CN115016463A - Vehicle control method and device - Google Patents
Vehicle control method and device Download PDFInfo
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Abstract
The embodiment of the application discloses a vehicle control method and a device, and the main technical scheme comprises the following steps: acquiring the position information of the vehicle at the current moment; obtaining obstacle information around the vehicle at the current time from a spatial database, wherein the spatial database is maintained by map data and obstacle data obtained by a vehicle networking system; performing local path planning on the vehicle based on the position information of the vehicle at the current moment and the obstacle information; and sending control information to the vehicle based on the result of the local path planning. This application can reduce vehicle repacking cost, improves the security.
Description
Technical Field
The present application relates to the field of automatic driving technologies, and in particular, to a vehicle control method and apparatus.
Background
With the development of autopilot technology, more and more scenes have autopilot requirements, such as public transportation, ports, enclosed parks, unmanned environments, main logistics, and the like. The existing automatic driving technology is generally a single-vehicle intelligent scheme, namely, road and obstacle information is detected and identified through a vehicle-mounted sensor, so that path planning is carried out to control vehicles. However, a large number of sensors need to be arranged in the single-vehicle intelligent scheme, the refitting cost is high, and the vehicle-mounted sensors are easily influenced by angles, building shielding and other factors, so that enough environmental information cannot be acquired, and the safety is influenced.
Disclosure of Invention
In view of this, the present application provides a vehicle control method so as to reduce the refitting cost of the vehicle and improve the safety.
The application provides the following scheme:
according to a first aspect, there is provided a vehicle control method including:
acquiring the position information of the vehicle at the current moment;
obtaining obstacle information around the vehicle at the current time from a spatial database, wherein the spatial database is maintained by map data and obstacle data obtained by a vehicle networking system;
performing local path planning on the vehicle based on the position information of the vehicle at the current moment and the obstacle information;
and sending control information to the vehicle based on the result of the local path planning.
According to an implementation manner in the embodiment of the present application, the method is periodically executed or executed after the position information reported by the vehicle is acquired.
According to an implementable manner in an embodiment of the present application, the method further comprises:
acquiring barrier data reported by a vehicle-mounted sensor and/or roadside sensing equipment;
and storing the obstacle data, the reporting time of the obstacle data and the map data in a spatial database in an associated manner.
According to an implementation manner in the embodiment of the present application, the obtaining of the obstacle information around the vehicle at the current time from the spatial database includes:
inquiring barrier data of the current time from the spatial database according to the current time;
and inquiring the obstacle information mapped on the map within a preset range from the position information of the vehicle at the current moment from the obstacle data at the current moment.
According to an implementation manner in the embodiment of the present application, performing local path planning on the vehicle based on the position information of the vehicle at the current time and the obstacle information includes:
acquiring the speed information of the vehicle at the current moment;
determining a speed search space of the vehicle based on speed information and position information of the vehicle at the current moment;
carrying out speed sampling in the speed search space to obtain more than one speed sampling point;
performing local track simulation based on each speed sampling point to obtain a simulation track corresponding to each speed sampling point;
and evaluating the simulated track corresponding to each speed sampling point based on a preset evaluation function, and determining the simulated track with the highest evaluation value as a local path planning result.
According to an implementation manner in the embodiment of the present application, the sending of the control information to the vehicle based on the result of the local path planning includes:
determining the speed sampling point corresponding to the simulation track with the highest evaluation as target speed information;
transmitting control information including the target speed information to the vehicle.
According to an implementation manner in the embodiment of the present application, the determining the speed search space of the vehicle based on the speed information and the position information of the vehicle at the current time includes:
determining a first speed search space of the vehicle according to the speed parameter information of the vehicle;
determining a second speed search space enabling the vehicle not to collide according to the acceleration parameter information of the vehicle and the closest distance between the vehicle and the obstacle at the current moment;
determining a speed range which can be reached by the vehicle within a preset interval duration as a third speed search space according to the actual speed and a preset acceleration range of the vehicle at the current moment;
and determining the intersection of the first speed search space, the second speed search space and the third speed search space as the speed search space of the vehicle.
According to an implementation manner in the embodiment of the present application, the local trajectory simulation is performed based on each speed sampling point, and obtaining a simulation trajectory corresponding to each speed sampling point includes:
and respectively taking the speed sampling points as the speed of the vehicle at the next moment, and predicting the running track of the vehicle in a preset simulation duration as the simulation track corresponding to the speed sampling points.
According to an implementation manner in the embodiment of the present application, the evaluating the analog trajectory corresponding to each speed sampling point based on the preset evaluation function includes:
determining each evaluation index value of the simulation track corresponding to the speed sampling point;
weighting each evaluation index value to obtain an evaluation value of the simulated track corresponding to the speed sampling point;
the evaluation index comprises one or any combination of the distance between the simulated track and the obstacle, the running speed of the vehicle on the simulated track, the included angle between the running direction of the vehicle and the destination on the simulated track and the distance between the simulated track and the lane edge line.
According to an implementable manner of the embodiments of the present application, the speeds include linear and angular speeds.
According to a second aspect, there is provided a vehicle control apparatus comprising:
a first acquisition unit configured to acquire position information of a vehicle at a current time;
a second acquisition unit configured to acquire obstacle information around the vehicle at a current time from a spatial database maintained by map data and obstacle data acquired by a vehicle networking system;
a local planning unit configured to perform local path planning on the vehicle based on the position information of the vehicle at the current time and the obstacle information;
a result transmitting unit configured to transmit control information to the vehicle based on a result of the local path plan.
According to a third aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the method of any of the first aspects described above.
According to a fourth aspect, there is provided an electronic device, comprising:
one or more processors; and
a memory associated with the one or more processors for storing program instructions that, when read and executed by the one or more processors, perform the steps of the method of any of the first aspects described above.
According to the specific embodiments provided by the application, the application discloses the following technical effects:
1) the method and the system for controlling the vehicle based on the vehicle networking system acquire the obstacle information around the vehicle and perform local path planning based on the map data acquired by the vehicle networking system and the space database maintained by the obstacle data, so that the vehicle is controlled. On one hand, a large number of sensors do not need to be installed on the vehicle, so that the modification cost is reduced; on the other hand, the obstacle data obtained by the vehicle networking system is richer and more reliable, and the defect of low safety caused by the problems of illumination, view shielding and the like of road environment and obstacle information obtained by a vehicle sensor in a single vehicle intelligent scheme is overcome.
2) The obstacle data and the reporting time reported by the vehicle-mounted sensor and/or the roadside sensing equipment in the vehicle networking system are associated with the map data and stored in the spatial database, so that the obstacle data near any position can be rapidly inquired according to the time when the spatial database is inquired, and the vehicle control has the characteristics of multiple visual fields and beyond visual range.
3) The distance between the evaluation index of the simulation track and the lane edge line is introduced in the local path planning, so that the local path is more reasonable, and the safety is higher.
Of course, it is not necessary for any product to achieve all of the above-described advantages at the same time for practicing the present application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 illustrates an exemplary system architecture diagram to which embodiments of the present application may be applied;
FIG. 2 is a flow chart of a vehicle control method provided by an embodiment of the present application;
fig. 3 is a flowchart of a method for local path planning according to an embodiment of the present application;
FIG. 4 is a comparative schematic diagram of a partial path provided by an embodiment of the present application;
FIG. 5 shows a schematic block diagram of the vehicle control apparatus according to one embodiment;
fig. 6 is an architecture diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application are within the scope of protection of the present application.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter associated objects are in an "or" relationship.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
In order to facilitate understanding of the embodiments of the present application, first, a brief description is given of a system architecture on which the embodiments of the present application are based. Fig. 1 shows an exemplary system architecture to which an embodiment of the present application may be applied, and as shown in fig. 1, the system architecture mainly includes a vehicle control server, a vehicle with a vehicle-mounted terminal system installed, and a spatial database, and further may further include a roadside sensing device and a map database.
The vehicle is a vehicle having a vehicle-mounted terminal system that runs on a road, and may be an unmanned vehicle or a driving assistance vehicle. The vehicle type may be various forms of bus, truck, taxi, logistics car, etc., and is applied to, but not limited to, application scenarios such as public transportation, port, closed park, unmanned environment, main logistics, etc., which is not particularly limited in this application.
The vehicle-mounted terminal system is a system installed in a vehicle, and can perform functions such as information interaction, control of devices in the vehicle and the like, and also can realize functions such as high-precision positioning, execution of control strategies and the like. The vehicle-mounted terminal system can report the position information of the vehicle to the vehicle control server periodically or based on a specific event, receive the control information sent by the vehicle control server, and control the vehicle based on the control information.
The vehicle control server can interact with a vehicle-mounted terminal system in the vehicle, and obtains obstacle data from a spatial database and map data from a map database. The method provided by the embodiment of the application generates the control information for the vehicle and sends the control information to the corresponding vehicle.
The vehicle control server may be a single server or a server cluster composed of a plurality of servers, and the server may be a cloud server. The cloud Server is also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of large management difficulty and weak service expansibility in the conventional physical host and virtual Private Server (VPs) service.
The spatial database is maintained by map data and obstacle data acquired by the internet of vehicles system. The vehicle networking system can be a system in which vehicles, roadside sensing equipment, traffic system equipment and the like are interconnected through a network transmission mode.
The roadside sensing device is mainly arranged at the roadside and used for sensing road condition information and information generated by the operation of traffic participants on a target road section, such as position information, speed information, shape information and the like of vehicles, pedestrians and the like. The roadside sensing device can comprise a camera, a millimeter wave radar, a laser radar and other sensors and a roadside edge computing node.
The map database is maintained with map data. In particular, high-precision map data is maintained in the map database in the present application. The high-precision map is also called a high-resolution map, generally composed of three types of vectors including a lane model, a road component and a road attribute containing semantic information, and is a novel map data model for automatic driving. The relative coordinate precision of the high-precision map can reach the centimeter level.
It should be understood that the number of vehicles, roadside sensing devices, vehicle control servers, etc. in fig. 1 is merely illustrative. There may be any number of vehicles, roadside sensing devices, vehicle control servers, etc., as desired for implementation.
Fig. 2 is a flowchart of a vehicle control method provided in an embodiment of the present application, which may be executed by a vehicle control server in the system architecture shown in fig. 1. As shown in fig. 2, the method may include the steps of:
step 202: and acquiring the position information of the vehicle at the current moment.
Step 204: obstacle information around the vehicle at the present time is acquired from a spatial database maintained by map data and obstacle data acquired by a vehicle networking system.
Step 206: and performing local path planning on the vehicle based on the position information and the obstacle information of the vehicle at the current moment.
Step 208: and sending control information to the vehicle based on the result of the local path planning.
It can be seen that the method and the system for controlling the vehicle acquire the obstacle information around the vehicle and perform local path planning based on the space database maintained by the map data and the obstacle data acquired by the vehicle networking system, so as to control the vehicle. On one hand, a large number of sensors do not need to be installed on the vehicle, so that the modification cost is reduced; on the other hand, the obstacle data obtained by the vehicle networking system is richer and more reliable, and the defect of low safety caused by the problems of illumination, view shielding and the like of road environment and obstacle information obtained by a vehicle sensor in a single vehicle intelligent scheme is overcome.
The steps in the above-described flow are described in detail below with reference to examples.
In step 202, the vehicle control server may acquire the location information periodically reported by the vehicle, or may periodically acquire the location information of the vehicle from the spatial database.
Accordingly, as a trigger mechanism of the above process, the vehicle control server may start to execute the above method process for the vehicle to determine the control information sent to the vehicle after receiving the location information reported by the vehicle. For example, the vehicle reports the position information of the vehicle every 100ms, which means that the vehicle control server executes the above-mentioned process every 100ms to determine the control information to be transmitted to the vehicle.
Alternatively, the vehicle control server may autonomously control the vehicle periodically, for example, every 100ms, to perform the above-described method flow for the vehicle.
The method shown in fig. 2 is described in one cycle, and if the current time is t, the position information of the vehicle at the current time is P t 。
The above step 204, i.e., "obtaining obstacle information around the vehicle at the current time from the spatial database" will be described in detail with reference to the embodiments.
The construction of the spatial database is first described. In the embodiment of the present application, the sensing device disposed at the road side (the road side is a broad concept, and may be disposed at the side of the road, or may be disposed on a street lamp, a bracket, a transportation device, etc. at the side of the road, as long as the sensing device is located at a position where the road condition and the information of the traffic participants can be conveniently collected) can collect the road condition information and the operation information of the traffic participants on the road. In the case of a vehicle, since the surrounding traffic participants are obstacles of the vehicle, it is equivalent to the roadside sensing device that can collect obstacle data. Such as position information, speed information, shape information, etc. of the vehicle, pedestrian, etc. In addition to information about the operation of the traffic participants on the road, the obstacle data can also include information such as road blocks, construction facilities, etc., which can likewise be collected by road-side sensing devices. In addition, for some vehicles provided with vehicle-mounted sensors, road condition information and obstacle information of roads can be collected through the vehicle-mounted sensors. A large amount of vehicle sensors and roadside perception equipment upload the obstacle data of gathering and upload to the server end for the road environment has been mastered comprehensively, abundantly to the server end, including obstacle data.
As an implementation mode, data collected by the roadside sensing equipment and the vehicle-mounted sensor can be periodically reported to the server side. Based on the current 5G communication network, a very small reporting period can be set, for example, reporting every 100 ms.
In addition, the server side may acquire map data, preferably high-precision map data, from a map database. The map data may be obtained from an interface or database provided by a third party map data provider, and will not be described in detail herein.
After the obstacle data and the map data are acquired, the obstacle data, the reporting time of the obstacle data and the map data can be stored in a spatial database in an associated manner.
In the spatial database, all the obstacle data are stored in association with the reporting time, so that the obstacle data can be quickly inquired by using the subsequent time. In addition, the obstacle data and the map data are stored in a correlated manner, that is, the obstacle data is mapped to the map data, so that the obstacle data and the environmental information such as roads in a certain position range can be quickly inquired. The obstacle data may include, among other things, position information, speed information, shape information, etc. of obstacles (vehicles, pedestrians, roadblocks, construction equipment, etc. that may affect the operation of the vehicle). Therefore, the vehicle control based on the spatial database has the characteristics of multiple visual fields and over-the-horizon.
The above step 206, that is, "perform local path planning on the vehicle based on the position information and the obstacle information of the vehicle at the current time" will be described in detail with reference to the embodiment.
For vehicles, global path planning is performed from a starting location to a destination, and the global path planning is performed based on map data. That is, the global route planning is to find a preferred route from the starting point to the destination according to the map data, and is a global prior planning. On the basis of global path planning, the vehicle can further carry out local path planning according to real-time local environment information to avoid obstacles, and the local path planning is mainly based on perception of the surrounding environment information of the vehicle. The embodiment of the present application does not improve the global path planning, and may adopt, for example, Dijkstra (Dijkstra) algorithm, RRT (Rapid-exploration Random Tree) algorithm, and the like, which is not described in detail herein.
The present application relates to improvements and innovations in local path planning. Taking DWA (Dynamic window approach) as an example, a specific method of local path planning may be as shown in fig. 3, and includes the following steps:
step 302: and acquiring the speed information of the vehicle at the current moment.
The speed information of each vehicle on the road can be acquired by the road side sensing device and uploaded to the server side and stored in the spatial database, or detected by other terminals (such as vehicle-mounted sensors of other vehicles, mobile phone terminals on the vehicle, and the like) in the vehicle networking system and uploaded to the server side and stored in the spatial database, so that the speed information of the vehicle at the current time can be acquired from the spatial database.
If the vehicle has a speed measuring device, the vehicle can report its speed information to the server, for example, the vehicle reports its speed information while reporting the position information. The server side can directly acquire the speed information reported by the vehicle. The speed information of the vehicle at the current moment can also be acquired in other manners, which are not listed here.
Step 304: and determining a speed search space of the vehicle based on the speed information and the position information of the vehicle at the current moment.
Specifically, a first speed search space of the vehicle may be first determined depending on the speed parameter information of the vehicle. It should be noted that the velocities referred to in the embodiments of the present application may include a linear velocity v and an angular velocity w.
The vehicle is limited by the maximum speed, the minimum speed and the like of the vehicle, and has basic speed parameters, so that a first speed search space V of the vehicle can be determined s It can be expressed as:
V s ＝{v∈[v min ,v max ],w∈[ω min ,ω max ]}
wherein v is min 、v max 、ω min And ω max Respectively minimum of vehiclesLinear velocity, maximum linear velocity, minimum angular velocity, and maximum angular velocity.
Then, a second speed search space for preventing the vehicle from colliding is determined according to the acceleration parameter information of the vehicle and the closest distance between the vehicle and the obstacle at the present time. This is mainly based on vehicle safety considerations, which enable the vehicle to stop before hitting an obstacle, so that the speed will have a range as the second speed search space V under maximum deceleration conditions a It can be expressed as:
wherein, V b And W b The maximum deceleration of the vehicle in the direction of motion and in the angular direction, respectively, is usually set by combining various factors such as vehicle performance, safety, comfort, etc. dis (v ω) is the closest distance between the vehicle and the obstacle at the present time.
It should be noted that, when calculating dis (v ω), it is necessary to query the obstacle information near the vehicle at the current time, and the obstacle data at the current time may be queried from the spatial database according to the current time, and then the obstacle information mapped on the map within a preset range from the position information of the vehicle at the current time may be queried from the obstacle data at the current time. In the embodiment of the application, the obstacles in front of and at the side of the vehicle are mainly considered, and the obstacles behind the vehicle (such as a vehicle following behind) also need to be considered in some scenes.
Then, a speed range that can be reached by the vehicle within a preset interval duration may be determined as a third speed search space according to the actual speed of the vehicle at the present time and the preset acceleration range. This is mainly to consider that the vehicle is affected by the performance of the electric machine, e.g. limited by the torque of the electric machine, limited by the maximum acceleration, deceleration and by the current actual speed of the vehicle, and that during the period of forward simulation of the vehicle (i.e. the simulated period) there is a dynamic window in which the speed is what the vehicle can actually reach. This dynamic window is the third speedSearch space V d It can be expressed as:
V d ＝{vω|v∈[v a -V b *Δt,v a +V c *Δt]∩|ω∈[ω a -W b *Δt,ω a +W c *Δt]}
wherein v is a And ω a The actual linear and angular velocities at the current time of the vehicle. V c And W c The maximum acceleration of the vehicle in the direction of motion and the angular direction, respectively, is usually set by combining various factors such as vehicle performance, safety, comfort, etc. Δ t is a preset interval duration, and may coincide with a cycle duration, for example, 100ms, for executing the vehicle control method of the present application.
Finally, the first speed is searched for the space V s Second speed search space V a And a third speed search space V d Is determined as the speed search space V of the vehicle r Expressed as:
V r ＝V s ∩V a ∩V d
step 306: and carrying out speed sampling in a speed search space to obtain more than one speed sampling point.
Determining a velocity search space V r Then from V r Sampling is carried out, the sampled speed sampling points are all composed of linear speed and angular speed, and each sampling point V i Can be expressed as (v) i ,ω i ) Assume that n velocity samples are available.
The granularity of sampling (or referred to as sampling step size) can be set according to actual requirements, or an empirical value or an experimental value and the like can be adopted.
Step 308: and carrying out local track simulation based on each speed sampling point to obtain a simulation track corresponding to each speed sampling point.
In this step, the travel locus of the vehicle in the simulation time period is actually predicted when the speed sampling point is taken as the speed of the vehicle at the next time (the next time after the preset interval time period, for example, the time 100ms after the current time).
The simulation time duration may be a value greater than a preset time duration, and may be a value including a plurality of time durations, for example, 2s, that is, a speed sampling point is used as the speed of the vehicle at the next time, and a track generated by driving at a constant speed for 2s according to the speed (including a linear speed and an angular speed) is predicted to be used as the simulation track corresponding to the speed sampling point, where the simulation track may be a straight line (when the angular speed is 0) or a curve (when the angular speed is not zero).
It should be noted that the simulated trajectory corresponding to each speed sampling point obtained in this step is not the actual driving trajectory of the vehicle, but is merely a simulated trajectory when each speed sampling point is evaluated and selected.
Step 310: and evaluating the simulated track corresponding to each speed sampling point based on a preset evaluation function, and determining the simulated track with the highest evaluation value as a local path planning result.
If n speed sampling points exist, n simulated tracks are obtained through the step 308, the n simulated tracks are evaluated respectively in the step to obtain evaluation values of the simulated tracks, and the simulated track with the highest evaluation value is selected as a local path planning result.
When evaluating the simulated trajectory, each evaluation index value of the simulated trajectory may be determined, and the evaluation index value may be weighted to obtain an evaluation value of the simulated trajectory. The weighting process may include weighted summation, weighted averaging, and the like. The evaluation index adopted in the embodiment of the present application may include one or any combination of a distance between the simulated trajectory and the obstacle, a speed of the vehicle traveling on the simulated trajectory, an angle between a traveling direction of the vehicle and the destination on the simulated trajectory, and a distance between the simulated trajectory and the lane edge line.
For example, the evaluation function may employ the following formula:
Score＝α*CostsObstacle+β*CostsPath+γ*CostsGoal+σ*CostsBoundary
wherein, Score is an evaluation value of the simulated trajectory, and α, β, γ, and σ are preset weighting coefficients, respectively.
Costssobstacle is an evaluation index representing the distance between a simulated trajectory and an obstacle. The evaluation index value is higher the greater the distance between the simulated trajectory and the obstacle, wherein the position of the point on the simulated trajectory and the position of the obstacle can be queried from a high-precision map, and the distance is calculated.
CostsPath is an evaluation index that simulates the speed at which a vehicle travels on a track. The higher the speed at which the vehicle travels on the simulated trajectory is, the higher the evaluation index value is, and the speed is actually the value of the speed sampling point.
CostsGoal is an evaluation index embodied by an included angle between the driving direction of the vehicle and a destination on a simulated track, and the smaller the included angle, the higher the index value. Wherein, the position of the destination can be inquired from the high-precision map, and then the included angle is calculated.
Costsbaundary is an evaluation index representing the distance between a simulated trajectory and a lane edge line. The data of the lane edge line can be acquired from a high-precision map, and the index value is higher as the distance between the simulated track and the lane edge line is larger. The evaluation index is blended into the evaluation function, so that the vehicle cannot be pressed or exceed the lane edge line in the running process as much as possible in the local path planning process, the conditions of collision with vehicles on the road edge and other lanes and the like are avoided, the actual road environment is better met, and the running safety of the vehicle is further improved. The information of the lane edge line can be inquired from a high-precision map, and then the distance between the simulated track and the lane edge line is calculated.
As shown in fig. 4, it is assumed that the current time position of the vehicle is position point 1, an obstacle exists at position point 2, and position point 3 is a local path end point, which may be one of discrete points in a global path trajectory obtained in the global path planning. The local path (marked as L1 in the figure) planned by the method fully considers the influence of the track and the lane edge line, while the local path (marked as L2 in the figure) planned by the traditional method does not consider the influence of the lane edge line, so that the track can exceed the road edge to cause collision danger.
After the evaluation value of each simulation track is calculated, the simulation track with the highest evaluation value is selected, and the information of the simulation track and the corresponding speed sampling point are recorded.
The above step 208, i.e., "send control information to the vehicle based on the result of the local path planning" will be described in detail with reference to the embodiment.
If the simulated track with the highest evaluation value is obtained in the local path planning, the speed sampling point corresponding to the simulated track with the highest evaluation value can be used as the target speed information, and the control information containing the target speed information is sent to the vehicle so that the vehicle-mounted terminal system of the vehicle can control the vehicle. It is understood that the in-vehicle terminal system controls the vehicle to take the target speed as the speed at the next time. The vehicle can be controlled to reach the target speed at the next moment by calculating the appropriate acceleration and the steering wheel rotation angle through a certain algorithm, and the specific calculation method is not described in detail.
The foregoing description of specific embodiments has been presented for purposes of illustration and description. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
According to an embodiment of another aspect, a vehicle control apparatus is provided. Fig. 5 shows a schematic block diagram of the vehicle control apparatus according to an embodiment, which is provided on the vehicle control server side in the architecture shown in fig. 1. The apparatus may be an application located at the server side, or may also be a functional unit such as a plug-in or Software Development Kit (SDK) located in the application located at the server side, which is not particularly limited in this embodiment of the present application. As shown in fig. 5, the apparatus 500 includes: the first obtaining unit 501, the second obtaining unit 502, the local planning unit 503 and the result sending unit 504 may further include a database maintenance unit 505. The main functions of each component unit are as follows:
a first acquisition unit 501 configured to acquire position information of the vehicle at the present time.
For example, the first obtaining unit 501 may obtain the location information periodically reported by the vehicle, or may periodically obtain the location information of the vehicle from the spatial database.
A second obtaining unit 502 configured to obtain obstacle information around the vehicle at the present time from a spatial database maintained by the map data and the obstacle data obtained by the internet of vehicles system.
A local planning unit 503 configured to perform local path planning on the vehicle based on the position information of the vehicle at the current time and the obstacle information.
A result transmitting unit 504 configured to transmit control information to the vehicle based on a result of the local path planning.
The vehicle control device executes processing periodically or executes processing after acquiring position information reported by a vehicle.
The database maintenance unit 505 is configured to acquire obstacle data reported by the vehicle-mounted sensor and/or the roadside sensing device; and storing the obstacle data, the reporting time of the obstacle data and the map data in a spatial database in an associated manner.
The obstacle data may be information on the operation of a traffic participant such as a vehicle or a pedestrian on a road, and may further include information such as a road block or a construction facility. The information can be collected by the road side sensing equipment. In addition, for some vehicles provided with vehicle-mounted sensors, road condition information and obstacle information of roads can be collected through the vehicle-mounted sensors. A large amount of vehicle sensors and roadside sensing equipment upload acquired barrier data and upload the barrier data to the server side, so that the server side comprehensively and abundantly masters the road environment including the barrier data.
Accordingly, the second obtaining unit 502 may query the obstacle data of the current time from the spatial database according to the current time; and inquiring the obstacle information which is mapped on the map and is within a preset range from the position information of the vehicle at the current time from the obstacle data at the current time. Wherein the map data may be obtained from a map database.
As an implementable way, the local planning unit 503 may be specifically configured to: acquiring speed information of a vehicle at the current moment; determining a speed search space of the vehicle based on the speed information and the position information of the vehicle at the current moment; carrying out speed sampling in a speed search space to obtain more than one speed sampling point; performing local track simulation based on each speed sampling point to obtain a simulation track corresponding to each speed sampling point; and evaluating the simulated track corresponding to each speed sampling point based on a preset evaluation function, and determining the simulated track with the highest evaluation value as a local path planning result.
Accordingly, the result transmitting unit 504 may be specifically configured to: determining a speed sampling point corresponding to the simulation track with the highest evaluation as target speed information; control information including the target speed information is transmitted to the vehicle.
As an achievable way, the local planning unit 503 may specifically perform, when determining the speed search space of the vehicle based on the speed information and the position information of the vehicle at the current time:
determining a first speed search space of the vehicle according to the speed parameter information of the vehicle;
determining a second speed search space for preventing the vehicle from colliding according to the acceleration parameter information of the vehicle and the closest distance between the vehicle and the obstacle at the current moment;
determining a speed range which can be reached by the vehicle within a preset interval duration as a third speed search space according to the actual speed and the preset acceleration range of the vehicle at the current moment;
and determining the intersection of the first speed search space, the second speed search space and the third speed search space as the speed search space of the vehicle.
As an implementation manner, when the local planning unit 503 performs local trajectory simulation based on each speed sampling point to obtain a simulated trajectory corresponding to each speed sampling point, the speed sampling points may be respectively used as the speed of the vehicle at the next time, and the driving trajectory of the vehicle within the preset simulation duration may be predicted as the simulated trajectory corresponding to the speed sampling point.
As an achievable way, when evaluating the analog trajectory corresponding to each speed sampling point based on a preset evaluation function, the local planning unit 503 may specifically perform:
determining each evaluation index value of the simulation track corresponding to the speed sampling point;
weighting each evaluation index value to obtain an evaluation value of the simulated track corresponding to the speed sampling point;
the evaluation index comprises one or any combination of the distance between the simulated track and the obstacle, the running speed of the vehicle on the simulated track, the included angle between the running direction of the vehicle and the destination on the simulated track and the distance between the simulated track and the lane edge line.
The speed may include a linear speed and an angular speed.
The local planning unit 503 may also obtain the vehicle-to-edge information from the high-accuracy map data in combination with the high-accuracy map data in the map database, such as the distance between the simulated trajectory and the obstacle, the angle between the vehicle traveling direction and the destination on the simulated trajectory, and the distance between the simulated trajectory and the lane edge line.
The vehicle referred to in the embodiments of the present application may include, but is not limited to: unmanned vehicles, assisted driving vehicles, and the like.
It should be noted that, in the embodiments of the present application, the user data may be used, and in practical applications, the user-specific personal data may be used in the scheme described herein within the scope permitted by the applicable laws and regulations, in case of meeting the requirements of the applicable laws and regulations in the country (for example, the user explicitly agrees, the user is informed certainly, and the like).
In addition, the present application also provides a computer readable storage medium, on which a computer program is stored, where the computer program is used to implement the steps of the method described in any one of the foregoing method embodiments when executed by a processor.
And an electronic device comprising:
one or more processors; and
a memory associated with the one or more processors for storing program instructions that, when read and executed by the one or more processors, perform the steps of the method of any of the preceding method embodiments.
Fig. 6 illustrates an architecture of an electronic device, which may specifically include a processor 610, a video display adapter 611, a disk drive 612, an input/output interface 613, a network interface 614, and a memory 620. The processor 610, the video display adapter 611, the disk drive 612, the input/output interface 613, the network interface 614, and the memory 620 may be communicatively connected by a communication bus 630.
The processor 610 may be implemented by a general-purpose CPU, a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute a relevant program to implement the technical solution provided by the present Application.
The Memory 620 may be implemented in the form of a ROM (Read Only Memory), a RAM (random access Memory), a static storage device, a dynamic storage device, or the like. The memory 620 may store an operating system 621 for controlling the operation of the electronic device 600, a Basic Input Output System (BIOS)622 for controlling low-level operations of the electronic device 600. In addition, a web browser 623, a data storage management system 624, a vehicle control device 625, and the like may also be stored. The vehicle control device 625 may be an application program that implements the operations of the foregoing steps in this embodiment of the present application. In summary, when the technical solution provided in the present application is implemented by software or firmware, the relevant program code is stored in the memory 620 and called to be executed by the processor 610.
The input/output interface 613 is used for connecting an input/output module to realize information input and output. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The network interface 614 is used for connecting a communication module (not shown in the figure) to realize the communication interaction between the device and other devices. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above devices only show the processor 610, the video display adapter 611, the disk drive 612, the input/output interface 613, the network interface 614, the memory 620, the bus 630, etc., in a specific implementation, the device may also include other components necessary for normal operation. Furthermore, it will be understood by those skilled in the art that the apparatus described above may also include only the components necessary to implement the solution of the present application, and not necessarily all of the components shown in the figures.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments of the present application.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, the system or system embodiments, which are substantially similar to the method embodiments, are described in a relatively simple manner, and reference may be made to some descriptions of the method embodiments for relevant points. The above-described system and system embodiments are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The technical solutions provided by the present application are introduced in detail, and specific examples are applied in the description to explain the principles and embodiments of the present application, and the descriptions of the above examples are only used to help understanding the method and the core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, the specific embodiments and the application range may be changed. In view of the above, the description should not be taken as limiting the application.
Claims (13)
1. A vehicle control method comprising:
acquiring the position information of the vehicle at the current moment;
obtaining obstacle information around the vehicle at the current time from a spatial database, wherein the spatial database is maintained by map data and obstacle data obtained by a vehicle networking system;
performing local path planning on the vehicle based on the position information of the vehicle at the current moment and the obstacle information;
and sending control information to the vehicle based on the result of the local path planning.
2. The method of claim 1, wherein the method is performed periodically or after location information reported by the vehicle is acquired.
3. The method of claim 1, further comprising:
acquiring obstacle data reported by a vehicle-mounted sensor and/or roadside sensing equipment;
and storing the obstacle data, the reporting time of the obstacle data and the map data in a spatial database in an associated manner.
4. The method of claim 3, wherein the obtaining obstacle information around the vehicle at the current time from a spatial database comprises:
inquiring barrier data of the current time from the spatial database according to the current time;
and inquiring the obstacle information mapped on the map within a preset range from the position information of the vehicle at the current moment from the obstacle data at the current moment.
5. The method of claim 1, wherein the local path planning for the vehicle based on the location information of the vehicle at the current time and the obstacle information comprises:
acquiring the speed information of the vehicle at the current moment;
determining a speed search space of the vehicle based on speed information and position information of the vehicle at the current moment;
carrying out speed sampling in the speed search space to obtain more than one speed sampling point;
performing local track simulation based on each speed sampling point to obtain a simulation track corresponding to each speed sampling point;
and evaluating the simulated track corresponding to each speed sampling point based on a preset evaluation function, and determining the simulated track with the highest evaluation value as a local path planning result.
6. The method of claim 5, wherein sending control information to the vehicle based on the result of the local path plan comprises:
determining the speed sampling point corresponding to the simulation track with the highest evaluation as target speed information;
transmitting control information including the target speed information to the vehicle.
7. The method of claim 5, wherein the determining the speed search space for the vehicle based on the speed information and the location information for the vehicle at the current time comprises:
determining a first speed search space of the vehicle according to the speed parameter information of the vehicle;
determining a second speed search space enabling the vehicle not to collide according to the acceleration parameter information of the vehicle and the closest distance between the vehicle and the obstacle at the current moment;
determining a speed range which can be reached by the vehicle within a preset interval duration as a third speed search space according to the actual speed and a preset acceleration range of the vehicle at the current moment;
and determining the intersection of the first speed search space, the second speed search space and the third speed search space as the speed search space of the vehicle.
8. The method of claim 5, wherein performing a local trajectory simulation based on each velocity sampling point to obtain a simulated trajectory corresponding to each velocity sampling point comprises:
and respectively taking the speed sampling points as the speed of the vehicle at the next moment, and predicting the running track of the vehicle in a preset simulation duration as the simulation track corresponding to the speed sampling points.
9. The method of claim 5, wherein the evaluating the analog trajectory corresponding to each velocity sampling point based on a preset evaluation function comprises:
determining each evaluation index value of the simulation track corresponding to the speed sampling point;
weighting each evaluation index value to obtain an evaluation value of the simulation track corresponding to the speed sampling point;
the evaluation index comprises one or any combination of the distance between the simulated track and the obstacle, the running speed of the vehicle on the simulated track, the included angle between the running direction of the vehicle and the destination on the simulated track and the distance between the simulated track and the lane edge line.
10. The method of any of claims 5 to 9, wherein the speed comprises a linear speed and an angular speed.
11. A vehicle control apparatus comprising:
a first acquisition unit configured to acquire position information of a vehicle at a current time;
a second acquisition unit configured to acquire obstacle information around the vehicle at a current time from a spatial database maintained by the map data and the obstacle data acquired by the internet of vehicles system;
a local planning unit configured to perform local path planning on the vehicle based on the position information of the vehicle at the current time and the obstacle information;
a result transmitting unit configured to transmit control information to the vehicle based on a result of the local path plan.
12. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the steps of the method of one of the claims 1 to 10.
13. An electronic device, comprising:
one or more processors; and
a memory associated with the one or more processors for storing program instructions that, when read and executed by the one or more processors, perform the steps of the method of any of claims 1 to 10.
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