehicle speed at each initial position according to the initial vehicle pitch.
According to the traffic operation rule, on the premise that overspeed is not generated, the smaller the vehicle distance between the current vehicle and the front vehicle is, the smaller the vehicle speed of the current vehicle is, the larger the vehicle distance between the current vehicle and the front vehicle is, the vehicle speed of the current vehicle can be larger, therefore, for any virtual vehicle, the vehicle distance between the current virtual vehicle and the front virtual vehicle is determined, and then the vehicle speed of the current virtual vehicle is determined according to the vehicle distance, specifically, according to the traffic operation rule, the vehicle speed of the current initial position can be determined based on the vehicle initial distance between the current initial position and the front initial position, and because the randomly acquired vehicle initial distance has certain diversity and randomness, the vehicle speed obtained based on the vehicle initial distance also has certain diversity and randomness, and further, virtual vehicles with various states and according with the traffic operation rule are generated in the ramp, and the visual effect can be improved.
Based on this, after setting the initial speed of the virtual vehicle, the initial heading angle of the virtual vehicle is determined as the lane direction of the position where the virtual vehicle is located.
Specifically, referring to fig. 12, fig. 12 is an alternative schematic diagram of historical traffic data provided by an embodiment of the present application.
On an actual highway, the coverage rate of the sensing device may not be high, i.e., real-time vehicle track data cannot cover all highway sections that need simulation. In the road section without the coverage of the sensing equipment, firstly, whether the historical traffic state described by the historical data exists or not is judged, and the average traffic flow, the density, the speed and other set of data collected by the common bayonet equipment are collected. Fig. 11 shows a line graph of the 24 hour average flow for a road segment, with time granularity ranging from 1 minute, 5 minutes, 10 minutes, 15 minutes, 30 minutes to hours. Depending on the classification of the data sources, there may also be similar data curves of traffic density and average speed, after which the current simulation may be set up by means of historical data.
In one possible implementation manner, the ramp is an up ramp, the vehicle speed of each initial position is determined according to the initial vehicle distance, specifically, the vehicle speed of each initial position is traversed, the reciprocal of the initial vehicle distance between the current initial position and the previous initial position is calculated, the third traffic density of the current initial position is obtained, a traffic basic map for representing the mapping relationship between the vehicle speed and the traffic density is obtained, and the vehicle speed of the current initial position is searched from the traffic basic map according to the third traffic density.
In another possible implementation manner, the ramp is an under ramp, and the vehicle speed of each initial position is determined according to the initial vehicle distance, specifically, the vehicle speed of each initial position is traversed, if the current initial position is not the last traversed to the initial position, the reciprocal of the initial vehicle distance between the current initial position and the previous initial position is calculated, the fourth traffic density of the current initial position is obtained, a traffic basic diagram for representing the mapping relationship between the vehicle speed and the traffic density is obtained, and the vehicle speed of the previous initial position is searched from the traffic basic diagram according to the fourth traffic density;
and if the current initial position is the last traversed to the initial position, acquiring the highest speed limit of the ramp, and taking the highest speed limit as the vehicle speed of the current initial position.
The traffic base graph may also be referred to as a macroscopic base graph, and the traffic base graph may be used to describe a relationship among macroscopic traffic flow (simply referred to as traffic flow), traffic density, and speed in a traffic network. The traffic base map may be arranged in a coordinate system with traffic density on the horizontal axis and traffic capacity on the vertical axis.
Specifically, referring to fig. 13, fig. 13 is an alternative schematic view of a traffic basic map according to an embodiment of the present application.
The traffic basic map comprises two straight line segments, and each point on the straight line segments can represent a traffic state, for example, the traffic state can comprise states of traffic density, traffic capacity, traffic speed and the like of a road section.
Wherein the straight line segment located in the smaller traffic density interval describes the free form state of the vehicle, and the slope of the straight line segment is the free flow vehicle speed V max The traffic speed is a desired speed determined by a driver according to the current condition of the road, the free-flow speed is a traffic speed when the traffic density is 0, and the free-flow speed is a speed of the vehicle which is not affected by the upstream and downstream conditions. At a traffic density increasing from 0 to a critical density K cr In the process, the traffic speed can be kept unchanged, the traffic capacity is gradually increased, and the maximum traffic capacity Q is reached when the traffic density reaches the critical density max . When the traffic density continues to increase due to the continuous increase of vehicles, the speed of the vehicles gradually slows down and the vehicles enter a congestion state, and the traffic capacity also decreases accordingly, namely, the state described by straight line segments in a larger traffic density interval, when the traffic density increases to the congestion density K jam When the traffic flow enters a completely congested stop state, the speed and traffic capacity of each vehicle in the traffic flow are reduced to 0. It can be seen that, for any vehicle, if the traffic density and the speed corresponding to the vehicle conform to the relationship described in the traffic base map, it is indicated that the vehicle conforms to the traffic law, for example when the traffic density is high, Traffic laws in which vehicles may be in a congested state (the traffic speed of the vehicles is small). In the traffic basic map, V max Critical density K of 80 km/h cr 25 vehicles/km, Q max At a blocking density K of 2000 g/h jam 140 vehicles/km.
Specifically, referring to fig. 14, fig. 14 is another alternative schematic view of a traffic basic map according to an embodiment of the present application.
Wherein, the blocking density K in the traffic basic diagram jam Depending on the distance from head to head when traffic is completely congested, 140 vehicles/km may be taken as a default value, or other values may be taken as default values, without limitation. Maximum traffic capacity Q max And free-flow vehicle speed V max Parameters related to the road type can be obtained through parameter correction or query related specifications.
Wherein, in the traffic basic map, the traffic basic map is composed of the blocking density K jam Maximum capacity Q MAx Free-flow vehicle speed V mAC The three parameters can uniquely determine two straight line segments in the traffic basic diagram; or by the blocking density k jam Maximum capacity Q max Critical density k cr The three parameters can also uniquely determine two straight line segments in the traffic basic diagram; or, from the blocking density K jam Free flow vehicle speed V max Critical density K cr These three parameters can also uniquely determine two straight line segments in the traffic base map, and so on. The method for obtaining the traffic basic map is not limited, and for example, the free flow speed V can be set in the scene file by a user max Blocking density K jam Critical density K cr Maximum capacity Q max The values of the relevant parameters are equal to enable the simulator to generate corresponding traffic basic graphs, or the simulator directly adopts default values of the parameters to generate corresponding traffic basic graphs; for another example, different default basic graphs can be set in the simulator so as to describe the basic traffic attributes of road areas with different road types, thereby obtaining corresponding traffic basic graphs according to the road types related to the current simulation.
Specifically, referring to fig. 15, fig. 15 is a schematic diagram of an alternative vehicle-to-vehicle distance relationship of an up ramp according to an embodiment of the present application.
When the ramp is an up ramp, the initialization direction of the up ramp may be the upstream direction of the up ramp, and a vehicle initial distance D is found from the nose end of the confluence region to the upstream of the ramp j As an initial position of the virtual vehicle to be filled in the on-ramp, the initial position may be generated at the lane center line, and in turn, the vehicle initial distance D is generated j (j=i, i+1, …), i being a positive integer, the initial vehicle pitch D of each vehicle j The method and the device can obey normal distribution with the average value of the average distance D of the vehicles, and backtrack and fill virtual vehicles upstream based on the initial distance of each vehicle, the filling sequence of the virtual vehicles is not limited, if the ramp comprises a plurality of runnable lanes, the virtual vehicles can be filled according to the lanes, namely one lane is filled first and then the other lane is filled, or the virtual vehicles can be near and far, namely the region close to the nose end is filled first and backtrack is carried out upstream; in the upstream backtracking process, if a vehicle initial distance D is selected j When the range of the ramp (such as crossing the start point of the upper ramp) is exceeded after the upstream backtracking, the initialization of the ramp is considered to be completed.
The method for traffic simulation processing in the embodiment of the application can limit the maximum filling quantity of the virtual vehicles in the ramp, and stop when the quantity of the filled virtual vehicles reaches the maximum filling quantity when the virtual vehicles are filled in the ramp.
Starting from the virtual boundary of the nose end of the upper ramp, which corresponds to the boundary of the upper ramp and the accelerating lane, the vehicle is filled back upstream, and the vehicle initial distance between the current initial position and the previous initial position can be used as the traffic distance of the current initial position because the current initial position is positioned upstream of the previous initial position, so that the traffic distance of the virtual vehicle Car1 is D 1 The passing distance of the virtual vehicle Car2 is D 2 The passing distance of the virtual vehicle Car3 is D 3 . Each virtualThe maximum speed of the vehicle is determined by the speed limit of the road section where the vehicle is located.
Specifically, referring to fig. 16, fig. 16 is a schematic diagram of an alternative vehicle-to-vehicle distance relationship of an on-ramp provided in an embodiment of the present application.
When the ramp is an off-ramp, the initialization direction of the off-ramp may be the downstream direction of the off-ramp, and a vehicle initial distance D is found from the split point of the split area to the downstream of the ramp j As an initial position of the virtual vehicle to be filled in the down-ramp, the initial position may be generated at the lane center line, and in turn, the vehicle initial distance D is generated j (j=i, i+1, …), i being a positive integer, the initial vehicle pitch D of each vehicle j The method and the device can obey normal distribution with the average value of the average distance D of the vehicles, and backtrack and fill the virtual vehicles downstream based on the initial distance of each vehicle, the filling sequence of the virtual vehicles is not limited, if the ramp comprises a plurality of runnable lanes, the virtual vehicles can be filled according to the lanes, namely one lane is filled first and then the other lane is filled, or the virtual vehicles can be near and far, namely the region close to the nose is filled first and backtrack is carried out downstream; during the downstream backtracking, if a vehicle initial distance D is selected j When the range of the ramp (such as crossing the end point of the down ramp) is exceeded after the downstream backtracking, the initialization of the ramp is considered to be completed.
The method for traffic simulation processing in the embodiment of the application can limit the maximum filling quantity of the virtual vehicles in the down-ramp, and stop when the quantity of the filled virtual vehicles reaches the maximum filling quantity when the virtual vehicles are filled in the down-ramp.
Starting from the virtual boundary where the diversion point of the down-ramp is located, which is equivalent to starting at the junction of the down-ramp and the auxiliary lane, the vehicle is filled backward downstream, and since the current initial position is located downstream of the previous initial position, the vehicle initial distance between the current initial position and the previous initial position can be used as the passing distance of the previous initial position, and therefore, the passing distance of the virtual vehicle Car4 is D 6 The passing distance of the virtual vehicle Car5 is D 7 The passing distance of the virtual vehicle Car6 is D 8 。
Based on the above, the traffic space of any initial position can be used for determining the local traffic density of the initial position, then under the condition of the given traffic density, the traffic state of the vehicle can be uniquely determined according to the traffic basic diagram, so as to determine the speed of the vehicle, therefore, the local traffic density of the local road section of the initial position can be obtained by calculating the reciprocal of the traffic space, and then the traffic state of the local road section is determined according to the traffic basic diagram and the local traffic density, so as to obtain the initial speed required by the virtual vehicle positioned on the local road section. However, the general simulation area only includes the high-speed main line and the associated up-down ramp, and for the initial position of the most downstream down ramp, since the next position point separated from the initial position by one vehicle initial distance has passed the end of the down ramp, i.e. beyond the boundary of the simulation area, at this time, the next position point of the down ramp is not simulated, so that the next vehicle initial distance of the initial position cannot be accurately determined, which corresponds to the passing distance D 8 Not necessarily accurate, the highest speed limit of the down-ramp is taken as the initial speed required by the virtual vehicle at the initial position.
Specifically, assume that the passing distance of one initial position is D j Local traffic density K j ＝1/D j The initial speed required by the virtual vehicle of the local road section is V j When K is j ≤K cr V at the time of j ＝V max The method comprises the steps of carrying out a first treatment on the surface of the When K is j >K cr V at the time of j ＝[K j *Q max /(K cr -K jam )+K jam *Q max /(K jam -K cr )]K; thus, K is j The third traffic density corresponding to the initial position can be calculated by the traffic basic map to obtain K j V corresponding to j The method is equivalent to searching the vehicle speed at the current initial position from the traffic basic map, wherein the vehicle speed accords with the traffic operation rule, the unit of the initial distance of the vehicles is meter, and the unit of the initial speed is meter/second.
The traffic basic map may be a state curve of other shapes besides two straight line segments, and is not limited herein, and the traffic basic map only needs to satisfy that a corresponding curve can be reproduced through related parameters, and the traffic state (vehicle distance, speed, etc.) of the traffic flow may be described through traffic density or other parameters.
It can be seen that if the target area includes two sub-areas at the same time, one sub-area has an up-ramp, and the other sub-area has a down-ramp, the sub-area having the up-ramp can be processed according to the above-mentioned traffic simulation processing method for filling the virtual vehicle with the up-ramp, and the sub-area having the down-ramp can be processed according to the above-mentioned traffic simulation processing method for filling the virtual vehicle with the down-ramp.
In another possible implementation manner, the virtual vehicle is initialized in the ramp according to the average distance between vehicles, specifically, the ordering positions of the subareas with the ramp in the subareas are determined, the first vehicle type duty ratio of each candidate vehicle type in the reference area located at the upstream and downstream of the target area is obtained, the vehicle type duty ratio corresponding to the ordering positions is interpolated according to the first vehicle type duty ratio, the second vehicle type duty ratio of each candidate vehicle type in the subarea with the ramp is obtained, and the virtual vehicle is initialized in the ramp according to the second vehicle type duty ratio and the average distance between vehicles.
In the reference area, the sensing device can continuously collect vehicle information data in the reference area in real time, the vehicle type data of the vehicles collected by the sensing device belong to the vehicle information data, and the simulation system can aggregate the collected real-time collected information according to different space and time dimensions so as to obtain traffic flow parameters of time intervals in the sensing area, wherein the first vehicle type proportion of various candidate vehicle types in the reference area at the upstream and downstream of the target area respectively belong to the traffic flow parameters.
The sensing device may sense one or more vehicle type data, for example, the sensing device may sense vehicle type data of a large vehicle and a small vehicle, the large vehicle and the small vehicle in the sensing area may be distinguished through the vehicle type data, a ratio of the number of the large vehicles to the number of all vehicles in the sensing area is used as a first vehicle type ratio of the large vehicle types, a ratio of the number of the small vehicles to the number of all vehicles in the sensing area is used as a first vehicle type ratio of the small vehicle types, and the large vehicle may refer to a vehicle with a total mass of 4.5t, a number of passengers of 20 or a vehicle length of 6m or more, for example, the large vehicle may be a general bus, a medium-sized uploading vehicle, or the like; the small car herein may refer to a car having a total mass of 4.5t, 9 passengers, or a car length of 6m or less, and may be, for example, a sedan, a jeep, a minibus, a light bus, a light truck, or the like.
Based on the above, after dividing the target area into a plurality of sub-areas, determining the ordering position of the sub-area with the ramp in the plurality of sub-areas, and further determining the sub-area with the ramp as the i-th sub-area of the target area, wherein i is a positive integer, then, since there is a correlation between the vehicle type duty ratio of the target area and the vehicle type duty ratio of the reference area, taking the linear change of the vehicle type duty ratio in the road section as an example, determining the position relationship between the reference area and the target area, taking the first vehicle type duty ratio of the reference area located at the upstream of the target area as one of the known values, taking the first vehicle type duty ratio of the reference area located at the downstream of the target area as the other known value, and performing linear interpolation processing based on the two known values, so as to determine the vehicle type duty ratio interpolation of the i-th sub-area in the target area, namely, measuring the second vehicle type duty ratio of the i-th sub-area in the target area, and obtaining the predicted second traffic density with higher reliability. In the prediction process of the second vehicle type duty ratio, the second vehicle type duty ratio of each vehicle type is predicted based on the corresponding first vehicle type duty ratio, for example, the second vehicle type duty ratio of the large vehicle type is predicted based on the first vehicle type duty ratio of the large vehicle type, and the second vehicle type duty ratio of the small vehicle type is predicted based on the first vehicle type duty ratio of the small vehicle type.
Specifically, when the target region is located upstream or downstream of all the reference regions, extrapolation may be used for prediction; when the target region is located upstream of one of the reference regions and downstream of the other reference region, interpolation may be used for prediction.
For example, in the case where the prediction is performed using interpolation, in the case where the number of known values is two, instead of the linear interpolation process, a constant interpolation process may be employed, for example, taking an average value of the two known values as the second pattern duty ratio of each sub-region; under the condition that the number of the known values is more than two, the second vehicle type duty ratio can be predicted by adopting processing modes such as secondary interpolation processing, exponential interpolation processing, lagrange interpolation processing and the like.
In another possible implementation manner, the vehicle type duty ratio corresponding to the sorting position is interpolated according to the first vehicle type duty ratio to obtain the second vehicle type duty ratio of various candidate vehicle types in the sub-region with the ramp, specifically, when the first vehicle type duty ratio of the reference region positioned at the upstream of the target region is smaller than or equal to the first vehicle type duty ratio of the reference region positioned at the downstream of the target region, the vehicle type duty ratio difference between the two reference regions is determined, a second interpolation item is determined according to the vehicle type duty ratio difference and the sorting position, and the second vehicle type duty ratio of the sub-region with the ramp is obtained according to the sum of the second interpolation item and the first vehicle type duty ratio of the reference region positioned at the upstream of the target region;
Or when the first vehicle type duty ratio of the reference area positioned at the upstream of the target area is larger than the first vehicle type duty ratio of the reference area positioned at the downstream of the target area, determining a vehicle type duty ratio difference between the two reference areas, determining a second interpolation item according to the vehicle type duty ratio difference and the sequencing position, and obtaining a second vehicle type duty ratio of the sub-area with the ramp according to the sum of the second interpolation item and the first vehicle type duty ratio of the reference area positioned at the downstream of the target area.
The first vehicle type duty ratio of the reference area positioned at the upstream of the target area is used as an upstream vehicle type duty ratio, the first vehicle type duty ratio of the reference area positioned at the downstream of the target area is used as a downstream vehicle type duty ratio, the magnitude relation between the upstream vehicle type duty ratio and the downstream vehicle type duty ratio is determined first, a second interpolation item can be determined based on a linear interpolation calculation method under the two conditions of the magnitude relation, then the second vehicle type duty ratio is determined through the second interpolation item, the second vehicle type duty ratio of a subarea where the ramp exists can be calculated quickly and accurately, and the vehicle type duty ratio of the ramp can be kept consistent with the second vehicle type duty ratio of the subarea in the subarea because the vehicle type duty ratio is the ratio of the number of vehicles of a certain vehicle type to the total number of vehicles, and the vehicle type of virtual vehicles on the ramp can be determined based on the second vehicle type duty ratio.
Based on the above, taking a second vehicle type ratio of the predicted large vehicle type as an example, a calculation formula of the second vehicle type ratio is as follows:
when P up ≤P down When (1):
P i ＝P up +i*(P down -P up )/(n+1)
when P up >P down When (1):
P i ＝P down +i*(P up -P down )I(n+1)
wherein P is i The second vehicle type duty ratio of the large vehicle type is that n is the total number of subareas divided by the target area, i is the sequencing positions of subareas with ramps in a plurality of subareas, i is not more than n, i and n are positive integers, and P up Is the duty ratio of an upstream vehicle, P down For the downstream vehicle type duty cycle, i (P down -P up ) /(n+1) and i (P) up -P down ) And/(n+1) are the second interpolation terms.
It can be seen that the vehicle type ratio of other sub-areas without ramps in the target area can be predicted by the linear interpolation processing method, specifically, the sequencing positions of the sub-areas in the plurality of sub-areas need to be determined first, then the vehicle type ratio corresponding to the sequencing positions is predicted by the linear interpolation processing, and the initialization of the virtual vehicle in the sub-areas can be completed by combining the vehicle type ratio.
In another possible implementation manner, the virtual vehicle is initialized in the ramp according to the second vehicle type duty ratio and the vehicle average distance, specifically, the target value interval is divided into a plurality of sub-value intervals according to each second vehicle type duty ratio, the target value is randomly generated in the target value interval, the target vehicle type is determined from a plurality of candidate vehicle types according to the attribution relationship between the target value and the plurality of sub-value intervals, and the virtual vehicle is initialized in the ramp according to the target vehicle type and the vehicle average distance.
Based on the above, the predicted vehicle type distribution is determined through the second vehicle type ratio of each vehicle type, then the second vehicle type ratio corresponding to each vehicle type is multiplied by the total length of the target value interval to obtain the target length of the sub-value interval of each vehicle type, then the target value interval is divided based on each target length, the divided sub-value intervals are not overlapped, when the virtual vehicle is initialized at one initial position, a target value serving as a random number needs to be generated in the target value interval, the attribution relation between the target value and the sub-value interval refers to that the target value is in the sub-value interval, or the target value is not in the sub-value interval, the sub-value interval in which the target value is located can be determined through the attribution relation, then the candidate vehicle type corresponding to the sub-value interval in which the target value is located is determined as the target vehicle type, then the virtual vehicle in the initial position is set as the target, when the virtual vehicle is initialized at the next initial position, the target value is randomly generated again, and the type of the target is diversified, so that the vehicle type in the ramp has certain random effect can be promoted.
For example, the sensing device may sense two types of data, i.e., a large car and a small car, and the predicted vehicle type distribution may indicate that the ratio of the large car to the small car is: 20%:80%, the second vehicle type ratio corresponding to the large vehicle is 20%, the second vehicle type ratio of the small vehicle is 80%, the target value interval is assumed to be (0, 1), the target value interval is divided into 2 subvalue intervals according to the respective second vehicle type ratio, the subvalue interval corresponding to the large vehicle may be (0,0.2), the subvalue interval corresponding to the small vehicle may be [0.2,1), then when the virtual vehicle is initialized at one initial position, the target value needs to be randomly generated in (0, 1), the target value is assumed to be 0.5, and since 0.5 is within [0.2,1 ], the target value is within the subvalue interval corresponding to the small vehicle, the target vehicle is the small vehicle corresponding to the small vehicle, and therefore the vehicle type of the virtual vehicle at the initial position is set as the small vehicle type.
Wherein when the sensing device is only capable of sensing two types of vehicle types, the second vehicle type duty ratio of one type of vehicle type can be used as a judgment threshold, for example, for a large vehicle type and a small vehicle type, the second vehicle type duty ratio of the large vehicle type can be used as a judgment threshold, when the target value is smaller than the judgment threshold, the large vehicle type is selected, otherwise, the small vehicle type is selected, specifically, the second vehicle type duty ratio P of the large vehicle type is selected i A target value of p E (0, 1) when p is 0.2<P i When the vehicle type of the large vehicle is selected, otherwise, selecting the trolley type.
In another possible implementation manner, the ramp includes multiple target lanes, the average distance between vehicles of the ramp is determined according to the target traffic density, specifically, the fifth traffic density of each target lane is determined according to the target traffic density and a preset second scaling factor, and the reciprocal of each fifth traffic density is calculated to obtain the average distance between vehicles of each target lane.
The target lane refers to a lane for vehicles to pass in the ramp, when the ramp has a plurality of parallel lanes for vehicles to pass, the ramp is equivalent to the ramp including a plurality of target lanes, and in the initial state setting stage, the virtual vehicle can be set in a state that the ramp is not being changed, so that the initialization processing of different target lanes can be regarded as non-interference, and therefore, the virtual vehicle can be initialized on the plurality of target lanes through serial processing or parallel processing; the serial processing means initializing one of the uninitialized target lanes and then initializing the other uninitialized target lane; parallel processing means that multiple uninitialized target lanes may be initialized at the same time.
Based on the above, after the target traffic density of the ramp is determined, the target traffic density can be regarded as the sum of the traffic densities of all the parallel target lanes in the ramp, so that the traffic density borne by each target lane can be determined through a second proportionality coefficient, the second proportionality coefficient is used for representing the bearing degree of the target lane to the traffic density of the ramp, the second proportionality coefficient of the target lane is multiplied by the target traffic density, the fifth traffic density of the target lane can be obtained, after the fifth traffic density borne by the target lane is determined, the vehicle average distance of the target lane can be obtained through calculating the reciprocal of the fifth traffic density, and then a virtual vehicle is initialized on the target lane according to the vehicle average distance of the target lane. The value of the second scaling factor is not particularly limited, and is generally equal to the fifth traffic density, so that the fifth traffic density of each target lane is the same.
Referring to fig. 17, fig. 17 is a schematic flow chart of an alternative initial state setting of an up ramp according to an embodiment of the present application.
The above ramp initial state is set as an example, it is assumed that the target area is located between two reference areas, the two reference areas are adjacent to the target area, and the sensing device can sense that the vehicle is a cart type or a trolley type, the target area corresponds to a sensing midstream area, one of the reference areas corresponds to a sensing coverage area located upstream of the sensing midstream area, and the other reference area corresponds to a sensing coverage area located downstream of the sensing midstream area. The method comprises the following specific steps:
Step 1701, aggregating perceived coverage areas upstream and downstream of perceived midstream region at [ T ] 0 -T w ,T 0 ) The traffic density and the cart proportion of the time period are equivalent to the first traffic density and the first vehicle type duty ratio of the acquired reference area.
In step 1702, the midstream region is divided into a plurality of road segments according to the length of about 200m of each segment, the road segments are separated by virtual boundaries, which is equivalent to dividing a target region in a traffic simulation scene into a plurality of sub-regions, at least one sub-region has an up ramp, in actual cases, the division lengths of the road segments are not necessarily uniform, midstream road segments with different lengths can exist, and the division length can be more than or less than 200 meters.
Step 1703, calculating traffic density sum of each segment by linear interpolationRatio of cart, value a on To calculate the traffic density of the up-ramp and update the traffic density of the road section before confluence, so that the second traffic density and the second vehicle type duty ratio of the sub-region where the ramp exists and the target traffic density of the ramp can be obtained.
Step 1704, generating virtual vehicles on each midstream section of the main line based on traffic density and cart proportion on the main line.
Step 1705, determine if all lanes in the ramp have been filled? If yes, the initial state setting is finished; if not, go to step 1706.
Step 1706, if any lane in the ramp is not completely filled, randomly selecting an unfilled lane as the current lane, and taking the downstream boundary as the current position.
Step 1707, from N (D, sigma 2 ) Is selected to be a D j The value is equivalent to randomly selecting an initial vehicle distance D from a normal distribution with the average vehicle distance D as the obeying mean value j ；
Step 1708, determining that the current position backtracks a D j Is the turn-up start point already crossed? If yes, re-execute step 1705; if not, go to step 1709. Step 1708 is to determine whether the current position is the last determined current position, and determine a backtracking position after backtracking, which is equivalent to finding an initial distance D of the vehicle from the nose end of the merging region to the upstream ramp j As an initial position of the virtual vehicle in the up-ramp that needs to be filled.
Step 1709, determine if P is less than the cart fraction P for randomly generated P i ? If yes, selecting a cart type, and executing step 1710; if not, a dolly type is selected and step 1710 is performed. Step 1709 is used to determine whether the target value is smaller than the cart proportion, where the target value is p, and p is randomly generated in (0, 1).
Step 1710, based on the model selection, generating a virtual vehicle at the backtracking location.
Step 1711, based on the traffic base map and the current D j Value-determining traffic stateAnd further, assigning an initial speed of the virtual vehicle, taking the vehicle position as a current position, and re-executing step 1707.
Based on the method, the initial state of the virtual vehicle in the ramp is determined through the average distance of the vehicles, so that the virtual vehicle can accord with the traffic running rule, the virtual vehicle can be effectively initialized in the ramp, the traffic state of the road displayed in the visualization of the target area is good in continuity in time and space, and the visualization effect is improved.
Referring to fig. 18, fig. 18 is a schematic flow chart of an alternative initial state setting of an off ramp according to an embodiment of the present application.
Step 1801, aggregating perceived coverage areas located upstream and downstream of perceived midstream region at [ T ] 0 -T w ,T 0 ) The traffic density and the cart proportion of the time period are equivalent to the first traffic density and the first vehicle type duty ratio of the acquired reference area.
In step 1802, the midstream region is divided into a plurality of road segments according to the length of about 200m, the road segments are separated by virtual boundaries, which is equivalent to dividing the target region in the traffic simulation scene into a plurality of sub-regions, and the under ramp exists in at least one sub-region, in the actual situation, the dividing lengths of the road segments are not necessarily uniform, midstream road segments with different lengths may exist, and the dividing length may be greater than or less than 200 meters.
Step 1803, calculating traffic density and cart proportion of each section by linear interpolation, and taking value a off To calculate the traffic density of the down-ramp and update the traffic density of the road section before confluence, so that the second traffic density and the second vehicle type duty ratio of the subarea where the ramp exists and the target traffic density of the ramp can be obtained.
At step 1804, virtual vehicles are generated at each midstream section of the main line based on traffic density and cart proportion on the main line.
Step 1805, determine whether all lanes in the down-ramp have been filled? If yes, the initial state setting is finished; if not, go to step 1806.
Step 1806, if any lane in the ramp is not completely filled, randomly selecting an unfilled lane as the current lane, and taking the upstream boundary as the current position.
Step 1807, from N (D, σ 2 ) Is selected to be a D j The value is equivalent to randomly selecting an initial vehicle distance D from a normal distribution with the average vehicle distance D as the obeying mean value j ；
Step 1808, determining that the current position backtracks a D j Is the down-ramp endpoint already crossed? If yes, go to step 1809; if not, go to step 1810. Step 1808 is to determine whether the current position is the last determined current position, and determine a backtracking position after backtracking, which is equivalent to finding an initial distance D of the vehicle from the split point of the split area to the downstream ramp j As an initial position of the virtual vehicle in the down-ramp that needs to be filled.
Step 1809, determining whether a virtual vehicle exists at the current location? If yes, assigning a value for the initial speed of the vehicle by using the speed limit of the current down ramp, and re-executing step 1805; if not, step 1805 is re-executed. When the length of the down-ramp is shorter, the down-ramp end point is crossed when the down-ramp is traced back for the first time, the situation that no virtual vehicle exists in the current position in the judgment of step 1809 is equivalent to that no virtual vehicle is generated in the lane of the down-ramp.
Step 1810, determining whether a virtual vehicle exists at the current location? If yes, based on the traffic basic diagram and the current D j Determining a traffic state by the value, further assigning a value to the initial speed of the virtual vehicle, and executing step 1811; if not, go to step 1811.
Step 1811, moving the current position downstream of ramp by D j The method is equivalent to taking the backtracking position as a new current position;
step 1812, determine if P is less than the cart fraction P for randomly generated P i ? If yes, selecting a cart type, and executing a step 1813; if not, a dolly type is selected and step 1813 is performed. Step 1812 is to determine whether the target value is less than the cart ratio, the target value is p, p is randomly generated in (0, 1).
Step 1813, based on the model selection, generates a virtual vehicle at the current location and re-executes step 1807.
Based on the method, the initial state of the virtual vehicle in the down ramp is determined through the average vehicle distance, so that the virtual vehicle can accord with the traffic running rule, the virtual vehicle can be effectively initialized in the ramp, the traffic state of the road displayed in the target area in the visualization process is good in continuity in time and space, and the visualization effect is improved.
It will be appreciated that, although the steps in the flowcharts described above are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order unless explicitly stated in the present embodiment, and may be performed in other orders. Moreover, at least some of the steps in the flowcharts described above may include a plurality of steps or stages that are not necessarily performed at the same time but may be performed at different times, and the order of execution of the steps or stages is not necessarily sequential, but may be performed in turn or alternately with at least a portion of the steps or stages in other steps or other steps.
Referring to fig. 19, fig. 19 is a schematic view of an alternative structure of a traffic simulation processing apparatus according to an embodiment of the present application, where the traffic simulation processing apparatus 1900 includes:
the division module 1901 is configured to divide a target area, which is not covered by the sensing device, in the traffic simulation scene into a plurality of sub-areas, where a ramp exists in at least one sub-area;
a prediction module 1902, configured to obtain a first traffic density of a reference area in a traffic simulation scene, and predict a second traffic density of a sub-area where a ramp exists according to the first traffic density, where the reference area is covered by a sensing device;
a density determining module 1903, configured to determine a target traffic density of the ramp according to the second traffic density and a preset first proportion coefficient;
an initialization module 1904 is configured to determine a vehicle average distance of the ramp according to the target traffic density, and initialize the virtual vehicle in the ramp according to the vehicle average distance.
Further, the prediction module 1902 is specifically configured to:
determining the ordering positions of the subareas with the ramp in a plurality of subareas;
and interpolating the traffic density corresponding to the sequencing position according to the first traffic density of the reference area positioned at the upstream and downstream of the target area to obtain the second traffic density of the subarea with the ramp.
Further, the prediction module 1902 is specifically configured to:
when the first traffic density of the reference area positioned at the upstream of the target area is smaller than or equal to the first traffic density of the reference area positioned at the downstream of the target area, determining a traffic density difference value between the two reference areas, determining a first interpolation item according to the traffic density difference value and the sequencing position, and obtaining a second traffic density of the subarea with the ramp according to the sum of the first interpolation item and the first traffic density of the reference area positioned at the upstream of the target area;
or when the first traffic density of the reference area positioned at the upstream of the target area is larger than the first traffic density of the reference area positioned at the downstream of the target area, determining a traffic density difference value between the two reference areas, determining a first interpolation item according to the traffic density difference value and the sequencing position, and obtaining the second traffic density of the subarea with the ramp according to the sum of the first interpolation item and the first traffic density of the reference area positioned at the downstream of the target area.
Further, the initialization module 1904 is specifically configured to:
taking the direction from one end of the ramp to the other end of the ramp as an initialization direction, and sequentially determining initial positions of a plurality of virtual vehicles in the ramp along the initialization direction according to the average distance between the vehicles;
The virtual vehicle is initialized in each initial position.
Further, the initialization module 1904 is specifically configured to:
constructing normal distribution by taking the average distance of vehicles as a mean value, and sequentially and randomly acquiring a plurality of initial distances of the vehicles from the normal distribution;
along the initialization direction, initial positions of a plurality of virtual vehicles are sequentially determined in the ramp according to initial pitches of the vehicles.
Further, the initialization module 1904 is further configured to:
determining the vehicle speed of each initial position according to the initial vehicle distance;
the vehicle speed is determined as an initial speed of the virtual vehicle at the initial position.
Further, the ramp is an up ramp, and the initialization module 1904 is specifically configured to:
traversing each initial position, and calculating the reciprocal of the initial distance between the current initial position and the previous initial position to obtain the third traffic density of the current initial position;
and acquiring a traffic basic diagram for representing the mapping relation between the vehicle speed and the traffic density, and searching the vehicle speed at the current initial position from the traffic basic diagram according to the third traffic density.
Further, the ramp is an off ramp, and the initialization module 1904 is specifically configured to:
traversing each initial position, if the current initial position is not the last traversed to the initial position, calculating the reciprocal of the initial distance between the current initial position and the previous initial position to obtain a fourth traffic density of the current initial position, obtaining a traffic basic diagram for representing the mapping relation between the vehicle speed and the traffic density, and searching the vehicle speed of the previous initial position from the traffic basic diagram according to the fourth traffic density;
And if the current initial position is the last traversed to the initial position, acquiring the highest speed limit of the ramp, and taking the highest speed limit as the vehicle speed of the current initial position.
Further, the initialization module 1904 is specifically configured to:
determining the ordering positions of the subareas with the ramp in a plurality of subareas;
acquiring the first vehicle type duty ratio of each candidate vehicle type in the reference area at the upstream and downstream of the target area, and interpolating the vehicle type duty ratio corresponding to the sequencing position according to the first vehicle type duty ratio to obtain the second vehicle type duty ratio of each candidate vehicle type in the sub-area with the ramp;
initializing a virtual vehicle in the ramp according to the second vehicle type duty cycle and the average vehicle spacing.
Further, the initialization module 1904 is specifically configured to:
when the first vehicle type duty ratio of the reference area positioned at the upstream of the target area is smaller than or equal to the first vehicle type duty ratio of the reference area positioned at the downstream of the target area, determining a vehicle type duty ratio difference between the two reference areas, determining a second interpolation item according to the vehicle type duty ratio difference and the sequencing position, and obtaining a second vehicle type duty ratio of the sub-area with the ramp according to the sum of the second interpolation item and the first vehicle type duty ratio of the reference area positioned at the upstream of the target area;
Or when the first vehicle type duty ratio of the reference area positioned at the upstream of the target area is larger than the first vehicle type duty ratio of the reference area positioned at the downstream of the target area, determining a vehicle type duty ratio difference between the two reference areas, determining a second interpolation item according to the vehicle type duty ratio difference and the sequencing position, and obtaining a second vehicle type duty ratio of the sub-area with the ramp according to the sum of the second interpolation item and the first vehicle type duty ratio of the reference area positioned at the downstream of the target area.
Further, the initialization module 1904 is specifically configured to:
dividing the target numerical value interval into a plurality of sub-numerical value intervals according to the duty ratio of each second vehicle type;
randomly generating a target value in a target value interval, and determining a target vehicle type from a plurality of candidate vehicle types according to the attribution relation between the target value and a plurality of sub-value intervals;
and initializing a virtual vehicle in the ramp according to the target vehicle type and the average vehicle distance.
Further, the ramp includes a plurality of target lanes, and the initialization module 1904 is specifically configured to:
determining a fifth traffic density of each target lane according to the target traffic density and a preset second proportionality coefficient;
and calculating the reciprocal of each fifth traffic density to obtain the average vehicle distance of each target lane.
The vehicle processing device 1900 and the traffic simulation processing method are based on the same inventive concept, and virtual vehicles conforming to traffic operation rules can be added in the ramp of the target area, so that the traffic state of the road displayed in the target area in the visualization process has better continuity in time and space, and the visualization effect is improved.
The electronic device for executing the traffic simulation processing method provided by the embodiment of the present application may be a terminal, and referring to fig. 20, fig. 20 is a partial block diagram of the terminal provided by the embodiment of the present application, where the terminal includes: the camera assembly 2010, the memory 2020, the input unit 2030, the display unit 2040, the sensor 2050, the audio circuit 2060, the wireless fidelity (wireless fidelity, abbreviated as WiFi) module 2070, the processor 2080, the power supply 2090 and the like. It will be appreciated by those skilled in the art that the terminal structure shown in fig. 20 is not limiting of the terminal and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
The camera assembly 2010 may be used to capture images or video. Optionally, the camera assembly 2010 includes a front camera and a rear camera. Typically, the front camera is disposed on the front panel of the terminal and the rear camera is disposed on the rear surface of the terminal. In some embodiments, the at least two rear cameras are any one of a main camera, a depth camera, a wide-angle camera and a tele camera, so as to realize that the main camera and the depth camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize a panoramic shooting and Virtual Reality (VR) shooting function or other fusion shooting functions.
The memory 2020 may be used for storing software programs and modules, and the processor 2080 executes various functional applications of the terminal and data processing by executing the software programs and modules stored in the memory 2020.
The input unit 2030 may be used for receiving input numerical or character information and generating key signal inputs related to setting and function control of the terminal. Specifically, the input unit 2030 may include a touch panel 2031 and other input devices 2032.
The display unit 2040 may be used to display input information or provided information and various menus of the terminal. The display unit 2040 may include a display panel 2041.
Audio circuitry 2060, speaker 2061, microphone 2062 may provide an audio interface.
The power supply 2090 may be an ac, dc, disposable battery or rechargeable battery.
The number of sensors 2050 may be one or more, the one or more sensors 2050 including, but not limited to: acceleration sensors, gyroscopic sensors, pressure sensors, optical sensors, etc. Wherein:
the acceleration sensor may detect the magnitudes of accelerations on three coordinate axes of a coordinate system established with the terminal. For example, an acceleration sensor may be used to detect the components of gravitational acceleration in three coordinate axes. The processor 2080 may control the display unit 2040 to display the user interface in a lateral view or a longitudinal view according to the gravitational acceleration signal acquired by the acceleration sensor. The acceleration sensor may also be used for the acquisition of motion data of a game or a user.
The gyroscope sensor can detect the body direction and the rotation angle of the terminal, and the gyroscope sensor can be cooperated with the acceleration sensor to collect the 3D action of the user on the terminal. The processor 2080 may implement the following functions based on the data collected by the gyroscopic sensor: motion sensing (e.g., changing UI according to a tilting operation by a user), image stabilization at shooting, game control, and inertial navigation.
The pressure sensor may be provided at a side frame of the terminal and/or a lower layer of the display unit 2040. When the pressure sensor is arranged on the side frame of the terminal, the holding signal of the terminal by the user can be detected, and the processor 2080 can perform left-right hand identification or quick operation according to the holding signal acquired by the pressure sensor. When the pressure sensor is disposed at the lower layer of the display unit 2040, the processor 2080 realizes control of the operability control on the UI interface according to the pressure operation of the user on the display unit 2040. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.
The optical sensor is used to collect the ambient light intensity. In one embodiment, the processor 2080 may control the display brightness of the display unit 2040 according to the ambient light intensity collected by the optical sensor. Specifically, when the ambient light intensity is high, the display luminance of the display unit 2040 is turned up; when the ambient light intensity is low, the display luminance of the display unit 2040 is turned down. In another embodiment, the processor 2080 may also dynamically adjust the shooting parameters of the camera assembly 2010 based on the intensity of ambient light collected by the optical sensor.
In this embodiment, the processor 2080 included in the terminal may execute the traffic simulation processing method of the previous embodiment.
The electronic device for executing the traffic simulation processing method according to the embodiment of the present application may also be a server, referring to fig. 21, and fig. 21 is a partial block diagram of the server according to the embodiment of the present application, where the server 2100 may have a relatively large difference due to different configurations or performances, and may include one or more central processing units (Central Processing Units, abbreviated as CPUs) 2122 (e.g., one or more processors) and a memory 2132, and one or more storage media 2130 (e.g., one or more mass storage devices) storing application programs 2142 or data 2144. Wherein the memory 2132 and the storage medium 2130 may be transient storage or persistent storage. The program stored in the storage medium 2130 may include one or more modules (not shown), each of which may include a series of instruction operations in the server 2100. Still further, the central processor 2122 may be configured to communicate with a storage medium 2130 and execute a series of instruction operations in the storage medium 2130 on the server 2100.
The server 2100 may also include one or more power supplies 2126, one or more wired or wireless network interfaces 2150, one or more input/output interfaces 2158, and/or one or more operating systems 2141, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, and the like.
The processor in the server 2100 may be used to perform a traffic simulation processing method.
The embodiment of the application also provides a computer readable storage medium for storing program codes for executing the traffic simulation processing method of each embodiment.
Embodiments of the present application also provide a computer program product comprising a computer program stored on a computer readable storage medium. The processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program so that the computer device executes the traffic simulation processing method described above.
The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
It should be understood that in the description of the embodiments of the present application, plural (or multiple) means two or more, and that greater than, less than, exceeding, etc. are understood to not include the present number, and that greater than, less than, within, etc. are understood to include the present number.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, etc., which can store program codes.
It should also be appreciated that the various embodiments provided by the embodiments of the present application may be arbitrarily combined to achieve different technical effects.
While the preferred embodiment of the present application has been described in detail, the present application is not limited to the above embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit and scope of the present application, and these equivalent modifications or substitutions are included in the scope of the present application as defined in the appended claims.

Claims (16)

1. A traffic simulation processing method, comprising:
dividing a target area which is not covered by sensing equipment in a traffic simulation scene into a plurality of subareas, wherein a ramp exists in at least one subarea;
acquiring a first traffic density of a reference area in the traffic simulation scene, and predicting a second traffic density of the sub-area where the ramp exists according to the first traffic density, wherein the reference area is covered by the sensing equipment;
determining the target traffic density of the ramp according to the second traffic density and a preset first proportional coefficient;
and determining the average distance between vehicles of the ramp according to the target traffic density, and initializing a virtual vehicle in the ramp according to the average distance between vehicles.
2. The traffic simulation processing method according to claim 1, wherein the predicting a second traffic density of the sub-region where the ramp exists based on the first traffic density includes:
determining the ordering positions of the subareas with the ramp in a plurality of subareas;
and interpolating traffic densities corresponding to the sorting positions according to the first traffic densities of the reference areas positioned at the upstream and downstream of the target area to obtain a second traffic density of the subarea with the ramp.
3. The traffic simulation processing method according to claim 2, wherein interpolating traffic densities corresponding to the sorting positions according to the first traffic densities of the reference areas located upstream and downstream of the target area to obtain a second traffic density of the sub-area where the ramp exists, comprises:
when the first traffic density of the reference area positioned at the upstream of the target area is smaller than or equal to the first traffic density of the reference area positioned at the downstream of the target area, determining a traffic density difference value between the two reference areas, determining a first interpolation item according to the traffic density difference value and the sorting position, and obtaining a second traffic density of the subarea with the ramp according to the sum of the first interpolation item and the first traffic density of the reference area positioned at the upstream of the target area;
Or when the first traffic density of the reference area positioned at the upstream of the target area is larger than the first traffic density of the reference area positioned at the downstream of the target area, determining a traffic density difference value between the two reference areas, determining a first interpolation item according to the traffic density difference value and the sorting position, and obtaining a second traffic density of the subarea with the ramp according to the sum of the first interpolation item and the first traffic density of the reference area positioned at the downstream of the target area.
4. The traffic simulation processing method according to claim 1, wherein the initializing a virtual vehicle in the ramp according to the average vehicle pitch includes:
taking the direction from one end of the ramp to the other end of the ramp as an initialization direction, and sequentially determining initial positions of a plurality of virtual vehicles in the ramp along the initialization direction according to the average vehicle spacing;
initializing the virtual vehicle in each of the initial positions.
5. The traffic simulation processing method according to claim 4, wherein sequentially determining initial positions of a plurality of virtual vehicles in the ramp along the initialization direction according to the average vehicle pitch comprises:
Constructing normal distribution by taking the average vehicle spacing as a mean value, and sequentially and randomly acquiring a plurality of initial vehicle spacing from the normal distribution;
and sequentially determining initial positions of a plurality of virtual vehicles in the ramp according to the initial intervals of the vehicles along the initialization direction.
6. The traffic simulation processing method according to claim 5, wherein the initializing a virtual vehicle in the ramp according to the average vehicle pitch further comprises:
determining the vehicle speed of each initial position according to the initial vehicle distance;
the vehicle speed is determined as an initial speed of the virtual vehicle at the initial position.
7. The traffic simulation processing method according to claim 6, wherein the ramp is an up ramp, and the determining the vehicle speed at each of the initial positions according to the initial vehicle pitch includes:
traversing each initial position, and calculating the reciprocal of the initial distance between the current initial position and the previous initial position of the vehicle to obtain a third traffic density of the current initial position;
and acquiring a traffic basic diagram for representing a mapping relation between the vehicle speed and the traffic density, and searching the vehicle speed at the current initial position from the traffic basic diagram according to the third traffic density.
8. The traffic simulation processing method according to claim 6, wherein the ramp is an off ramp, and the determining the vehicle speed at each of the initial positions according to the initial vehicle pitch includes:
traversing each initial position, if the current initial position is not the last traversed to the initial position, calculating the reciprocal of the initial distance between the current initial position and the previous initial position of the vehicle to obtain a fourth traffic density of the current initial position, obtaining a traffic basic diagram for representing the mapping relation between the vehicle speed and the traffic density, and searching the vehicle speed of the previous initial position from the traffic basic diagram according to the fourth traffic density;
and if the current initial position is the last traversed to the initial position, acquiring the highest speed limit of the ramp, and taking the highest speed limit as the vehicle speed of the current initial position.
9. The traffic simulation processing method according to claim 1, wherein the initializing a virtual vehicle in the ramp according to the average vehicle pitch includes:
determining the ordering positions of the subareas with the ramp in a plurality of subareas;
Acquiring first vehicle type duty ratios of various candidate vehicle types in the reference areas at the upstream and downstream of the target area, and interpolating the vehicle type duty ratios corresponding to the sequencing positions according to the first vehicle type duty ratios to obtain second vehicle type duty ratios of various candidate vehicle types in the subarea with the ramp;
initializing a virtual vehicle in the ramp according to the second vehicle type duty ratio and the vehicle average distance.
10. The traffic simulation processing method according to claim 9, wherein interpolating the vehicle type ratios corresponding to the sorting positions according to the first vehicle type ratio to obtain a second vehicle type ratio of each candidate vehicle type in the sub-region where the ramp exists, comprises:
when the first vehicle type duty ratio of the reference area positioned at the upstream of the target area is smaller than or equal to the first vehicle type duty ratio of the reference area positioned at the downstream of the target area, determining a vehicle type duty ratio difference between the two reference areas, determining a second interpolation item according to the vehicle type duty ratio difference and the sequencing position, and obtaining a second vehicle type duty ratio of the subarea with the ramp according to the sum of the second interpolation item and the first vehicle type duty ratio of the reference area positioned at the upstream of the target area;
Or when the first vehicle type duty ratio of the reference area positioned at the upstream of the target area is larger than the first vehicle type duty ratio of the reference area positioned at the downstream of the target area, determining a vehicle type duty ratio difference between the two reference areas, determining a second interpolation item according to the vehicle type duty ratio difference and the sequencing position, and obtaining a second vehicle type duty ratio of the subarea with the ramp according to the sum of the second interpolation item and the first vehicle type duty ratio of the reference area positioned at the downstream of the target area.
11. The traffic simulation processing method according to claim 9, wherein initializing a virtual vehicle in the ramp according to the second vehicle type duty ratio and the vehicle average distance comprises:
dividing a target numerical value interval into a plurality of sub-numerical value intervals according to the duty ratio of each second vehicle type;
randomly generating a target value in the target value interval, and determining a target vehicle type from a plurality of candidate vehicle types according to the attribution relation between the target value and a plurality of sub-value intervals;
initializing a virtual vehicle in the ramp according to the target vehicle type and the average vehicle distance.
12. The traffic simulation processing method according to claim 1, wherein the ramp includes a plurality of target lanes, and the determining the average vehicle pitch of the ramp according to the target traffic density includes:
determining a fifth traffic density of each target lane according to the target traffic density and a preset second proportionality coefficient;
and calculating the reciprocal of each fifth traffic density to obtain the average vehicle distance of each target lane.
13. A vehicle processing apparatus, characterized by comprising:
the system comprises a dividing module, a sensing module and a judging module, wherein the dividing module is used for dividing a target area which is not covered by sensing equipment in a traffic simulation scene into a plurality of subareas, and a ramp exists in at least one subarea;
the prediction module is used for obtaining a first traffic density of a reference area in the traffic simulation scene, and predicting a second traffic density of the sub-area with the ramp according to the first traffic density, wherein the reference area is covered by the sensing equipment;
the density determining module is used for determining the target traffic density of the ramp according to the second traffic density and a preset first proportion coefficient;
And the initialization module is used for determining the average distance between vehicles of the ramp according to the target traffic density and initializing a virtual vehicle in the ramp according to the average distance between the vehicles.
14. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the traffic simulation processing method of any of claims 1 to 12 when executing the computer program.
15. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the traffic simulation processing method of any one of claims 1 to 12.
16. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the traffic simulation processing method of any of claims 1 to 12.
CN202310743007.1A 2023-06-21 2023-06-21 Traffic simulation processing method and device, electronic equipment and storage medium Pending CN116956554A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117172036A (en) * 2023-11-02 2023-12-05 腾讯科技（深圳）有限公司 Road traffic simulation method and related device
CN117292548A (en) * 2023-11-10 2023-12-26 腾讯科技（深圳）有限公司 Traffic simulation method, device, equipment and storage medium
CN117392359A (en) * 2023-12-13 2024-01-12 中北数科(河北)科技有限公司 Vehicle navigation data processing method and device and electronic equipment

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117172036A (en) * 2023-11-02 2023-12-05 腾讯科技（深圳）有限公司 Road traffic simulation method and related device
CN117172036B (en) * 2023-11-02 2024-02-27 腾讯科技（深圳）有限公司 Road traffic simulation method and related device
CN117292548A (en) * 2023-11-10 2023-12-26 腾讯科技（深圳）有限公司 Traffic simulation method, device, equipment and storage medium
CN117292548B (en) * 2023-11-10 2024-02-09 腾讯科技（深圳）有限公司 Traffic simulation method, device, equipment and storage medium
CN117392359A (en) * 2023-12-13 2024-01-12 中北数科(河北)科技有限公司 Vehicle navigation data processing method and device and electronic equipment
CN117392359B (en) * 2023-12-13 2024-03-15 中北数科(河北)科技有限公司 Vehicle navigation data processing method and device and electronic equipment

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