CN117172036A

CN117172036A - Road traffic simulation method and related device

Info

Publication number: CN117172036A
Application number: CN202311448709.3A
Authority: CN
Inventors: 杜海宁
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2023-11-02
Filing date: 2023-11-02
Publication date: 2023-12-05
Anticipated expiration: 2043-11-02
Also published as: CN117172036B

Abstract

The embodiment of the application discloses a road traffic simulation method and a related device, which are used for realizing short-term prediction of a road traffic state without frequently updating and displaying the road traffic state, improving simulation efficiency and reducing calculation resource occupation. The method comprises the following steps: configuring target time information required to carry out simulation deduction in a vehicle simulation system, wherein the target time information comprises a simulation duration time, a first time length and a first starting time of primary simulation deduction, and the first time length indicates the interval time when each two adjacent simulation deductions are executed; calculating a second start time based on the first start time and the first time length; when the current time reaches the second starting time, acquiring first initial simulation data of the target simulation road at the second starting time; and carrying out simulation deduction according to the first initial simulation data and the first historical driving data, generating a simulation result of the Nth simulation deduction, and obtaining a first target time period from the second starting time and the simulation duration period.

Description

Road traffic simulation method and related device

Technical Field

The embodiment of the application relates to the technical field of computers, in particular to a road traffic simulation method and a related device.

Background

In the existing microscopic simulation software, the real-time twin traffic simulation system can reproduce the real-world vehicle state in a high-precision and low-time-delay mode.

However, the existing real-time twin traffic simulation system lacks the capability of predicting the road traffic state after a certain period of time, and only the road traffic state condition can be frequently displayed to people, so that people can only complete the visualization of the road simulation through the system and can not exert the calculable advantage, and the problems that the existing real-time twin traffic simulation has lower simulation efficiency, occupies more calculation resources in the display process and the like are caused.

In view of the above problems, no effective solution has been proposed at present.

Disclosure of Invention

The embodiment of the application provides a road traffic simulation method and a related device, which are used for realizing short-term prediction of a road traffic state, and the road traffic state is not required to be updated and displayed frequently, so that the simulation efficiency can be improved, and the occupation of computing resources can be reduced.

In a first aspect, an embodiment of the present application provides a road traffic simulation method. The method comprises the following steps: pre-configuring target time information required to be subjected to simulation deduction in a vehicle simulation system, wherein the target time information comprises a simulation duration time, a first time length and a first starting time of initial simulation deduction, the target time information corresponds to a target simulation road required to be subjected to the simulation deduction, the simulation duration time is used for indicating a time period when vehicle driving data in a preset future time length are predicted, and the first time length is used for indicating an interval time when the simulation deduction is executed every two adjacent times; calculating a second starting time based on the first starting time and the first time, wherein the second starting time is a predicted starting time when the N-th simulation deduction is performed, N is more than or equal to 2, and N is an integer; when the current moment reaches the second starting moment, acquiring first initial simulation data of the target simulation road at the second starting moment, wherein the first initial simulation data are determined by real-time driving data of the second starting moment, the real-time driving data represent vehicle driving data acquired on the target road in real time, and the target road corresponds to the target simulation road; and carrying out the simulation deduction according to the first initial simulation data and the first historical driving data, and generating a simulation result of the Nth simulation deduction, wherein the simulation result of the Nth simulation deduction is used for indicating vehicle driving data collected from a target simulation road in a first target time period during the Nth simulation deduction, the first historical driving data is used for indicating vehicle driving data collected from the target road in the first target time period on a historical period, and the first target time period is obtained from the second starting time and the simulation duration period.

In a second aspect, an embodiment of the present application provides a traffic simulation apparatus. The traffic simulation device comprises a configuration unit, an acquisition unit and a processing unit. The configuration unit is used for pre-configuring target time information needed to be subjected to simulation deduction in the vehicle simulation system, the target time information comprises a simulation duration time period, a first duration time and a first starting time of initial simulation deduction, the target time information corresponds to a target simulation road needed to be subjected to the simulation deduction, the simulation duration time period is used for indicating duration time when the simulation deduction is continuously executed, and the first duration time is used for indicating interval time when the simulation deduction is executed every two times adjacently. The processing unit is used for calculating a second starting time based on the first starting time and the first time, wherein the second starting time is a predicted starting time when the simulation deduction is carried out for the Nth time, N is more than or equal to 2, and N is an integer. The acquisition unit is used for acquiring first initial simulation data of the target simulation road at the second starting time when the current time reaches the second starting time, wherein the first initial simulation data are determined by real-time driving data of the second starting time, the real-time driving data represent vehicle driving data acquired on the target road in real time, and the target road corresponds to the target simulation road. The processing unit is configured to perform the simulation deduction according to the first initial simulation data and first historical driving data, generate a simulation result of the nth simulation deduction, and use the simulation result of the nth simulation deduction to indicate vehicle driving data collected from a target simulation road in a first target time period during the nth simulation deduction, where the first historical driving data is used to indicate vehicle driving data collected from the target road in the first target time period on a historical period, and the first target time period is obtained from the second starting time and the simulation duration.

In some alternative examples, the processing unit is further to: after the simulation deduction is carried out according to the first initial simulation data and the first historical driving data to generate a simulation result of the nth simulation deduction, the simulation result of the nth simulation deduction is quantized to obtain road network evaluation index information; generating a road network thermodynamic diagram deduced by the Nth simulation based on the road network evaluation index information; and displaying the road network thermodynamic diagram deduced by the Nth simulation.

In other alternative examples, the processing unit is configured to: determining a target simulation vehicle which needs to be added on the target simulation road in the continuous process of the Nth simulation deduction according to the first historical driving data; updating the first initial simulation data of the target simulation vehicle based on a preset mesoscopic simulation deduction model, and carrying out the simulation deduction to generate a simulation result of the Nth simulation deduction.

In other alternative examples, the processing unit is configured to: when the Nth simulation deduction progress to the ending time of the Nth simulation deduction, a mesoscopic driving track of the target simulation vehicle driving in the first target time period is saved; and generating a simulation result of the Nth simulation deduction according to the mesoscopic driving track.

In other alternative examples, the processing unit is configured to: determining road section density and blocking density in each simulation period on the target simulation road based on the mesoscopic driving track, wherein the first target period comprises a plurality of simulation periods; determining a lower vehicle speed limit and an expected free-running speed corresponding to the target simulation vehicle under the blocking density; calculating an actual running speed of the target simulation vehicle in a running direction based on the link density and the blocking density, the lower vehicle speed limit, and the desired free running speed within each of the simulation periods; calculating an actual vehicle position of the target simulation vehicle based on the first initial simulation data and the actual traveling speed; and collecting the actual running speeds and the actual vehicle positions of all the target simulation vehicles to obtain simulation results of the Nth simulation deduction.

In other alternative examples, the processing unit is configured to: determining a target vehicle passing through the target road in a first target time period on the history period according to the first history driving data; adding a target simulation vehicle to the target simulation road according to a dynamic shunt proportion based on the first initial simulation data and the target vehicle, and performing simulation deduction, wherein the target simulation vehicle is related to the target vehicle, and the dynamic shunt proportion is used for indicating the driving flow direction occupation ratio condition of the target simulation vehicle on the target simulation road; and generating a simulation result of the Nth simulation deduction when the Nth simulation deduction is carried out to the ending time of the Nth simulation deduction.

In other alternative examples, the processing unit is further configured to: before adding a target simulation vehicle to the target simulation road according to a dynamic shunt proportion based on the first initial simulation data and the target vehicle, acquiring the number of vehicles at each road outlet on the target road in the first target time period from the first initial simulation data; and calculating the number ratio of the number of vehicles at each road outlet to obtain the dynamic shunt proportion.

In other alternative examples, the processing unit is further configured to: counting the number of each lane on the target road in the first target time period before adding the target simulation vehicle to the target simulation road according to a dynamic diversion ratio based on the first initial simulation data and the target vehicle; and calculating the proportion among the number of each lane to obtain the dynamic diversion proportion.

In other alternative examples, the product between N and the first time period is equal to the simulation duration.

In other alternative examples, the acquisition unit is further configured to: and before the simulation deduction is carried out according to the first initial simulation data and the first historical driving data and the simulation result of the Nth simulation deduction is generated, when the current moment reaches the first starting moment, acquiring second initial simulation data of the target simulation road at the first starting moment, wherein the second initial simulation data is determined by the real-time driving data at the first starting moment. The processing unit is used for carrying out the simulation deduction according to the second initial simulation data and the second historical driving data, generating a simulation result of the initial simulation deduction, wherein the simulation result of the initial simulation deduction is used for indicating vehicle driving data collected from a target simulation road in a second target time period during the initial simulation deduction, the second historical driving data is used for indicating vehicle driving data collected from the target road in the second target time period during the historical period, and the second target time period is obtained from the first starting time and the simulation duration period.

In other optional examples, the target time information further includes an end time of the initial simulation deduction; the processing unit is used for: determining a target vehicle passing through the target road in a second target time period on the history period according to the second history driving data; adding a target simulation vehicle to the target simulation road according to a dynamic shunt proportion based on the second initial simulation data and the target vehicle, and performing simulation deduction, wherein the target simulation vehicle is related to the target vehicle, and the dynamic shunt proportion is used for indicating the driving flow direction occupation ratio condition of the target simulation vehicle on the target simulation road; and generating a simulation result of the primary simulation deduction when the primary simulation deduction progresses to the ending time of the primary simulation deduction.

A third aspect of an embodiment of the present application provides a traffic simulation apparatus, including: memory, input/output (I/O) interfaces, and memory. The memory is used for storing program instructions. The processor is configured to execute the program instructions in the memory to execute the road traffic simulation method corresponding to the implementation manner of the first aspect.

A fourth aspect of the embodiments of the present application provides a computer-readable storage medium having instructions stored therein, which when run on a computer, cause the computer to perform to execute the method corresponding to the embodiment of the first aspect described above.

A fifth aspect of the embodiments of the present application provides a computer program product comprising instructions which, when run on a computer or processor, cause the computer or processor to perform the method described above to perform the embodiment of the first aspect described above.

From the above technical solutions, the embodiment of the present application has the following advantages:

in the embodiment of the application, target time information required to be subjected to simulation deduction is preconfigured in a vehicle simulation system, wherein the target time information comprises a simulation duration time, a first time length and a first starting time of initial simulation deduction, the simulation duration time is used for indicating a time period when predicting vehicle driving data in a preset future time length, the first time length is used for indicating interval time when each two adjacent simulation deductions are executed, the target time information corresponds to a target simulation road required to be subjected to simulation deduction, and further, a second starting time is calculated based on the first starting time and the first time length, the second starting time is a prediction starting time when the nth simulation deduction is carried out, and N is an integer greater than or equal to 2. Under the condition that the current time reaches the second starting time, acquiring first initial simulation data of the target simulation road at the second starting time, wherein the first initial simulation data is determined by real-time running data of the second starting time, the real-time running data represents vehicle running data acquired on the target road in real time, and the target road corresponds to the target simulation road. In this way, simulation deduction is performed according to the first initial simulation data and the first historical driving data, and a simulation result of the Nth simulation deduction is generated. The simulation result of the nth simulation deduction is used for indicating vehicle running data collected from a target simulation road in a first target time period during the nth simulation deduction, the first historical running data is used for indicating vehicle running data collected from the target road in the first target time period on a historical period, and the first target time period is obtained from a second starting time and a simulation duration. That is, in the embodiment of the present application, the target time information required to perform the simulation deduction is preset, and then the start time (i.e., the second start time) of the nth simulation deduction is determined by the first duration and the start time (i.e., the first start time) of the first simulation deduction, and on the basis that the real-time running data of the start time of the nth simulation deduction is taken as the initial simulation data of the nth simulation deduction, the nth simulation deduction is completed by comprehensively considering the historical running data in the same first target period, so that not only is the whole simulation deduction flow described, but also the short-term prediction is implemented on the road traffic in the first target period after the second start time. In addition, the application also can describe the updating process of the simulation result in the process of continuously carrying out the simulation deduction by configuring the first time length in the vehicle simulation system, so that the simulation result of every two adjacent simulation deductions is not required to be frequently displayed to a user, but the latest simulation result of the simulation deduction is updated and displayed after the first time length, so that the real-time twin is upgraded from 'visualization' to a computable digital space, the decision is assisted by prediction, the virtual world and the physical world are connected, the purpose of closed-loop control is finally realized, the simulation efficiency of road traffic is improved, and the occupation of calculation resources is reduced.

Drawings

In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic diagram of a system architecture provided by an embodiment of the present application;

FIG. 2 shows a flowchart of a road traffic simulation method provided by an embodiment of the present application;

FIG. 3 is a schematic illustration of an alternative road traffic simulation method in accordance with an embodiment of the present application;

FIG. 4 is a schematic illustration of another alternative road traffic simulation method in accordance with an embodiment of the present application;

FIG. 5 is a schematic flow chart of simulation deduction provided in an embodiment of the present application;

FIG. 6 is a schematic diagram of a road network thermodynamic diagram provided by an embodiment of the present application;

FIG. 7 is a schematic diagram of a functional module of a traffic simulation device according to an embodiment of the present application;

fig. 8 shows a schematic hardware structure of a traffic simulation device according to an embodiment of the present application.

Description of the embodiments

It will be appreciated that in the specific embodiments of the present application, related data such as user information is involved, and when the above embodiments of the present application are applied to specific products or technologies, user permissions or consents need to be obtained, and the collection, use and processing of related data need to comply with related laws and regulations and standards of related countries and regions.

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, 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 capable of being practiced otherwise than as specifically illustrated and described. 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 but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Digital twinning is a technical means for creating a virtual entity of a physical entity in a digital manner, simulating, verifying, predicting and controlling the whole life cycle process of the physical entity by means of historical data, real-time data, algorithm models and the like.

In a road network scene, digital twinning can be realized by establishing a virtual parallel world on an expressway, mapping elements such as environments, vehicles, events and the like of the physical world of the expressway completely in real time, fully sensing and dynamically monitoring sensor data distributed in the expressway to form accurate information expression and mapping of the virtual road to the entity road in an information dimension, so that a manager can master the global condition of the expressway without being on the expressway, and the problems of difficulty in checking and managing the whole road section, delay in event discovery, difficulty in event duplication and the like are solved. It has not only simulation capability, but also prediction and control capability.

The method is characterized in that in a road section area which can be covered by the sensor, information acquired by multidimensional traffic facilities such as video, radar and the like is automatically carried and fused, and original incoherent target information acquired by various sensors is mutually verified and mutually supplemented through a target fusion algorithm, so that basically complete target attribute information is formed, and accurate depiction of a vehicle running track on a high-speed main line is realized. For example, the association relation of the map is used for establishing the association between the radar detected target and the video identified target. Meanwhile, a real-time detection target is superimposed on a high-precision map, the butt joint of a physical space and a virtual space is realized, the holographic perception of digital mapping is completed, further, the real-time reproduction simulation can be carried out on the real-time detection target in a vehicle simulation system, and the simulation deduction is carried out on the basis, so that the description, diagnosis, prediction and decision-making of core services such as traffic hidden danger, traffic event and traffic jam are realized, the real-time efficient intelligent analysis and active management and control are realized, the closed-loop control is finally realized, the refinement, the intellectualization, the standardization and the specialization of the highway management are realized, and a solid foundation is laid for the traffic management.

In scenes such as highway networks, road traffic states of covered road sections, such as traffic flow, traffic flow density, traffic flow speed and the like, can be acquired through sensing equipment. The vehicle simulation system can perform reproduction simulation on the road traffic state, namely, the road traffic state is reproduced in the vehicle simulation system through a simulation deduction flow.

However, the traditional real-time twin traffic simulation system lacks the capability of predicting the road traffic state after a certain period of time, and only the road traffic state condition can be frequently displayed to people, so that people can only complete the visualization of the road simulation through the system and can not exert the calculable advantage, and the problems that the existing real-time twin traffic simulation has lower simulation efficiency, occupies more calculation resources in the display process and the like are caused.

Therefore, in order to solve the technical problems described above, the embodiment of the application provides a road traffic simulation method. The road traffic simulation method provided by the application can be applied to the system architecture shown in fig. 1. As shown in fig. 1, the system architecture includes at least a terminal device and a server. The terminal device and the server may be directly connected or indirectly connected through wired network communication or wireless network communication, and the present application is not limited in particular. In addition, the server may be connected to the terminal device and then used to provide services for the terminal device or an application installed on the terminal device, where the application may be a video application, an instant messaging application, a browser application, an educational application, a game application, or the like. For example, a database may also be provided on or separate from the server for providing data storage services for the server, such as a game data storage server or the like. The described wired network communications may include, but are not limited to, local area networks, metropolitan area networks, and wide area networks. The wireless network communications described may include, but are not limited to, bluetooth, wireless fidelity (wireless fidelity, wi-Fi), and other manners of implementing wireless communications.

The above-mentioned terminal device may be a terminal configured with an application program, and may include, but is not limited to, at least one of: a mobile phone, a notebook computer, a tablet computer, a palm computer, a mobile internet device (mobile internet devices, MID), a PAD, a desktop computer, a smart television, a smart voice interaction device, a smart home appliance, a vehicle-mounted terminal, an aircraft, a Virtual Reality (VR) terminal, an augmented reality (augmented reality, AR) terminal, a Mixed Reality (MR) terminal, and the like.

The servers shown in fig. 1 may be independent physical servers, may be server clusters or distributed systems formed by a plurality of physical servers, and may also be cloud servers that provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (context delivery network, CDNs), basic cloud computing services such as big data and artificial intelligence platforms, and the like.

As shown in connection with fig. 1, the above-mentioned road traffic simulation method can be implemented in the terminal device shown in fig. 1. Specifically, the terminal device may pre-configure target time information required to perform simulation deduction in the vehicle simulation system, where the target time information includes a simulation duration period, a first duration period, and a first start time of initial simulation deduction, where the simulation duration period is used to indicate a period when predicting vehicle driving data in a preset duration period, the first duration period is used to indicate an interval time when performing the simulation deduction every two adjacent times, the target time information corresponds to a target simulation road required to perform the simulation deduction, and further calculate, based on the first start time and the first duration period, a second start time, where N is an integer greater than or equal to 2, where N is a predicted start time when performing the nth simulation deduction. Under the condition that the current moment reaches the second starting moment, the terminal equipment acquires first initial simulation data of the target simulation road at the second starting moment, the first initial simulation data are determined by real-time running data of the second starting moment, the real-time running data represent vehicle running data acquired on the target road in real time, and the target road corresponds to the target simulation road. In this way, the terminal device performs simulation deduction according to the first initial simulation data and the first historical driving data, and generates a simulation result of the Nth simulation deduction. The simulation result of the nth simulation deduction is used for indicating vehicle running data collected from a target simulation road in a first target time period during the nth simulation deduction, the first historical running data is used for indicating vehicle running data collected from the target road in the first target time period on a historical period, and the first target time period is obtained from a second starting time and a simulation duration.

Alternatively, in the embodiment of the present application, the above-mentioned road traffic simulation method may also be implemented by the server shown in fig. 1; alternatively, the method may be implemented by the server and the terminal device shown in fig. 1 together.

In some alternative examples, the above-mentioned road traffic simulation method may also be applied to an application of performing a simulation process on a simulated vehicle in a simulated road scene based on an expressway or the like, and may also include, but not limited to, a simulation application of performing cargo transportation based on a specific type of transportation vehicle in a logistics transportation process, a simulation application of performing a daily operation process based on a network bus, or the like.

In other alternative modes, the road traffic simulation method can be applied to the simulation process of traffic systems such as intelligent traffic systems and intelligent vehicle-road cooperative systems.

The intelligent transportation system (intelligent traffic system, ITS) is also called intelligent transportation system (intelligent transportation system), which is a comprehensive transportation system for effectively and comprehensively applying advanced scientific technologies (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operation study, artificial intelligence and the like) to transportation, service control and vehicle manufacturing, and enhancing the connection among vehicles, roads and users, thereby forming a comprehensive transportation system for guaranteeing safety, improving efficiency, improving environment and saving energy.

The described intelligent vehicle-road collaboration system (intelligent vehicle infrastructure cooperative systems, IVICS), is one development of Intelligent Transportation Systems (ITS). The vehicle-road cooperative system adopts advanced wireless communication, new generation internet and other technologies, carries out vehicle-vehicle and vehicle-road dynamic real-time information interaction in all directions, develops vehicle active safety control and road cooperative management on the basis of full-time idle dynamic traffic information acquisition and fusion, fully realizes effective cooperation of people and vehicles and roads, ensures traffic safety, improves traffic efficiency, and forms a safe, efficient and environment-friendly road traffic system.

In practical application, the method of road traffic mentioned in the present application may be applied to other scenes, and the embodiment of the present application is not limited.

In order to facilitate understanding of the simulation method of the present application, a terminal device is taken as an execution body for executing the simulation method of road traffic of the present application, and the simulation method of road traffic provided by the embodiment of the present application is described with reference to the accompanying drawings. Fig. 2 shows a flowchart of a road traffic simulation method according to an embodiment of the present application. As shown in fig. 2, the road traffic simulation method may include the steps of:

201. The method comprises the steps that target time information needing to be subjected to simulation deduction is preconfigured in a vehicle simulation system, the target time information comprises a simulation duration time period, a first duration time and a first starting time of initial simulation deduction, the target time information corresponds to a target simulation road needing to be subjected to simulation deduction, the simulation duration time period is used for indicating a time period when vehicle driving data in a future preset duration time period are predicted, and the first duration time period is used for indicating an interval time when each two adjacent simulation deductions are executed.

In this example, the provided simulation method of road traffic may be applied to include, but is not limited to, one or more simulation software, such as mesoscopic traffic simulation software, and the like. The simulation software referred to may include, but is not limited to, simulation software requiring networking or simulation software not requiring networking. The mesoscopic traffic simulation software described may include, but is not limited to, a speed-density model or a point queuing model, etc., and is not limited in the present application.

Fig. 3 is a schematic diagram illustrating an alternative road traffic simulation method according to an embodiment of the present application. As shown in fig. 3, a road set that allows road traffic simulation may be viewed in the digital twin road simulation system, and all or part of the roads may be selected from the road set as the target simulation road, for example, by performing a touch operation on the road 1 and the road 2 to determine the road 1 and the road 2 as the target simulation road, and perform the road traffic simulation.

In addition, the above-mentioned target time information required to perform simulation deduction can be flexibly set by staff according to service requirements or priori knowledge. The target time information includes a simulation duration, a first time length, and a first start time of initial simulation deduction. As a schematic description, the simulation duration may be expressed using Td, and it can be understood that the period when the vehicle running data within the future preset time period is predicted, for example, 30 minutes in the future, 1 hour in the future, and the like. The first time period is denoted by Δt, for example, and it is understood that the interval time between two adjacent simulation deductions is different by a first time period, for example, Δt=5 minutes, Δt=10 minutes, or the like, between the start time of the initial simulation deduction and the start time of the 2 nd simulation deduction. The above-mentioned target time information also includes, for example, the end time of the initial simulation deduction.

Fig. 4 is a schematic diagram illustrating another alternative road traffic simulation method according to an embodiment of the present application. As shown in fig. 4, the above target time information is set through the interactive interface of the simulation system, for example, the simulation duration td=30 minutes, the first starting time is 8:15, and the first duration Δt=30 minutes. Optionally, the end time of the initial simulation deduction in the target time information may be 8:16.

It should be noted that the first start time of the initial simulation deduction is a time after the current time, and the end time of the initial simulation deduction is a time after the first start time, so as to predict the road traffic condition after a short time.

Fig. 5 is a schematic flow chart of simulation deduction provided in the embodiment of the present application. As shown in fig. 5, the simulation deduction branch line may be separated from the simulation deduction main line, so as to complete the simulation deduction flow at different starting moments through different simulation deduction branch lines. For example, a single branch line may be separated from the simulation deduction main line at the first starting time (i.e. the starting flow of the initial simulation deduction) t1=8:15 to complete the simulation deduction flow starting from t1=8:15. After a first time period (e.g., Δt=5 minutes) has elapsed on the basis of the first start time T1, another individual branch line is separated from the simulation deduction main line at a second start time, e.g., t2=8:20, to complete the simulation deduction flow starting from t2=8:20. Similarly, after the first time period Deltat passes, another independent branch line is separated from the simulation deduction main line to complete the simulation deduction flow of the next starting time.

In other words, when the first start time in the above-mentioned target time information is reached at the current time of the system, the above-mentioned initial simulation deduction flow starts to be executed. Similarly, when the current time of the system reaches the starting time determined according to the first starting time and the first duration, executing the nth simulation deduction flow. N is an integer greater than or equal to 2.

For example, taking the target time information shown in fig. 4 as an example, when the current time reaches 8:15, the process starts at 8:15 to predict vehicle travel data within 30 minutes of the future after 8:15, i.e. at this point 8:15 a road traffic condition of 8:45 can be predicted. Further, after Δt=5 minutes, i.e. when the current time continues to reach 8:20, a second simulation deduction is performed at 8:20 to predict vehicle driving data within 30 minutes after 8:20, i.e. a road traffic state of 8:50 can be predicted from 8:20. Similarly, the third simulation deduction, the fourth simulation deduction, the nth simulation deduction, etc. are performed in the same manner, and the embodiments of the present application are not described in detail.

It should be noted that the above-mentioned target simulation road is a preset simulation road, and the number thereof may include, but is not limited to, one or more.

In addition, for the main line simulation deduction mentioned in fig. 5, it is a main flow of simulation in the system according to the sensing data acquired in real time. Firstly, building up basic static elements of simulation through modeling of a high-precision map and other elements (such as signal lamps), and then reproducing the real-time perceived vehicle states (vehicle type, speed, position, course angle and the like) in a system in a one-to-one manner through a real-time perception and fusion algorithm, so that the effect of real-time twin simulation is realized. The main line simulation deduction is to reproduce the running state of the real vehicle in the system in a low-delay and high-accuracy mode.

The main line simulation deduction takes dynamic and static data as input, and can reproduce the state of the vehicle in the real world in real time. The described dynamic data may include, but is not limited to, structured road and object state information extracted when all driving areas of the road network are covered globally or non-globally by the sensing device. The static data can include, but is not limited to, high-precision maps and other traffic elements required for building a basic road network, such as a timing scheme of a signal lamp, and the like, and the background traffic network required for simulation can be built through the static data. Meanwhile, by utilizing historical traffic volume (origin destination, OD) data and combining traffic distribution, mesoscopy and other traffic models, branch simulation deduction can be carried out on the basis of a simulation deduction main line, so that different functional applications such as event simulation, management and control simulation and the like are realized.

For the simulation deduction branch line shown in fig. 5, a real-time twin simulation mode based on perception full coverage or perception non-full coverage is adopted to simulate traffic flow in the real world, meanwhile, at any moment in simulation, according to different assumptions or scenes which actually appear, the simulation deduction branch line is opened from the main line of the real-time simulation to perform deduction simulation on the corresponding scene, and the time-space influence of the simulation deduction branch line is predicted in a short time, so that functional applications such as event simulation, management and control simulation and the like are realized on the basis of the simulation deduction branch line.

The main line simulation visualizes the real traffic in the form of mesoscopic simulation, but in order to make the simulation realize greater practical significance, a branch line deduction simulation is introduced. The prediction of the what if is performed on the basis of the reproduction of the real traffic state is performed, virtual events (such as traffic accidents, construction road sealing and the like) which possibly affect traffic are added into the simulation, and then acceleration deduction is performed in the system by using the simulation.

Meanwhile, the branch line simulation does not need to receive real vehicle state data transmitted by the sensing equipment in real time, so that the cloud data stream calculation, distributed calculation, data synchronization and other technologies can be used for acceleration. Moreover, the mesoscopic simulation model has higher simulation running efficiency than the microscopic simulation model, so that the effect of rapidly obtaining the deduction simulation result in a short time is realized, for example, the simulation result of 30 minutes (system time) in the future is obtained through 1 minute (real time) simulation deduction.

In some alternative examples, for the above-mentioned initial simulation deduction flow, it may be understood with reference to the following manner, namely: under the condition that the current time reaches the first starting time, first obtaining second initial simulation data of the target simulation road at the first starting time, wherein the second initial simulation data is determined by real-time driving data at the first starting time. Similarly, a second target time period is also determined based on the first start time and the simulation continuation road section, for example, the second target time period is [ the first start time, the first start time+the simulation continuation period ], so as to obtain second historical driving data corresponding to the second target time period in the historical period. And then, performing simulation deduction according to the second initial simulation data and the second historical driving data to generate a simulation result of the initial simulation deduction.

The simulation result of the first simulation deduction is used for indicating the vehicle driving data acquired from the target simulation road in the second target time period during the first simulation deduction. In addition, the second history travel data mentioned above is used to indicate vehicle travel data collected for the target road in the second target period of time over the history period. The second target time period is described as being derived from the first start time and the simulation duration.

For example, taking the target time information shown in fig. 4 as an example, it is assumed that the time t0=8:00 at which the real-time simulation starts is performed, the first start time t1=8:15, and the simulation duration td=30 minutes, at which the user wants to predict the road network traffic state for 30 minutes in the future from the first start time t1=8:15. At this time, when the current time of the system reaches t1=8:15, it is necessary to acquire the second initial simulation data of the target simulation road at t1=8:15.

As a schematic description, for how to obtain the second initial simulation data when t1=8:15, it may be determined according to the coverage situation of the sensing device in the road network. That is, the sensing equipment deployed by the application can cover all or part of the running area of the road network, can extract the status information of the structured road and the target object, and can realize the functions of overall sensing of elements such as vehicles, roads, events and the like, positioning and target fusion pursuit of centimeter-level target lanes and the like. By means of algorithms such as target detection, vision pursuit and track splicing in the twin technology, when a vehicle runs in the sensing range of sensing equipment, key information (such as vehicle type, speed, position, course angle and the like) of the vehicle can be resolved, and real-time determination of the running track of the vehicle is realized.

For example, in the case that the sensing device can fully cover the target simulated road, lane-level track information of a vehicle traveling through the sensing area can be collected by the sensing device, and each piece of vehicle track information data includes, but is not limited to, the following fields: unique identification of the vehicle, vehicle type, list of timestamps, list of locations, list of degrees/heading angles, etc. Wherein the time stamp list is used to record the frequency of the perceived/transmitted track points of the perceiving device, such as once every 10 ms. The location list corresponds to a list of time stamps, i.e. each time stamp corresponds to the location of the vehicle at that moment, and generally consists of longitude and latitude and elevation, where only longitude and latitude, i.e. x, y values, are considered and elevation z (or altitude) is ignored. The speed/heading angle list corresponds to a list of time stamps, i.e., each time stamp corresponds to the speed and heading angle of the vehicle at that time. Because the time stamp interval is in the millisecond level, the actual running track of the vehicle can be sampled and collected at a very high frequency, the position points (longitude and latitude) in the position lists are connected by straight-line segments in the time stamp sequence, and a segmented folded line segment can be obtained, namely, straight-line segments are arranged between the points corresponding to every two adjacent time stamps, and a plurality of straight-line segments form a lane-level track collected through perception. Under the condition that sampling points are sufficiently dense (frequency is sufficiently high), the segmented broken line section vehicle track reproduced by the track point list and the actual running track are sufficiently close, and the position value of any time point (non-sampling moment) can be obtained between two adjacent data points at the time point in a linear difference mode, namely, the lane-level running track of a vehicle can be obtained, and the second initial simulation data is generated according to the running track.

Otherwise, if the sensing device cannot fully cover the target simulation road, the sensing device may collect an image before the vehicle enters the blind area section and an image after the vehicle exits from the blind area section, and further determine a vehicle state of the vehicle entering the blind area section based on the image before the vehicle enters the blind area section and the image after the vehicle exits from the blind area section, so as to combine the vehicle state of the non-blind area section as the second initial simulation data corresponding to t1=8:15. It should be noted that the blind area road section is understood to be a road section when the sensing device cannot perform data collection. For the vehicle state of the non-blind area road section, the foregoing collecting process in the full coverage scene may be referred to for understanding, which is not described herein.

Likewise, a second target period of time, for example, the second target period of time is [ T1, t1+td ], i.e., [8:15,8:45], needs to be determined based on the first start time (t1=8:15) and the simulation duration (i.e., td=30 minutes). Further, second historical driving data for the period of time is obtained [8:15,8:45] from the historical period. In this way, by performing simulation deduction based on the second initial simulation data at the first start time t1=8:15 and the second historical driving data in the [8:15,8:45] time period, the simulation result of the initial simulation deduction can be determined, that is, the driving data of the vehicle in 8:45 can be predicted from 8:15.

It should be noted that the history period may include, but is not limited to, a month ago, a 10 day ago, a year ago, etc., and the embodiment of the present application is not limited thereto.

Illustratively, the above-configured target time information further includes an end time of the initial simulation deduction, for example, te1=8:16. Then, for the process of how to perform the simulation deduction based on the second initial simulation data and the second historical driving data and generate the simulation result of the initial simulation deduction, the target vehicle passing through the target road in the second target time period on the historical period may be determined according to the second historical driving data; further, adding a target simulation vehicle to the target simulation road according to the dynamic shunt proportion based on the second initial simulation data and the target vehicle, and performing simulation deduction, wherein the target simulation vehicle is related to the target vehicle. The described dynamic split ratio is used to indicate the driving flow direction of the target simulation vehicle on the target simulation road. It should be noted that, for how to calculate the dynamic split ratio, it may be specifically understood by referring to one or more of the following modes 1 and 2, and details will not be described herein. In this way, after the simulation deduction is performed, when the initial simulation deduction is performed to the end time of the initial simulation deduction, a simulation result of the initial simulation deduction is generated.

202. Based on the first starting time and the first duration, calculating a second starting time, wherein the second starting time is a predicted starting time when the Nth simulation deduction is performed, N is more than or equal to 2, and N is an integer.

In this example, the user wishes to be able to continuously predict the road network traffic state from the predicted start time to within the simulation duration Td. That is, in an ideal case, the user wants to be able to view the road network traffic state from the front end display screen until the predicted start time starts for the simulation duration Td. In actual operation, however, the user does not need to pay attention to the change situation of the road network traffic state at all times, and updating the change situation of the road network traffic state in the front-end display screen too frequently consumes more computing resources of the system.

Therefore, in the embodiment of the present application, a first time length (i.e., Δt mentioned above) is introduced into the target time information configured in advance in the vehicle simulation system, and the road network traffic state interval is updated once every Δt time by the first time length, thereby giving consideration to timeliness and computing resources. In this regard, after the simulation result of the initial simulation deduction is generated in the aforementioned step 201, a second start time may be calculated based on the first start time and the first time length to instruct the predicted start time when the nth simulation deduction is performed by the second start time. N is an integer greater than or equal to 2.

For example, taking n=2 as an example, let the simulation start time t0=8:00, the first start time t1=8:15, and the first duration Δt=5 minutes. If the user wishes to predict the road network traffic condition for a future simulation duration td=30 minutes every interval Δt=5 minutes starting from t1=8:15. That is, a first simulation deduction may be performed at t1=8:15 to continuously predict the road network traffic state of 8:45. Then, taking the first starting time t1=8:15 as a starting point, and entering a second simulation deduction after an interval Δt=5 minutes, namely in the case that the second starting time (denoted by T2) is 8:20. In other words, the road network traffic state at the second start time t2=8:20 can be predicted for 30 minutes of the future simulation duration, i.e., the road network traffic state is continuously predicted from 8:20 to 8:50, and the road network traffic state does not need to be frequently updated in the front-end display screen between the first start time and the second start time (i.e., 8:15 to 8:20).

Similarly, in the case of n=3, the second simulation deduction may be performed at intervals Δt=5 minutes to the 3 rd simulation deduction on the basis of the starting time of the second simulation deduction (i.e. t2=8:20), where the calculated second starting time is t3=t1+ (3-1) ×Δt=8:25, and the road network traffic state when the future simulation duration is 30 minutes, i.e. the road network traffic state of 8:55, may be predicted at the second starting time t3=8:25, and the road network traffic state does not need to be frequently updated in the front display screen between the starting time of the previous simulation deduction and the second starting time of the current simulation deduction (i.e. 8:20 to 8:25).

For the case where N is the starting time corresponding to the other value, the calculation process of the starting time calculated by n=2 or n=3 may be referred to for understanding, and will not be described herein.

As an exemplary description, for how many simulation deduction branches need to be separated from the simulation deduction main line to complete the simulation deduction of the entire simulation duration, it may be determined by N. Illustratively, the product between N and the first time length is equivalent to the simulation duration, i.e., n×Δt=td.

203. When the current time reaches the second starting time, acquiring first initial simulation data of the target simulation road at the second starting time, wherein the first initial simulation data is determined by real-time running data of the second starting time, the real-time running data represents vehicle running data acquired on the target road in real time, and the target road corresponds to the target simulation road.

In this example, real-time travel data on the target simulated road may be collected if the current time of the system reaches the second start time. That is, in the case where the current time of the system reaches the second start time, the vehicle running data at the second start time may be collected in real time from the target road, and then the vehicle running data collected in real time may be used as the first initial simulation data of the target simulation road at the second start time.

It should be noted that, how to collect the first initial simulation data at the second start time may be considered from the case of sensing the coverage of the device in the road network. The process of acquiring the second initial simulation data in the case of full coverage or non-full coverage as described in the foregoing step 202 may be specifically understood, and will not be described herein.

204. According to the first initial simulation data and the first historical driving data, simulation results of the nth simulation deduction are generated, the simulation results of the nth simulation deduction are used for indicating vehicle driving data collected from a target simulation road in a first target time period during the nth simulation deduction, the first historical driving data are used for indicating vehicle driving data collected from the target road in the first target time period on a historical period, and the first target time period is obtained from a second starting time and a simulation duration period.

In this example, a historical OD matrix may also be generated by the historical perceived trajectory prior to formal operation of the vehicle simulation system. That is, by representing the history travel data by the history OD matrix and further inputting the history travel data as a part of the simulation deduction, it is possible to directly determine on which road section the target simulation vehicle travels. For example, in a high speed trunk scenario, there is typically only a single path between the start and stop points, i.e., if 100 vehicles travel from point a to point B in a period of 8:00-8:05, their path is determined, and the road segment taken is also determined. Therefore, in the process of performing the nth simulation deduction, it is necessary to acquire the history running data in the same period of time in addition to the first initial simulation data.

The first target time period is calculated based on the second starting time and the simulation duration, and the historical driving data in the first target time period on the historical period is obtained from the storage medium, so that the first historical driving data required by the Nth simulation deduction is obtained. For example, taking the second starting time t2=8:20 as an example, in the case of the simulation duration td=30 minutes, the calculated first target time period is [ T2, t2+td ] = [8:20,8:50], thereby obtaining [8:20,8:50] the first historical driving data in the time period from the historical period. It should be noted that, the history period described herein may be understood with reference to the foregoing description of step 203, which is not repeated herein.

Thus, after the first initial simulation data and the first historical driving data are obtained, the simulation deduction can be performed based on the first initial simulation data and the first historical driving data so as to generate a simulation result of the Nth simulation deduction. For example, the simulation result of the 2 nd simulation deduction can be determined by performing the simulation deduction based on the first initial simulation data at the second start time t2=8:20 and the first historical driving data in the [8:20,8:50] time period, that is, the driving data of the vehicle at the 8:50 can be predicted from the 8:20.

In some alternative ways, for how to perform the nth simulation deduction, it may first determine, according to the first historical driving data, a target simulation vehicle that needs to be added on the target simulation road in the duration of the nth simulation deduction. Further, updating first initial simulation data of the target simulation vehicle based on a preset mesoscopic simulation deduction model, and performing simulation deduction to generate a simulation result of the Nth simulation deduction. As a schematic description, in particular, under the condition that the nth simulation pushing proceeds to the ending time of the nth simulation pushing, a mesoscopic driving track of the target simulation vehicle driving in the first target time period may be saved, and further, a simulation result of the nth simulation pushing may be generated according to the mesoscopic driving track.

For example, taking n=2 as an example, the 2 nd simulation deduction is performed from the second starting time t2=8:20, if the 2 nd simulation deduction is performed to the ending time (e.g. te2=8:21) of the 2 nd simulation deduction, at this time, a mesoscopic driving track of the target simulation vehicle generated based on the preset mesoscopic simulation deduction model during the first target time period (e.g. 8:20, 8:50) may be saved, so that the simulation result of the 2 nd simulation deduction is generated based on the mesoscopic driving track during the [8:20,8:50 ].

As a schematic description, the simulation result of how to generate the nth simulation deduction based on the mesoscopic driving track may also be implemented in the following manner, that is:

a plurality of simulation time periods are extracted from the first target time period, and road segment density and blocking density in each simulation time period on the target simulation road are determined based on the mesoscopic driving track. For example, the number of vehicles per kilometer on the target simulated road during each simulation period may be counted, and the road segment density (e.g., represented using K (T)) may be determined based on the number of vehicles per kilometer during each simulation period. In addition, the blocking density may also be calculated by collecting vehicle travel data prior to the simulation deduction (e.g., using K _iam Representation).

And on the basis of determining the blocking density, determining the lower limit of the vehicle speed and the expected free-running speed corresponding to the target simulation vehicle under the blocking density. For example, the lower vehicle speed limit and the desired free-running speed corresponding to the blocking density may be acquired by a speed sensing device or the like in the target simulation vehicle. The desired speed of travel described can be understood as the speed at which free travel is possible. Subsequently, the actual running speed of the target simulated vehicle in the running direction is calculated based on the road section density, the blocking density, the lower limit of the vehicle speed, and the desired free running speed within each simulation period, i.e., V (T) =v _min +（V _max -V _min ）×（1-（K(T)/K _iam ） ^α ） ^β . Wherein V is _min Representing a lower speed limit of the vehicle, V _max Represents the expected speed, K (T) represents the road section density, K _iam Representing the occlusion density, alpha, beta are adjustable coefficients.

In this way, the actual vehicle position of the target simulation vehicle is calculated based on the first initial simulation data and the actual running speed. For example, an initial position of the target simulation vehicle is determined from the first initial simulation data, and a time difference between the current time and the second start time is calculated, so that an actual vehicle position of the current target simulation vehicle is calculated based on the time difference and the actual running speed based on the initial position of the target simulation vehicle.

In this way, after the actual running speeds and the corresponding actual vehicle positions of all the target simulation vehicles are calculated in a similar manner, the actual running speeds and the actual vehicle positions of all the target simulation vehicles are collected, so that the simulation result of the nth simulation deduction can be generated.

In other alternative examples, in the manner of performing the simulation deduction according to the first initial simulation data and the first historical driving data and generating the simulation result of the nth simulation deduction, the following manner may be also referred to for understanding, namely: and determining a target vehicle passing through the target road in a first target time period on the history period according to the first history driving data, and further adding a target simulation vehicle to the target simulation road according to the dynamic shunt proportion based on the first initial simulation data and the target vehicle, and performing simulation deduction. It should be noted that the target simulation vehicle is described in relation to a target vehicle. Thus, when the nth simulation deduction progresses to the ending time of the nth simulation deduction, a simulation result of the nth simulation deduction is generated.

For the dynamic split ratio mentioned above, it can be used to indicate the driving flow direction duty of the target simulation vehicle on the target simulation road. As an exemplary illustration, the determination of the dynamic split ratio can be considered in terms of:

mode 1: and acquiring the number of vehicles at each road exit on the target road in the first target time period from the first initial simulation data, and further calculating the number ratio of the number of vehicles at each road exit, thereby obtaining the dynamic shunt proportion. For example, assume that the total number of vehicles traveling on the target road is 100, and that there are 3 road exits on the target road, such as turn 1, turn 2, and main road exit. If the number of vehicles exiting from the turn mouth 1 is 20, the number of vehicles exiting from the turn mouth 2 is 30, and the number of vehicles continuing to travel on the main road from the main road exit is 50. Thus, the calculated dynamic split ratio is 2:3:5.

Mode 2: the number of each lane on the target road in the first target time period can be counted first, and then the proportion among the number of each lane is calculated, so that the dynamic diversion proportion is obtained. For example, it is assumed that the total number of lanes on the current target road is 4, and the number of lanes for straight running is 2, the number of lanes for right running is 1, and the number of lanes for left running is 1. The dynamic split ratio thus calculated is 2:1:1.

It should be noted that, for determining how the dynamic splitting ratio is determined, besides the above-mentioned modes 1 and 2, in practical application, the dynamic splitting ratio may also be obtained by collecting and processing by a sensing device (such as a camera) arranged at a key point, and the embodiment of the present application is not limited.

According to the embodiment of the application, the target time information required to be subjected to simulation deduction is preset, the starting time (namely the second starting time) of the nth simulation deduction is determined through the first duration and the starting time (namely the first starting time) of the first simulation deduction, and on the basis that the real-time running data of the starting time of the nth simulation deduction is taken as the initial simulation data of the nth simulation deduction, the historical running data in the same first target period are comprehensively considered to complete the nth simulation deduction, so that the whole simulation deduction flow is described, and the short-term prediction of the road traffic in the first target period after the second starting time is realized. In addition, the application also can describe the updating process of the simulation result in the process of continuously carrying out the simulation deduction by configuring the first time length in the vehicle simulation system, so that the simulation result of every two adjacent simulation deductions is not required to be frequently displayed to a user, and the latest simulation result of the simulation deduction is updated and displayed after the first time length, so that the real-time twin is upgraded from 'visualization' to a computable digital space, the decision is assisted by prediction, the virtual world and the physical world are connected, the purpose of closed-loop control is finally realized, the simulation efficiency of road traffic is improved, and the occupation of calculation resources is reduced.

Optionally, in other embodiments, after performing step 204, the following steps 205 to 207 may also be performed, namely:

205. and carrying out quantization processing on the simulation result deduced by the Nth simulation to obtain road network evaluation index information.

In this example, after the simulation result of the nth simulation deduction is generated, the simulation result of the nth simulation deduction may be further quantitatively processed to obtain corresponding road network evaluation index information, for example, but not limited to, information such as average speed, traffic flow, density, etc. of a road section or a section.

206. Generating the road network thermodynamic diagram deduced by the Nth simulation based on the road network evaluation index information.

207. And displaying the road network thermodynamic diagram deduced by the Nth simulation.

In this example, after the road network evaluation index information is obtained, a corresponding road network thermodynamic diagram of the nth simulation deduction is generated based on the road network evaluation index information, and then the road network thermodynamic diagram of the nth simulation deduction is displayed at the front end when the current time reaches the end time of the nth simulation deduction.

For example, taking the target time information shown in fig. 4 as an example, if the steps 201 to 204 are executed to generate the simulation result of the primary simulation deduction, that is, the road traffic state of 8:45 can be predicted from 8:15, at this time, the road network thermodynamic diagram corresponding to the simulation result of the primary simulation deduction can be displayed at the front end when the ending time of the primary simulation deduction is, for example, 8:16.

Then, after Δt=5 minutes, the above steps 201 to 204 are performed to generate the simulation result of the 2 nd simulation deduction, that is, the road traffic state of 8:50 can be predicted from 8:20, where the road network thermodynamic diagram corresponding to the simulation result of the 2 nd simulation deduction may be displayed at the front end at the end time of the 2 nd simulation deduction, for example, 8:21, and may be understood by referring to fig. 6, which is a schematic diagram showing the road network thermodynamic diagram provided by the embodiment of the present application. As shown in fig. 6, it is possible to check from the road network thermodynamic diagram which road segments the vehicle is in a creep state, a congestion state, or an unblocked state, etc. More specifically, in the front-end display, the road network thermodynamic diagram corresponding to the simulation result of the primary simulation deduction is updated to the road network thermodynamic diagram corresponding to the simulation result of the 2 nd simulation deduction.

By displaying the road network thermodynamic diagram, the road traffic state in a future period can be clearly and intuitively checked from the road network thermodynamic diagram, management and control and intervention of road traffic in advance are facilitated for a manager, and a user can conveniently select an optimal path and the like.

The foregoing description of the solution provided by the embodiments of the present application has been mainly presented in terms of a method. It should be understood that, in order to implement the above-described functions, hardware structures and/or software modules corresponding to the respective functions are included. Those of skill in the art will readily appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The embodiment of the application can divide the functional modules of the device according to the method example, for example, each functional module can be divided corresponding to each function, and two or more functions can be integrated in one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that, in the embodiment of the present application, the division of the modules is schematic, which is merely a logic function division, and other division manners may be implemented in actual implementation.

The following describes the traffic simulation device in the embodiment of the present application in detail, and fig. 7 is a schematic diagram of a functional module of the traffic simulation device provided in the embodiment of the present application. As shown in fig. 7, the traffic simulation device may include a configuration unit 701, an acquisition unit 702, and a processing unit 703.

The configuration unit 701 is configured to pre-configure target time information required to perform simulation deduction in the vehicle simulation system, where the target time information includes a simulation duration, a first duration, and a first start time of initial simulation deduction, the target time information corresponds to a target simulation road required to perform simulation deduction, the simulation duration is used to indicate a duration when the simulation deduction is continuously performed, and the first duration is used to indicate an interval time when each two adjacent simulation deductions are performed. It is specifically understood that the foregoing description of step 201 in fig. 2 is referred to, and details are not repeated herein.

The processing unit 703 is configured to calculate a second start time based on the first start time and the first time, where the second start time is a predicted start time when the nth simulation deduction is performed, N is greater than or equal to 2, and N is an integer. It is specifically understood that the foregoing description of step 202 in fig. 2 is referred to, and details are not repeated herein.

The obtaining unit 702 is configured to obtain, when the current time reaches the second start time, first initial simulation data of the target simulation road at the second start time, where the first initial simulation data is determined by real-time driving data of the second start time, and the real-time driving data represents vehicle driving data collected in real time on the target road, where the target road corresponds to the target simulation road. It is specifically understood that the foregoing description of step 203 in fig. 2 is referred to, and details are not repeated herein.

The processing unit 703 is configured to perform a simulation deduction according to the first initial simulation data and the first historical driving data, generate a simulation result of the nth simulation deduction, where the simulation result of the nth simulation deduction is used to indicate vehicle driving data collected for a target simulation road in a first target period during the nth simulation deduction, and the first historical driving data is used to indicate vehicle driving data collected for the target road in the first target period over a historical period, and the first target period is obtained from the second starting time and the simulation duration. It is specifically understood that the foregoing description of step 204 in fig. 2 is referred to, and details are not repeated herein.

In some alternative examples, the processing unit 703 is further configured to: after simulation deduction is carried out according to the first initial simulation data and the first historical driving data to generate a simulation result of the nth simulation deduction, quantization processing is carried out on the simulation result of the nth simulation deduction to obtain road network evaluation index information; generating a road network thermodynamic diagram deduced by the Nth simulation based on the road network evaluation index information; and displaying the road network thermodynamic diagram deduced by the Nth simulation.

In other alternative examples, the processing unit 703 is configured to: determining a target simulation vehicle which needs to be added on a target simulation road in the continuous process of the Nth simulation deduction according to the first historical driving data; updating first initial simulation data of the target simulation vehicle based on a preset mesoscopic simulation deduction model, and performing simulation deduction to generate a simulation result of the Nth simulation deduction.

In other alternative examples, the processing unit 703 is configured to: when the Nth simulation pushing is carried out to the end time of the Nth simulation pushing, a mesoscopic driving track of the target simulation vehicle driving in a first target time period is saved; and generating a simulation result of the Nth simulation deduction according to the mesoscopic driving track.

In other alternative examples, the processing unit 703 is configured to: determining road section density and blocking density in each simulation period on a target simulation road based on the mesoscopic driving track, wherein the first target period comprises a plurality of simulation periods; determining a lower vehicle speed limit and an expected free-running speed corresponding to the target simulation vehicle under the blocking density; calculating an actual running speed of the target simulation vehicle in the running direction based on the road section density and the blocking density in each simulation period, the lower limit of the vehicle speed and the expected free running speed; calculating an actual vehicle position of the target simulation vehicle based on the first initial simulation data and the actual running speed; and collecting the actual running speeds and the actual vehicle positions of all the target simulation vehicles to obtain the simulation result deduced by the Nth simulation.

In other alternative examples, the processing unit 703 is configured to: determining a target vehicle passing through a target road in a first target time period on a history period according to the first history driving data; adding a target simulation vehicle to the target simulation road according to a dynamic shunt proportion based on the first initial simulation data and the target vehicle, and performing simulation deduction, wherein the target simulation vehicle is related to the target vehicle, and the dynamic shunt proportion is used for indicating the driving flow direction occupation ratio condition of the target simulation vehicle on the target simulation road; and generating a simulation result of the Nth simulation deduction when the Nth simulation deduction progresses to the ending time of the Nth simulation deduction.

In other alternative examples, the processing unit 703 is further configured to: before adding target simulation vehicles to the target simulation road according to the dynamic shunt proportion based on the first initial simulation data and the target vehicles, acquiring the number of vehicles at each road exit on the target road in a first target time period from the first initial simulation data; and calculating the number ratio of the number of vehicles at each road outlet to obtain the dynamic shunt proportion.

In other alternative examples, the processing unit 703 is further configured to: before adding the target simulation vehicles to the target simulation road according to the dynamic diversion proportion based on the first initial simulation data and the target vehicles, counting the number of each lane on the target road in a first target time period; and calculating the proportion among the number of each lane to obtain the dynamic shunt proportion.

In other alternative examples, the obtaining unit 702 is further configured to: and before simulation deduction is carried out according to the first initial simulation data and the first historical driving data and a simulation result of the Nth simulation deduction is generated, when the current moment reaches the first starting moment, acquiring second initial simulation data of the target simulation road at the first starting moment, wherein the second initial simulation data is determined by the real-time driving data at the first starting moment. The processing unit 703 is configured to perform a simulation deduction according to the second initial simulation data and the second historical driving data, generate a simulation result of the initial simulation deduction, where the simulation result of the initial simulation deduction is used to indicate vehicle driving data collected for a target simulation road in a second target time period during the initial simulation deduction, and the second historical driving data is used to indicate vehicle driving data collected for the target road in the second target time period over a historical period, where the second target time period is obtained from the first starting time and the simulation duration.

In other optional examples, the target time information further includes an end time of the initial simulation deduction; the processing unit 703 is configured to: determining a target vehicle passing through a target road in a second target time period on the history period according to the second history driving data; adding a target simulation vehicle to the target simulation road according to a dynamic shunt proportion based on the second initial simulation data and the target vehicle, and performing simulation deduction, wherein the target simulation vehicle is related to the target vehicle, and the dynamic shunt proportion is used for indicating the driving flow direction occupation ratio condition of the target simulation vehicle on the target simulation road; and generating a simulation result of the primary simulation deduction when the primary simulation deduction is carried out to the ending time of the primary simulation deduction.

The traffic simulation device in the embodiment of the present application is described above from the point of view of the modularized functional entity, and the traffic simulation device in the embodiment of the present application is described below from the point of view of hardware processing. Fig. 8 is a schematic structural diagram of a traffic simulation device according to an embodiment of the present application. The traffic simulation device may vary considerably in configuration or performance, including but not limited to the traffic simulation apparatus described in fig. 7. The traffic simulation device may include at least one processor 801, communication circuitry 807, memory 803, and at least one communication interface 804.

The processor 801 may be a general purpose central processing unit (central processing unit, CPU), microprocessor, application-specific integrated circuit (server IC), or one or more integrated circuits for controlling the execution of programs in accordance with aspects of the present application.

Communication line 807 may include a pathway to transfer information between the aforementioned components.

Communication interface 804, using any transceiver-like device for communicating with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), etc.

The memory 803 may be a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that may store information and instructions, and the memory may be stand-alone and coupled to the processor via a communication line 807. The memory may also be integrated with the processor.

The memory 803 is used for storing computer-executable instructions for performing the aspects of the present application, and is controlled by the processor 801 for execution. The processor 801 is configured to execute computer-executable instructions stored in the memory 803, thereby implementing the road traffic simulation method provided in the above embodiment of the present application.

Alternatively, the computer-executable instructions in the embodiments of the present application may be referred to as application program codes, which are not particularly limited in the embodiments of the present application.

In a specific implementation, as an embodiment, the traffic simulation device may include multiple processors, such as processor 801 and processor 802 in fig. 8. Each of these processors may be a single-core (single-CPU) processor or may be a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).

In a specific implementation, as an embodiment, the traffic simulation device may further include an output device 805 and an input device 806. An output device 805 communicates with the processor 801 and can display information in a variety of ways. The input device 806 is in communication with the processor 801 and may receive input of a target object in a variety of ways. For example, the input device 806 may be a mouse, a touch screen device, a sensing device, or the like.

The traffic simulation device described above may be a general-purpose device or a special-purpose device. In a specific implementation, the traffic simulation device may be a server, a terminal, etc. or a device having a similar structure in fig. 8. The embodiment of the application is not limited to the type of the traffic simulation equipment.

It should be noted that, the processor 801 in fig. 8 may cause the traffic simulation device to execute the method in the method embodiment corresponding to fig. 2 by calling the computer execution instruction stored in the memory 803.

Specifically, the functions/implementation procedures of the configuration unit 701 and the processing unit 703 in fig. 7 may be implemented by the processor 801 in fig. 8 calling computer-executable instructions stored in the memory 803. The functions/implementation of the acquisition unit 702 in fig. 7 may be implemented by the communication interface 804 in fig. 8.

The embodiment of the present application also provides a computer storage medium storing a computer program for electronic data exchange, where the computer program causes a computer to execute part or all of the steps of any one of the road traffic simulation methods described in the above method embodiments.

Embodiments of the present application also provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform part or all of the steps of any one of the road traffic simulation methods described in the method embodiments above.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

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, or other various media capable of storing program codes.

The above-described embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof, and when implemented in software, may be implemented in whole or in part in the form of a computer program product.

The computer program product includes one or more computer instructions. When the computer-executable instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are fully or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). Computer readable storage media can be any available media that can be stored by a computer or data storage devices such as servers, data centers, etc. that contain an integration of one or more available media. Usable media may be magnetic media (e.g., floppy disks, hard disks, magnetic tape), optical media (e.g., DVD), or semiconductor media (e.g., SSD)), or the like.

The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A simulation method of road traffic, comprising:

pre-configuring target time information required to be subjected to simulation deduction in a vehicle simulation system, wherein the target time information comprises a simulation duration time, a first time length and a first starting time of initial simulation deduction, the target time information corresponds to a target simulation road required to be subjected to the simulation deduction, the simulation duration time is used for indicating a time period when vehicle driving data in a preset future time length are predicted, and the first time length is used for indicating an interval time when the simulation deduction is executed every two adjacent times;

calculating a second starting time based on the first starting time and the first time, wherein the second starting time is a predicted starting time when the N-th simulation deduction is performed, N is more than or equal to 2, and N is an integer;

When the current moment reaches the second starting moment, acquiring first initial simulation data of the target simulation road at the second starting moment, wherein the first initial simulation data are determined by real-time driving data of the second starting moment, the real-time driving data represent vehicle driving data acquired on the target road in real time, and the target road corresponds to the target simulation road;

and carrying out the simulation deduction according to the first initial simulation data and the first historical driving data, and generating a simulation result of the Nth simulation deduction, wherein the simulation result of the Nth simulation deduction is used for indicating vehicle driving data collected from a target simulation road in a first target time period during the Nth simulation deduction, the first historical driving data is used for indicating vehicle driving data collected from the target road in the first target time period on a historical period, and the first target time period is obtained from the second starting time and the simulation duration period.

2. The method of claim 1, wherein after generating the simulation result of the nth simulation deduction from the first initial simulation data and first historical driving data, the method further comprises:

Performing quantization processing on the simulation result deduced by the Nth simulation to obtain road network evaluation index information;

generating a road network thermodynamic diagram deduced by the Nth simulation based on the road network evaluation index information;

and displaying the road network thermodynamic diagram deduced by the Nth simulation.

3. The method according to any one of claims 1 to 2, wherein performing the simulation deduction from the first initial simulation data and first historical driving data, generating a simulation result of the nth simulation deduction, comprises:

determining a target simulation vehicle which needs to be added on the target simulation road in the continuous process of the Nth simulation deduction according to the first historical driving data;

updating the first initial simulation data of the target simulation vehicle based on a preset mesoscopic simulation deduction model, and carrying out the simulation deduction to generate a simulation result of the Nth simulation deduction.

4. The method of claim 3, wherein updating the first initial simulation data of the target simulation vehicle based on a preset mesoscopic simulation deduction model and performing the simulation deduction to generate a simulation result of the nth simulation deduction comprises:

When the Nth simulation deduction progress to the ending time of the Nth simulation deduction, a mesoscopic driving track of the target simulation vehicle driving in the first target time period is saved;

and generating a simulation result of the Nth simulation deduction according to the mesoscopic driving track.

5. The method of claim 4, wherein generating the simulation result of the nth simulation deduction from the mesoscopic driving trajectory comprises:

determining road section density and blocking density in each simulation period on the target simulation road based on the mesoscopic driving track, wherein the first target period comprises a plurality of simulation periods;

determining a lower vehicle speed limit and an expected free-running speed corresponding to the target simulation vehicle under the blocking density;

calculating an actual running speed of the target simulation vehicle in a running direction based on the link density and the blocking density, the lower vehicle speed limit, and the desired free running speed within each of the simulation periods;

calculating an actual vehicle position of the target simulation vehicle based on the first initial simulation data and the actual traveling speed;

and collecting the actual running speeds and the actual vehicle positions of all the target simulation vehicles to obtain simulation results of the Nth simulation deduction.

6. The method according to any one of claims 1 to 2, wherein performing the simulation deduction from the first initial simulation data and first historical driving data, generating a simulation result of the nth simulation deduction, comprises:

determining a target vehicle passing through the target road in a first target time period on the history period according to the first history driving data;

adding a target simulation vehicle to the target simulation road according to a dynamic shunt proportion based on the first initial simulation data and the target vehicle, and performing simulation deduction, wherein the target simulation vehicle is related to the target vehicle, and the dynamic shunt proportion is used for indicating the driving flow direction occupation ratio condition of the target simulation vehicle on the target simulation road;

and generating a simulation result of the Nth simulation deduction when the Nth simulation deduction is carried out to the ending time of the Nth simulation deduction.

7. The method of claim 6, wherein prior to adding a target simulation vehicle to the target simulation road in a dynamic split ratio based on the first initial simulation data and the target vehicle, the method further comprises:

Acquiring the number of vehicles at each road exit on the target road in the first target time period from the first initial simulation data;

and calculating the number ratio of the number of vehicles at each road outlet to obtain the dynamic shunt proportion.

8. The method of claim 6, wherein prior to adding a target simulation vehicle to the target simulation road in a dynamic split ratio based on the first initial simulation data and the target vehicle, the method further comprises:

counting the number of each lane on the target road in the first target time period;

and calculating the proportion among the number of each lane to obtain the dynamic diversion proportion.

9. The method according to any of claims 1 to 2, wherein the product between N and the first time period is equal to the simulation duration.

10. The method according to any one of claims 1 to 2, wherein the simulation deduction is performed according to the first initial simulation data and first historical driving data, and before generating the simulation result of the nth simulation deduction, the method further comprises:

When the current time reaches the first starting time, acquiring second initial simulation data of the target simulation road at the first starting time, wherein the second initial simulation data is determined by real-time driving data of the first starting time;

and carrying out the simulation deduction according to the second initial simulation data and second historical driving data, and generating a simulation result of the initial simulation deduction, wherein the simulation result of the initial simulation deduction is used for indicating vehicle driving data collected from a target simulation road in a second target time period during the initial simulation deduction, the second historical driving data is used for indicating vehicle driving data collected from the target road in the second target time period during the historical period, and the second target time period is obtained from the first starting time and the simulation duration period.

11. The method according to any one of claims 1 to 2, wherein the target time information further includes an end time of the initial simulation deduction; the step of performing the simulation deduction according to the second initial simulation data and the second historical driving data to generate a simulation result of the initial simulation deduction includes:

Determining a target vehicle passing through the target road in a second target time period on the history period according to the second history driving data;

adding a target simulation vehicle to the target simulation road according to a dynamic shunt proportion based on the second initial simulation data and the target vehicle, and performing simulation deduction, wherein the target simulation vehicle is related to the target vehicle, and the dynamic shunt proportion is used for indicating the driving flow direction occupation ratio condition of the target simulation vehicle on the target simulation road;

and generating a simulation result of the primary simulation deduction when the primary simulation deduction progresses to the ending time of the primary simulation deduction.

12. A traffic simulation device, comprising:

the configuration unit is used for pre-configuring target time information required to carry out simulation deduction in a vehicle simulation system, wherein the target time information comprises a simulation duration time, a first duration time and a first starting time of initial simulation deduction, the target time information corresponds to a target simulation road required to carry out the simulation deduction, the simulation duration time is used for indicating the duration time when the simulation deduction is continuously executed, and the first duration time is used for indicating the interval time when the simulation deduction is executed every two adjacent times;

The processing unit is used for calculating a second starting time based on the first starting time and the first time, wherein the second starting time is a predicted starting time when the simulation deduction is carried out for the Nth time, N is more than or equal to 2, and N is an integer;

the acquisition unit is used for acquiring first initial simulation data of the target simulation road at the second starting time when the current time reaches the second starting time, wherein the first initial simulation data is determined by real-time driving data of the second starting time, the real-time driving data represents vehicle driving data acquired on the target road in real time, and the target road corresponds to the target simulation road;

the processing unit is configured to perform the simulation deduction according to the first initial simulation data and first historical driving data, generate a simulation result of the nth simulation deduction, and use the simulation result of the nth simulation deduction to indicate vehicle driving data collected from a target simulation road in a first target time period during the nth simulation deduction, where the first historical driving data is used to indicate vehicle driving data collected from the target road in the first target time period on a historical period, and the first target time period is obtained from the second starting time and the simulation duration.

13. A traffic simulation device, comprising: an input/output interface, a processor, and a memory, the memory having program instructions stored therein;

the processor is configured to execute program instructions stored in a memory to perform the method of any one of claims 1 to 11.

14. A computer readable storage medium comprising instructions which, when run on a computer device, cause the computer device to perform the method of any of claims 1 to 11.

15. A computer program product comprising instructions which, when run on a computer device, cause the computer device to perform the method of any of claims 1 to 11.