CN112650224A - Method, device, equipment and storage medium for automatic driving simulation - Google Patents
Method, device, equipment and storage medium for automatic driving simulation Download PDFInfo
- Publication number
- CN112650224A CN112650224A CN202011439859.4A CN202011439859A CN112650224A CN 112650224 A CN112650224 A CN 112650224A CN 202011439859 A CN202011439859 A CN 202011439859A CN 112650224 A CN112650224 A CN 112650224A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- cloud control
- information
- scene
- target cloud
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004088 simulation Methods 0.000 title claims abstract description 86
- 238000000034 method Methods 0.000 title claims abstract description 71
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 24
- 230000008859 change Effects 0.000 claims description 20
- 238000004590 computer program Methods 0.000 claims description 13
- 238000012360 testing method Methods 0.000 abstract description 25
- 238000010586 diagram Methods 0.000 description 12
- 230000008569 process Effects 0.000 description 11
- 238000004891 communication Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 230000001133 acceleration Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008447 perception Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000005315 distribution function Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000003137 locomotive effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/028—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The application discloses a method, a device, equipment and a storage medium for automatic driving simulation, which specifically comprise the following steps: acquiring target cloud control traffic scene information in a cloud control system; generating initial vehicle running information under a target cloud control traffic scene by using a preset traffic flow model according to the target cloud control traffic scene information; determining vehicle track data in a target cloud control traffic scene by using a cloud control algorithm in the target cloud control traffic scene according to the initial vehicle running information; and performing automatic driving simulation of the vehicle according to the vehicle track data. According to the embodiment of the application, the automatic driving simulation test data are refined so as to adapt to the automatic driving simulation test in the cloud control scene.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for automatic driving simulation.
Background
The intelligent network cloud control automatic driving service system (hereinafter referred to as a cloud control system) takes real-time dynamic data of vehicles, roads, environments and the like as a core, and combines with data of existing traffic related systems and facilities supporting cloud control application, so that basic common services can be provided for automatic driving, and cooperative control and cooperative decision of the vehicles are supported.
In the test process of the related technology, the cloud control system focuses more on the cooperation of multiple vehicles, and needs to control the behaviors of most or all vehicles in the cloud control traffic scene, so the simulation method in the related technology is not suitable for the automatic driving simulation test in the cloud control scene.
Disclosure of Invention
The embodiment of the application provides an automatic driving simulation method, device and equipment and a computer storage medium, which can refine simulation test data by utilizing vehicle track data determined according to a specific target cloud control traffic scene, and can be further suitable for automatic driving simulation test in the cloud control scene.
In a first aspect, an embodiment of the present application provides a method for automatic driving simulation, where the method includes:
acquiring target cloud control traffic scene information in a cloud control system;
generating initial vehicle running information under a target cloud control traffic scene by using a preset traffic flow model according to the target cloud control traffic scene information;
determining vehicle track data in a target cloud control traffic scene by using a cloud control algorithm in the target cloud control traffic scene according to the initial vehicle driving information;
and performing automatic driving simulation of the vehicle according to the vehicle track data.
Optionally, the target cloud-controlled traffic scene comprises an expressway straight-going scene;
the method for determining vehicle track data in a target cloud control traffic scene by using a cloud control algorithm in the target cloud control traffic scene according to the initial vehicle driving information comprises the following steps:
inputting initial vehicle running information in an express way straight-going scene into a preset following model to obtain vehicle track data in the express way straight-going scene.
Optionally, the target cloud-controlled traffic scenario includes an express way merge scenario;
the method for determining vehicle track data in a target cloud control traffic scene by using a cloud control algorithm in the target cloud control traffic scene according to the initial vehicle driving information comprises the following steps:
updating the lane where the vehicle runs in the first cooperation area according to the initial vehicle running information in the scene of the expressway convergence to obtain first vehicle running information running on the right lane of the ramp and the main road;
calculating the first time when each first vehicle corresponding to the first vehicle running information is expected to reach the sink point according to the first vehicle running information;
determining a first vehicle driving sequence according to the first time;
calculating a first safe distance between each pair of adjacent first vehicles in the first sequence of vehicle travel;
updating the speed and position information of each pair of adjacent first vehicles according to the first safe distance and a preset first updating condition;
and determining vehicle track data under the scene of merging the express way according to the updated speed and position information of each pair of adjacent first vehicles.
Optionally, the target cloud-controlled traffic scenario comprises an expressway remittance scenario;
the method for determining vehicle track data in a target cloud control traffic scene by using a cloud control algorithm in the target cloud control traffic scene according to the initial vehicle driving information comprises the following steps:
determining the type of a second vehicle to be remitted according to the initial vehicle running information in the expressway remittance scene;
determining a second cooperation area corresponding to each type of second vehicle according to the type of the second vehicle to be remitted;
calculating a second safety distance between each pair of adjacent second vehicles entering the second coordination area;
updating the speed and position information of each pair of adjacent second vehicles according to the second safety distance and a preset second updating condition;
and determining vehicle track data under the expressway remittance scene according to the updated speed and position information of each pair of adjacent second vehicles.
Optionally, the determining, according to the initial vehicle driving information in the expressway remittance scene, a type of a second vehicle to be remitted includes:
calculating lane change times required by a second vehicle to be remitted according to initial vehicle running information in a highway remittance scene;
and determining the type of the second vehicle to be remitted according to the lane changing times.
Optionally, the initial vehicle travel information includes: one or more of a vehicle running path, a vehicle running initial speed, lane information, a headway and a vehicle departure time.
Optionally, the acquiring information of the target cloud control traffic scene in the cloud control system includes:
acquiring road information in an actual map;
and determining target cloud control traffic scene information in a cloud control system based on the road information.
Optionally, the preset traffic flow model includes a traffic flow model corresponding to the target cloud-controlled traffic scene.
In a second aspect, an embodiment of the present application provides an apparatus for automatic driving simulation, where the apparatus includes:
the acquisition module is used for acquiring target cloud control traffic scene information in the cloud control system;
the generating module is used for generating initial vehicle running information under a target cloud control traffic scene by utilizing a preset traffic flow model according to the target cloud control traffic scene information;
the determining module is used for determining vehicle track data in a target cloud control traffic scene by utilizing a cloud control algorithm in the target cloud control traffic scene according to the initial vehicle driving information;
and the simulation module is used for carrying out automatic driving simulation on the vehicle according to the vehicle track data.
In a third aspect, an embodiment of the present application provides an apparatus for automatic driving simulation, where the apparatus includes:
a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a method of automated driving simulation as described in any of the first aspect and the first aspect.
In a fourth aspect, embodiments of the present application provide a computer storage medium having computer program instructions stored thereon, which when executed by a processor, implement the method of automatic driving simulation according to the first aspect and any one of the first to fourth aspects.
The method, the device, the equipment and the computer storage medium for automatic driving simulation in the embodiment of the application can determine vehicle track data in a target cloud control traffic scene through initial vehicle running information in the target cloud control traffic scene and a corresponding cloud control algorithm. And performing automatic driving simulation on the vehicle in the cloud control system based on the vehicle track data in the target cloud control traffic scene. During simulation test, vehicle track data determined according to a specific target cloud control traffic scene are utilized, so that automatic driving simulation test data are refined, and the method can be further suitable for automatic driving simulation test in the cloud control scene.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method for automated driving simulation provided by an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a process for determining vehicle trajectory data in an expressway converging scenario according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating a method for determining vehicle trajectory data in an express way exit scenario according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of an apparatus for automated driving simulation provided by an embodiment of the present application;
fig. 5 is a schematic hardware structure diagram of an apparatus for automatic driving simulation according to an embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The automatic driving technology is rapidly developed in recent years, and the automatic driving technology based on single-vehicle perception has been partially commercialized, but the single-vehicle perception has the problems of limited vehicle perception range, great influence by factors such as environment and the like, insufficient cooperative driving among vehicles and the like. The cloud control system can solve the problems, and the cloud control system takes real-time dynamic data of vehicles, roads, environments and the like as a core, combines data of existing traffic related systems and facilities supporting cloud control application, can provide basic common services for automatic driving, and simultaneously supports cooperative control and cooperative decision of the vehicles. Different from the traditional automatic driving simulation test, the cloud control system focuses more on the multi-vehicle cooperation, and in the test process, the behaviors of part of or even all vehicles in a scene need to be controlled, so that the scene, the distribution and the running condition of traffic flow need to be more finely modeled, corresponding data are generated, and the data are used for the simulation test of the cloud control scene. Therefore, generating traffic flow parameters, vehicle tracks and the like which are matched with the actual scene has important significance for scene simulation of the cloud control system.
In order to solve the prior art problems, embodiments of the present application provide a method, an apparatus, a device, and a computer storage medium for automatic driving simulation, which determine vehicle trajectory data in a target cloud-controlled traffic scene through initial vehicle driving information in the target cloud-controlled traffic scene and a corresponding cloud control algorithm. And performing automatic driving simulation on the vehicle in the cloud control system based on the vehicle track data in the target cloud control traffic scene. During simulation test, vehicle track data determined according to a specific target cloud control traffic scene are utilized, so that automatic driving simulation test data are refined, and the method can be further suitable for automatic driving simulation test in the cloud control scene.
The following describes a method, an apparatus, a device and a computer storage medium for automatic driving simulation provided by the embodiments of the present application with reference to the accompanying drawings. It should be noted that these examples are not intended to limit the scope of the present disclosure.
The following first describes a method for automatic driving simulation provided in the embodiment of the present application.
Fig. 1 is a schematic flow chart of a method for automatic driving simulation according to an embodiment of the present application. As shown in fig. 1, in the embodiment of the present application, the method for automatic driving simulation specifically includes the following steps:
s101: and acquiring target cloud control traffic scene information in the cloud control system.
In some embodiments of the present application, first, road information in an actual map is acquired. And then, determining target cloud control traffic scene information in the cloud control system based on the road information. In particular, the map may comprise a high-precision map, i.e. an autopilot map. The high-precision map can be used for planning an automatic driving path, and can also be applied to automatic driving positioning, region of interest (ROI) filtering and the like. High-precision maps are electronic maps for use with autonomous vehicles.
The high-precision map includes information describing lanes, boundary lines of the lanes, various traffic facilities and pedestrian crossings on the road, and the like. And based on the road information in the high-precision map, target cloud control traffic scene information in the cloud control system can be determined.
For example, a high-precision map file can be acquired as scene data, and the road information in the high-precision map is directly read by using an algorithm to generate target cloud-controlled traffic scene information, such as an expressway straight-going scene, an expressway incoming scene, an expressway outgoing scene, and the like. Or, a high-precision map required by the target cloud control traffic scene can be generated by configuring the number of roads, the connection relation among the roads and the lane data.
S102: and generating initial vehicle running information under the target cloud control traffic scene by using a preset traffic flow model according to the target cloud control traffic scene information.
In some embodiments of the present application, the target cloud-controlled traffic scenario may include an express way straight-going scenario, an express way import scenario, an express way export scenario, and the like. The target cloud-controlled traffic scene information may be cloud-controlled traffic information in a corresponding scene.
The initial vehicle travel information may include: one or more of a vehicle running path, a vehicle running initial speed, lane information, a headway and a vehicle departure time.
The preset traffic flow model can comprise a traffic flow model corresponding to the target cloud control traffic scene. The traffic flow model may be a headway probability distribution model.
In some embodiments of the present application, generating initial vehicle driving information in a target cloud-controlled traffic scene by using a preset traffic flow model according to the target cloud-controlled traffic scene information may include:
firstly, acquiring a corresponding traffic flow probability distribution function and parameters thereof in a target cloud control traffic scene. Specifically, traffic flow probability distribution fitting functions corresponding to different scenes in different time periods and different weather conditions, that is, probability distribution models based on headway are obtained, as shown in table 1.
Table 1: traffic flow locomotive time distance distribution model under different target cloud control traffic scenes
Then, a function with the best fitting condition is selected according to the scene parameters to generate initial vehicle driving information of the scene, wherein the initial vehicle driving information can comprise traffic flow information and vehicle initial data.
In some embodiments, the initial vehicle travel speed may be distributed according to a normal distribution, the vehicle travel path and the lane may be distributed uniformly, and the vehicle departure time is determined according to a traffic flow fitting probability distribution function. It is understood that, in practical applications, each initialization information of the vehicle may be configured according to requirements, and will not be described herein again.
The preset traffic flow model determined based on the road network real data can be consistent with traffic flow parameters in real roads as much as possible in the simulation process, and the reality of a cloud control simulation scene can be improved.
S103: and determining vehicle track data in a target cloud control traffic scene by using a cloud control algorithm in the target cloud control traffic scene according to the initial vehicle running information.
Vehicle trajectory data in the target cloud-controlled traffic scene may include, but is not limited to, a vehicle's location such as latitude and longitude information, speed, acceleration, heading angle, timestamp, and the like. In addition, the vehicle trajectory data contains information that is configurable.
S104: and performing automatic driving simulation of the vehicle according to the vehicle track data.
In summary, according to the method for automatic driving simulation in the embodiment of the application, vehicle trajectory data in the target cloud-controlled traffic scene can be determined through initial vehicle driving information in the target cloud-controlled traffic scene and a corresponding cloud control algorithm. And performing automatic driving simulation on the vehicle in the cloud control system based on the vehicle track data in the target cloud control traffic scene. During simulation test, vehicle track data determined according to a specific target cloud control traffic scene are utilized, so that simulation test data are refined, and the method can be further suitable for automatic driving simulation test in the cloud control scene.
In some embodiments of the present application, when the target cloud-controlled traffic scene is an expressway straight-ahead driving scene, determining vehicle trajectory data in the target cloud-controlled traffic scene by using a cloud control algorithm in the target cloud-controlled traffic scene according to the initial vehicle driving information specifically includes:
and inputting the initial vehicle running information in the express way straight-going scene into a preset following model to obtain vehicle track data in the express way straight-going scene.
In some embodiments of the present application, in the preset car following model, it may be assumed that all vehicles in the target cloud control traffic scene are intelligent networked cars, and the car following model may be expressed as the following formula (1):
a(t)＝κ(Vopt(y(t))-v(t))+λΔv(t)， (1)
wherein, kappa is the sensitivity degree of the speed change of the front vehicle, lambda is the feedback coefficient of the speed error, beta is a calibration parameter, and deltay is the distance between the two vehiclesY (t) is the actual distance between two vehicles at time t, V (t) is the current speed of the current vehicle (the rear vehicle), Δ V (t) is the speed difference between the current vehicle and the front vehicle at the current time, V (t) is the minimum safe distance between the two vehicles at the current time t, V (t) is the current speed of the current vehicle (the rear vehicle), V (t) is the speed difference between the current vehicle andopt(y (t)) is an intermediate variable of the vehicle following model for calculating the acceleration to be taken by the vehicle for the next time interval. Vehicles in the straight-ahead scene of the express way plan the driving track according to the vehicle following model, so that the information of surrounding vehicles can be utilized to a greater extent, the influence of traffic flow disturbance on the vehicles in the road is reduced, and the stability of the traffic flow is improved.
Fig. 2 is a schematic flowchart of determining vehicle trajectory data in an expressway converging scene according to an embodiment of the present application. As shown in fig. 2, in the method for automatic driving simulation provided in the embodiment of the present application, when a target cloud-controlled traffic scene is an expressway merging scene, determining vehicle trajectory data in the target cloud-controlled traffic scene by using a cloud control algorithm in the target cloud-controlled traffic scene according to initial vehicle driving information may specifically include the following steps:
s201: and updating the lane in which the vehicle runs in the first cooperation area according to the initial vehicle running information in the scene of the expressway convergence, so as to obtain the first vehicle running information running on the right lane of the ramp and the main road.
In some embodiments of the present application, the first cooperative area may be a vehicle lane change cooperative control area when the express way is merged. The length of the first cooperation area can be dynamically configured according to the density of the current road, and the length of the cooperation area is correspondingly longer when the density of the road is higher.
The number of the first vehicles may be a plurality, and what is obtained by S201 is a first vehicle list that travels on the ramp and the rightmost lane of the main road. The first vehicle travel information may include travel information of vehicles in the first vehicle list.
S202: and calculating the first time when each first vehicle corresponding to the first vehicle running information is expected to reach the merge point according to the first vehicle running information.
S203: a first vehicle travel sequence is determined based on the first time.
S204: a first safe distance between each pair of adjacent first vehicles in the first vehicle driving sequence is calculated.
S205: and updating the speed and position information of each pair of adjacent first vehicles according to the first safe distance and a preset first updating condition.
Each pair of adjacent first vehicles may include a vehicle on a ramp prior to reaching the merge point and a vehicle on the main road.
S206: and determining vehicle track data under the scene of merging the express way according to the updated speed and position information of each pair of adjacent first vehicles.
The specific implementation of the above steps is described below. First, on the one hand, it is determined whether each vehicle in the first cooperation area is on a ramp. On the other hand, it is determined whether the vehicles on the main road in the first cooperation area can change lanes to the left, and a lane change instruction is sent to the lane-changeable vehicles.
In some embodiments of the present application, whether the vehicle can complete lane change to the target lane depends mainly on whether a safety distance between the vehicle and vehicles in front of and behind the target lane satisfies a safety distance, i.e. a first safety distance l, and a calculation formula (2) of the first safety distance l is as follows:
wherein, gmIs the minimum distance between two vehicles on the same lane, LV is the front vehicle, EV is the rear vehicle, vLV(t) is the speed of the preceding vehicle at time t, xLV(t + n) is the position of the preceding vehicle at time t + n, xLV(t) is the position of the preceding vehicle at time t, vEV(t) speed of the following vehicle at time t, xEV(t + n) is the position of the rear vehicle at time t + n, xEV(t) is the position of the rear vehicle at the time t, n is the time for the rear vehicle to decelerate to the front vehicle speed by sudden braking, and the calculation formula (3) is as follows:
n＝[[vEV(t)-vLV(t)]/amax/Δt] (3)
wherein, amaxRefers to a vehicleIs determined according to the physical properties of the vehicle, vLV(t) the speed of the lead vehicle at time t, Δ t being the time interval over which the algorithm is applied.
Calculating x in formula (2)EV(t + n) and xLV(t + n) can be calculated by equations (4) and (5) as follows:
xEV(t+n)＝Δt(nvEV(t)-amaxΔtn(n+1)/2) (4)
according to the above two judgment results, if the vehicle in the first cooperation area is on the ramp or the vehicle is on the rightmost lane of the main road, the list of vehicles on the ramp and on the rightmost lane of the main road can be obtained. First vehicle travel information may be determined from the vehicle list.
Calculating the first time t predicted to reach the junction point of all vehicles in the vehicle list according to the first vehicle running informationEVAnd may be based on the first time tEVAnd sequencing the vehicle list to determine a first vehicle running sequence. Illustratively, the list of vehicles may be sorted in ascending order. A first time tEVThe calculation formula (6) is as follows:
wherein d isrDistance of the vehicle from the joining point, theadwayIs the shortest headway, tLVThe time of arrival at the merge point is predicted for the lead vehicle.
Then, the speed and position information of each pair of adjacent first vehicles is updated according to the first safe distance and a preset first updating condition.
Specifically, the preset first update condition may be that the first n-1 vehicles in the first vehicle driving sequence are traversed, and for the vehicle on the main road, it is determined whether the vehicle can pass through the merge point before accelerating the vehicle on the front ramp. If the vehicle on the main road can pass through the merge point before the vehicle on the front ramp by accelerating, the speed and position information of each pair of adjacent first vehicles is updated based on the first safety distance.
In some embodiments of the present application, in the first cooperation area, by adjusting the longitudinal speed of each pair of adjacent first vehicles using the speed adjustment algorithm, the vehicles on the ramp before reaching the merge point can be set apart from the vehicles on the main road by a sufficient safety distance, i.e., the first safety distance, so that the vehicles on the ramp can complete the merge safely. The speed adjustment equation (7) for adjusting each pair of adjacent first vehicles is as follows:
wherein v isEV(t) is the speed of the following vehicle (host vehicle) at time t, vLV(t) is the speed of the preceding vehicle at time t, tstartTo start the speed adjustment, tLCTime of completion of speed adjustment, tendTo end the moment of adjustment, α0And (t), alpha (t) is a preset coefficient, the two coefficients are the coefficient of speed adjustment calculated by the distance relation between the current vehicle and the front vehicle, the amplitude of speed change can be adjusted by adjusting beta, and the time for completing the adjustment. The time for starting the adjustment is the time when the rear vehicle (the vehicle) enters the cooperative area, the time for finishing the speed adjustment is the time when the distance between the front vehicle and the rear vehicle is greater than or equal to the first safety distance, and the time for finishing the adjustment is the time when the rear vehicle (the vehicle) drives away from the junction point.
In this scenario, the adjustment of the longitudinal speed of the vehicle needs to be completed within a specified length range on one hand, and it needs to be ensured that no collision occurs between vehicles when the adjustment cannot be completed, and on the other hand, the acceleration and the variation of the acceleration during the adjustment process need to be reduced as much as possible, and the above two problems can be better solved by using the longitudinal speed adjustment formula (7).
In some embodiments of the present application, a first safety distance between each pair of adjacent first vehicles in the first vehicle driving sequence may be calculated using equation (2) above.
And finally, determining vehicle track data under the scene of expressway convergence according to the updated speed and position information of each pair of adjacent first vehicles.
In some embodiments of the present application, in the highway merging scenario, the vehicle trajectory data may also include vehicle trajectory data of vehicles on non-rightmost lanes of the main road, i.e., other vehicles within the first cooperation area. For vehicles on the non-rightmost lane of the main road, driving can be carried out according to a vehicle following model under the scene of straight forward movement of the expressway, and corresponding vehicle track data are obtained.
Fig. 3 is a schematic flowchart of determining vehicle trajectory data in an express way remittance scenario according to an embodiment of the present application. As shown in fig. 3, in the method for automatic driving simulation provided in the embodiment of the present application, when the target cloud-controlled traffic scene is an expressway remittance scene, the vehicle trajectory data in the target cloud-controlled traffic scene is determined by using a cloud control algorithm in the target cloud-controlled traffic scene according to the initial vehicle driving information, which may specifically include the following steps:
s301: and determining the type of a second vehicle to be remitted according to the initial vehicle running information under the expressway remittance scene.
In some embodiments of the present application, first, according to initial vehicle running information in a highway remittance scene, lane change times required by a second vehicle to be remitted are calculated; then, the type of the second vehicle to be remitted is determined according to the lane change times. Exemplarily, the required number of lane changes is 1 is a second vehicle of the first type; the required number of lane changes 2 being a second type of second vehicle
In some embodiments of the present application, the number of second vehicles to be remitted may be a plurality of vehicles. Namely, a second vehicle list to be remitted is obtained according to the initial vehicle running information under the scene of fast road remittance.
S302: and determining a second cooperation area corresponding to each type of second vehicle according to the type of the second vehicle to be remitted.
In some embodiments of the present application, the second coordination area may be a vehicle coordination control range in a highway convergence scene.
S303: a second safe distance between each pair of adjacent second vehicles entering the second coordination area is calculated.
S304: and updating the speed and position information of each pair of adjacent second vehicles according to the second safe distance and a preset second updating condition.
The presetting of the second update condition may include determining whether a distance between each pair of adjacent second vehicles satisfies a current second safety distance.
S305: and determining vehicle track data under the expressway remittance scene according to the updated speed and position information of each pair of adjacent second vehicles.
Specific execution procedures of the above S301 to S305 are described below. In the scene of fast road junction, the cloud control system coordinates the relative position and speed relationship of the vehicles on the right lane on the main road, controls the vehicles needing to be joined to change lanes to the rightmost lane in advance, and reduces the influence of vehicle junction on the straight vehicles on the main road.
First, in a highway junction scene, according to initial vehicle running information of all vehicles, a second vehicle needing to be joined is determined, wherein the second vehicle can be a plurality of vehicles, namely the second vehicle comprises a second vehicle list. And calculating lane change times required by the vehicles to be remitted in the second vehicle list, and classifying the vehicles according to the lane change times. For example, vehicles that need lane change 1 time and that need lane change 2 times can be classified. For express roads with lane number greater than 3, classification categories can be further increased.
Secondly, calculating a second cooperation area required by a second vehicle of a different type according to the type of the second vehicle to be remitted. For example, the second coordination area required for a lane change may be in the range of 100 meters to 500 meters. In addition, the range of the second cooperation area may be selected according to an actual scene, which is not described herein again.
Again, a second safe distance between each pair of adjacent second vehicles entering the second coordination area is calculated. Specifically, for a second vehicle requiring lane change, a front vehicle and a rear vehicle on the target lane are found. And (3) calculating to obtain a second safety distance according to the calculation formula (2) of the first safety distance l, determining whether the distance between each pair of adjacent second vehicles, namely the front vehicle and the rear vehicle, meets the current second safety distance, and if so, controlling the vehicles to change lanes to the right.
If not, the speed of the second vehicle is adjusted using speed adjustment equation (7) and the position information of the second vehicle is updated according to the speed adjustment algorithm in the foregoing embodiment.
And finally, determining vehicle track data under the expressway remittance scene according to the updated speed and position information of each pair of adjacent second vehicles.
In some embodiments of the present application, in the highway egress scenario, the vehicle trajectory data may also include vehicle trajectory data of other vehicles within the second collaborative area.
In some embodiments of the present application, along with the updated speed and location information of the second vehicle, the speed and location information of the other vehicles are also updated accordingly, and the vehicle can be driven according to the following model to obtain corresponding vehicle trajectory data. In addition, other vehicles within the second coordination area may update the speed parameters in the following vehicle model based on the updated speed for reducing disturbances in the traffic flow.
In addition, in the expressway junction scene, vehicles which do not perform junction operation or lane change operation can be driven according to the following model in the expressway junction straight-ahead scene, and corresponding vehicle track data can be obtained.
In conclusion, the automatic driving simulation method in the embodiment of the application aims at the cooperative characteristic that the cloud control system focuses more on multiple vehicles, more refined modeling is performed on the scenes and the distribution and running conditions of the traffic flow, and vehicle track data corresponding to each scene is generated. The vehicle track data of different scenes are utilized to carry out simulation test, and the simulation test effect which is more consistent with the actual scene can be achieved. Meanwhile, the vehicle track data can also be used as standardized data of cloud control scene simulation tests and serve as test services of other applications.
Based on the method for automatic driving simulation provided by the embodiment, correspondingly, the application also provides a specific implementation mode of the device for automatic driving simulation. Please see the examples below.
Fig. 4 is a schematic structural diagram of an apparatus for automatic driving simulation according to an embodiment of the present application. As shown in fig. 4, in the embodiment of the present application, the apparatus for automatic driving simulation specifically includes:
the acquisition module 401 is used for acquiring target cloud control traffic scene information in a cloud control system;
a generating module 402, configured to generate initial vehicle driving information in a target cloud control traffic scene by using a preset traffic flow model according to the target cloud control traffic scene information;
a determining module 403, configured to determine vehicle trajectory data in a target cloud-controlled traffic scene according to the initial vehicle driving information and by using a cloud control algorithm in the target cloud-controlled traffic scene;
and the simulation module 404 is configured to perform automatic driving simulation of the vehicle according to the vehicle trajectory data.
Each module/unit in the apparatus shown in fig. 4 has a function of implementing each step in fig. 1 to 3, and can achieve the corresponding technical effect, and for brevity, the description is not repeated herein.
In summary, in the embodiment of the present application, the device for automatic driving simulation may be used to execute the method for automatic driving simulation in the above embodiment, and the method may determine vehicle trajectory data in a target cloud-controlled traffic scene through initial vehicle driving information in the target cloud-controlled traffic scene and a corresponding cloud control algorithm. And performing automatic driving simulation on the vehicle in the cloud control system based on the vehicle track data in the target cloud control traffic scene. During simulation test, vehicle track data determined according to a specific target cloud control traffic scene are utilized, so that simulation test data are refined, and the method can be further suitable for automatic driving simulation test in the cloud control scene.
Based on the method for automatic driving simulation provided by the embodiment, correspondingly, the application also provides a specific implementation mode of the automatic driving simulation equipment. Please see the examples below.
Fig. 5 shows a hardware structure diagram of an apparatus for automatic driving simulation provided by an embodiment of the present application.
The apparatus for automated driving simulation may include a processor 501 and a memory 502 having stored computer program instructions.
Specifically, the processor 501 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The processor 501 reads and executes computer program instructions stored in the memory 502 to implement any of the methods of autopilot simulation in the above embodiments.
In one example, the device for autopilot simulation may also include a communication interface 503 and a bus 510. As shown in fig. 5, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 510 to complete communication therebetween.
The communication interface 503 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present application.
The apparatus for automated driving simulation may perform the method for automated driving simulation in the embodiment of the present application, thereby implementing the method for automated driving simulation described in conjunction with fig. 1 to 3.
In addition, in combination with the method for automatic driving simulation in the foregoing embodiments, the embodiments of the present application may provide a computer storage medium to implement. The computer storage medium having computer program instructions stored thereon; the computer program instructions, when executed by a processor, implement any of the methods of autopilot simulation in the above embodiments.
It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As described above, only the specific embodiments of the present application are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.
Claims (11)
1. A method of automated driving simulation, comprising:
acquiring target cloud control traffic scene information in a cloud control system;
generating initial vehicle running information under a target cloud control traffic scene by using a preset traffic flow model according to the target cloud control traffic scene information;
determining vehicle track data in a target cloud control traffic scene by using a cloud control algorithm in the target cloud control traffic scene according to the initial vehicle driving information;
and performing automatic driving simulation of the vehicle according to the vehicle track data.
2. The method of claim 1, wherein the target cloud-controlled traffic scenario comprises an express way straight-ahead scenario;
the method for determining vehicle track data in a target cloud control traffic scene by using a cloud control algorithm in the target cloud control traffic scene according to the initial vehicle driving information comprises the following steps:
inputting initial vehicle running information in an express way straight-going scene into a preset following model to obtain vehicle track data in the express way straight-going scene.
3. The method of claim 1, wherein the target cloud controlled traffic scenario comprises an express way merge scenario;
the method for determining vehicle track data in a target cloud control traffic scene by using a cloud control algorithm in the target cloud control traffic scene according to the initial vehicle driving information comprises the following steps:
updating the lane where the vehicle runs in the first cooperation area according to the initial vehicle running information in the scene of the expressway convergence to obtain first vehicle running information running on the right lane of the ramp and the main road;
calculating the first time when each first vehicle corresponding to the first vehicle running information is expected to reach the sink point according to the first vehicle running information;
determining a first vehicle driving sequence according to the first time;
calculating a first safe distance between each pair of adjacent first vehicles in the first sequence of vehicle travel;
updating the speed and position information of each pair of adjacent first vehicles according to the first safe distance and a preset first updating condition;
and determining vehicle track data under the scene of merging the express way according to the updated speed and position information of each pair of adjacent first vehicles.
4. The method of claim 1, wherein the target cloud controlled traffic scenario comprises a highway remittance scenario;
the method for determining vehicle track data in a target cloud control traffic scene by using a cloud control algorithm in the target cloud control traffic scene according to the initial vehicle driving information comprises the following steps:
determining the type of a second vehicle to be remitted according to the initial vehicle running information in the expressway remittance scene;
determining a second cooperation area corresponding to each type of second vehicle according to the type of the second vehicle to be remitted;
calculating a second safety distance between each pair of adjacent second vehicles entering the second coordination area;
updating the speed and position information of each pair of adjacent second vehicles according to the second safety distance and a preset second updating condition;
and determining vehicle track data under the expressway remittance scene according to the updated speed and position information of each pair of adjacent second vehicles.
5. The method according to claim 4, wherein the determining the type of the second vehicle to be remitted according to the initial vehicle driving information in the expressway remittance scene comprises:
calculating lane change times required by a second vehicle to be remitted according to initial vehicle running information in a highway remittance scene;
and determining the type of the second vehicle to be remitted according to the lane changing times.
6. The method of claim 1, wherein the initial vehicle travel information comprises: one or more of a vehicle running path, a vehicle running initial speed, lane information, a headway and a vehicle departure time.
7. The method according to claim 1, wherein the acquiring target cloud control traffic scene information in a cloud control system comprises:
acquiring road information in an actual map;
and determining target cloud control traffic scene information in a cloud control system based on the road information.
8. The method according to claim 1, wherein the preset traffic flow model comprises a traffic flow model corresponding to a target cloud-controlled traffic scene.
9. An apparatus for automated driving simulation, the apparatus comprising:
the acquisition module is used for acquiring target cloud control traffic scene information in the cloud control system;
the generating module is used for generating initial vehicle running information under a target cloud control traffic scene by utilizing a preset traffic flow model according to the target cloud control traffic scene information;
the determining module is used for determining vehicle track data in a target cloud control traffic scene by utilizing a cloud control algorithm in the target cloud control traffic scene according to the initial vehicle driving information;
and the simulation module is used for carrying out automatic driving simulation on the vehicle according to the vehicle track data.
10. An apparatus for automated driving simulation, the apparatus comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a method of automated driving simulation according to any of claims 1 to 8.
11. A computer storage medium having computer program instructions stored thereon which, when executed by a processor, implement a method of autopilot simulation as claimed in any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011439859.4A CN112650224A (en) | 2020-12-11 | 2020-12-11 | Method, device, equipment and storage medium for automatic driving simulation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011439859.4A CN112650224A (en) | 2020-12-11 | 2020-12-11 | Method, device, equipment and storage medium for automatic driving simulation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112650224A true CN112650224A (en) | 2021-04-13 |
Family
ID=75351154
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011439859.4A Pending CN112650224A (en) | 2020-12-11 | 2020-12-11 | Method, device, equipment and storage medium for automatic driving simulation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112650224A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113569378A (en) * | 2021-06-16 | 2021-10-29 | 阿波罗智联(北京)科技有限公司 | Simulation scene generation method and device, electronic equipment and storage medium |
| CN114155705A (en) * | 2021-10-22 | 2022-03-08 | 广州文远知行科技有限公司 | Method, device and equipment for evaluating traffic barrier behavior of vehicle and storage medium |
| CN114707364A (en) * | 2022-06-02 | 2022-07-05 | 西南交通大学 | Ramp vehicle convergence simulation method, device, equipment and readable storage medium |
| CN115131965A (en) * | 2022-06-23 | 2022-09-30 | 重庆长安汽车股份有限公司 | Vehicle control method, device, system, electronic device and storage medium |
| CN115981177A (en) * | 2022-12-07 | 2023-04-18 | 北京百度网讯科技有限公司 | Simulated vehicle generation method and device, electronic equipment and computer storage medium |
| CN116167252A (en) * | 2023-04-25 | 2023-05-26 | 小米汽车科技有限公司 | Method, device, equipment and storage medium for determining radar configuration information |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108898854A (en) * | 2018-07-06 | 2018-11-27 | 广州交通信息化建设投资营运有限公司 | A kind of Ramp cooperative control method based on Model Predictive Control |
| CN109520744A (en) * | 2018-11-12 | 2019-03-26 | 百度在线网络技术（北京）有限公司 | The driving performance test method and device of automatic driving vehicle |
| WO2019065409A1 (en) * | 2017-09-29 | 2019-04-04 | 日立オートモティブシステムズ株式会社 | Automatic driving simulator and map generation method for automatic driving simulator |
| CN111123920A (en) * | 2019-12-10 | 2020-05-08 | 武汉光庭信息技术股份有限公司 | Method and device for generating automatic driving simulation test scene |
| CN111177887A (en) * | 2019-12-09 | 2020-05-19 | 武汉光庭信息技术股份有限公司 | Method and device for constructing simulation track data based on real driving scene |
| CN111325975A (en) * | 2020-02-19 | 2020-06-23 | 南京航空航天大学 | Centralized optimization coordination method of intelligent networked vehicles in afflux entrance area |
| CN111338351A (en) * | 2020-03-24 | 2020-06-26 | 东南大学 | Multi-intelligent-network-connection vehicle cooperative confluence control method for expressway ramp entrance |
| WO2020135742A1 (en) * | 2018-12-29 | 2020-07-02 | 长城汽车股份有限公司 | Autonomous driving vehicle horizontal decision system and horizontal decision-making method |
| CN111579251A (en) * | 2020-04-16 | 2020-08-25 | 国汽(北京)智能网联汽车研究院有限公司 | Method, device and equipment for determining vehicle test scene and storage medium |
| CN111780981A (en) * | 2020-05-21 | 2020-10-16 | 东南大学 | Intelligent vehicle formation lane change performance evaluation method |
| CN111798661A (en) * | 2020-07-13 | 2020-10-20 | 腾讯科技（深圳）有限公司 | Overtaking early warning method and device during vehicle running |
| CN111856968A (en) * | 2020-07-31 | 2020-10-30 | 成都信息工程大学 | Large-scale traffic simulation system and method based on parallel computing |
| CN111932910A (en) * | 2020-06-22 | 2020-11-13 | 淮阴工学院 | Real-time dynamic variable lane safety control method under intelligent vehicle-road cooperative environment |
-
2020
- 2020-12-11 CN CN202011439859.4A patent/CN112650224A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019065409A1 (en) * | 2017-09-29 | 2019-04-04 | 日立オートモティブシステムズ株式会社 | Automatic driving simulator and map generation method for automatic driving simulator |
| CN108898854A (en) * | 2018-07-06 | 2018-11-27 | 广州交通信息化建设投资营运有限公司 | A kind of Ramp cooperative control method based on Model Predictive Control |
| CN109520744A (en) * | 2018-11-12 | 2019-03-26 | 百度在线网络技术（北京）有限公司 | The driving performance test method and device of automatic driving vehicle |
| WO2020135742A1 (en) * | 2018-12-29 | 2020-07-02 | 长城汽车股份有限公司 | Autonomous driving vehicle horizontal decision system and horizontal decision-making method |
| CN111177887A (en) * | 2019-12-09 | 2020-05-19 | 武汉光庭信息技术股份有限公司 | Method and device for constructing simulation track data based on real driving scene |
| CN111123920A (en) * | 2019-12-10 | 2020-05-08 | 武汉光庭信息技术股份有限公司 | Method and device for generating automatic driving simulation test scene |
| CN111325975A (en) * | 2020-02-19 | 2020-06-23 | 南京航空航天大学 | Centralized optimization coordination method of intelligent networked vehicles in afflux entrance area |
| CN111338351A (en) * | 2020-03-24 | 2020-06-26 | 东南大学 | Multi-intelligent-network-connection vehicle cooperative confluence control method for expressway ramp entrance |
| CN111579251A (en) * | 2020-04-16 | 2020-08-25 | 国汽(北京)智能网联汽车研究院有限公司 | Method, device and equipment for determining vehicle test scene and storage medium |
| CN111780981A (en) * | 2020-05-21 | 2020-10-16 | 东南大学 | Intelligent vehicle formation lane change performance evaluation method |
| CN111932910A (en) * | 2020-06-22 | 2020-11-13 | 淮阴工学院 | Real-time dynamic variable lane safety control method under intelligent vehicle-road cooperative environment |
| CN111798661A (en) * | 2020-07-13 | 2020-10-20 | 腾讯科技（深圳）有限公司 | Overtaking early warning method and device during vehicle running |
| CN111856968A (en) * | 2020-07-31 | 2020-10-30 | 成都信息工程大学 | Large-scale traffic simulation system and method based on parallel computing |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113569378A (en) * | 2021-06-16 | 2021-10-29 | 阿波罗智联(北京)科技有限公司 | Simulation scene generation method and device, electronic equipment and storage medium |
| CN113569378B (en) * | 2021-06-16 | 2024-01-05 | 阿波罗智联(北京)科技有限公司 | Simulation scene generation method and device, electronic equipment and storage medium |
| CN114155705A (en) * | 2021-10-22 | 2022-03-08 | 广州文远知行科技有限公司 | Method, device and equipment for evaluating traffic barrier behavior of vehicle and storage medium |
| CN114707364A (en) * | 2022-06-02 | 2022-07-05 | 西南交通大学 | Ramp vehicle convergence simulation method, device, equipment and readable storage medium |
| CN115131965A (en) * | 2022-06-23 | 2022-09-30 | 重庆长安汽车股份有限公司 | Vehicle control method, device, system, electronic device and storage medium |
| CN115131965B (en) * | 2022-06-23 | 2023-07-07 | 重庆长安汽车股份有限公司 | Vehicle control method, device, system, electronic equipment and storage medium |
| CN115981177A (en) * | 2022-12-07 | 2023-04-18 | 北京百度网讯科技有限公司 | Simulated vehicle generation method and device, electronic equipment and computer storage medium |
| CN116167252A (en) * | 2023-04-25 | 2023-05-26 | 小米汽车科技有限公司 | Method, device, equipment and storage medium for determining radar configuration information |
| CN116167252B (en) * | 2023-04-25 | 2024-01-30 | 小米汽车科技有限公司 | Method, device, equipment and storage medium for determining radar configuration information |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112650224A (en) | Method, device, equipment and storage medium for automatic driving simulation | |
| WO2022121248A1 (en) | Test evaluation information determining method, apparatus and device, and computer storage medium | |
| JP6463713B2 (en) | Vehicle control method and apparatus and determination module acquisition method and apparatus | |
| WO2022121247A1 (en) | Vehicle collaborative decision-making method and apparatus, and electronic device and computer storage medium | |
| CN112668153A (en) | Method, device and equipment for generating automatic driving simulation scene | |
| CN110763246A (en) | Automatic driving vehicle path planning method and device, vehicle and storage medium | |
| CN111579251A (en) | Method, device and equipment for determining vehicle test scene and storage medium | |
| CN111123735A (en) | Automatic driving simulation operation method and device | |
| CN110654381B (en) | Method and device for controlling a vehicle | |
| CN112665590A (en) | Vehicle track determination method and device, electronic equipment and computer storage medium | |
| JP6850324B2 (en) | Obstacle distribution simulation method, device, terminal and program based on multi-model | |
| CN113168762B (en) | Vehicle travel support method, vehicle travel support device, and automatic driving system | |
| CN110696826B (en) | Method and device for controlling a vehicle | |
| US20230150549A1 (en) | Hybrid log simulated driving | |
| CN112249009A (en) | Vehicle speed control method, device and system and electronic equipment | |
| CN111368409A (en) | Vehicle flow simulation processing method, device, equipment and storage medium | |
| CN114475656A (en) | Travel track prediction method, travel track prediction device, electronic device, and storage medium | |
| CN113635912A (en) | Vehicle control method, device, equipment, storage medium and automatic driving vehicle | |
| CN109375630A (en) | A kind of intelligent driving test track seeking system and method | |
| CN109974731A (en) | A kind of method and apparatus of Unmanned Systems' path planning target selection | |
| Schlenoff et al. | PRIDE: A hierarchical, integrated prediction framework for autonomous on-road driving | |
| CN116224317A (en) | Method and device for predicting motion trail of non-motor vehicle and related equipment | |
| CN112644487B (en) | Automatic driving method and device | |
| CN109532840B (en) | A kind of automatic driving vehicle control method, device, system and electronic equipment | |
| CN113029155A (en) | Robot automatic navigation method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |