CN114815825B - Method and device for determining optimal running track of vehicle - Google Patents
Method and device for determining optimal running track of vehicle Download PDFInfo
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
- G05D1/0253—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means extracting relative motion information from a plurality of images taken successively, e.g. visual odometry, optical flow
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
- G05D1/0259—Control of position or course in two dimensions specially adapted to land vehicles using magnetic or electromagnetic means
- G05D1/0263—Control of position or course in two dimensions specially adapted to land vehicles using magnetic or electromagnetic means using magnetic strips
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- 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
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Abstract
The application provides a method and a device for determining an optimal running track of a vehicle, wherein the method comprises the following steps: when the optimal running track is required to be updated, the positions of obstacles around the target vehicle and the speed of the target vehicle are acquired; inputting the position and the vehicle speed into a pre-trained machine learning model to obtain a first area, wherein the first area is an area which does not comprise an obstacle; constructing a solving function according to the starting state, the ending state and the comfort index requirement of the target vehicle; and determining the optimal running track of the target vehicle in the first area according to the solving function. The application can reduce the used computing resources and shorten the computing time.
Description
Technical Field
The application relates to the technical field of vehicle driving, in particular to a method and a device for determining an optimal driving track of a vehicle.
Background
With the development of technology, automatic driving plays a vital role in the field of intelligent automobiles, and the core of the automatic driving method is how to determine an optimal driving path of a vehicle, so that the vehicle can drive according to the optimal driving path. The optimal running path is defined as a running path which is not collided with the obstacle and meets the kinematic constraint, the environmental constraint and the time constraint of the vehicle in the known starting state, the termination state, the obstacle distribution in the environment and the comfort index requirement of the vehicle.
In the prior art, a method for solving an optimal running track of a vehicle generally comprises the steps of firstly obtaining a surrounding environment of the vehicle, taking the surrounding environment of the vehicle as a solving area, and then constructing a corresponding solving space based on a sampling result of the solving area. Then, according to the starting state, the ending state, the obstacle distribution in the environment and the comfort index requirements of the vehicle, a solution function is constructed, a solution conforming to the solution function is found in a solution space, and the optimal running track of the vehicle is constructed based on the solutions.
However, the above procedure is to construct a solution space based on the surrounding environment of the vehicle, and thus a large number of solutions are included in the constructed solution space, so that the solution is performed in the solution space using the solution function, which requires a large amount of computing resources and takes a long time to calculate.
Disclosure of Invention
In view of the above, the present application provides a method and apparatus for determining an optimal driving trajectory of a vehicle, which can reduce the used computing resources and shorten the computing time.
In order to achieve the above purpose, the present application mainly provides the following technical solutions:
In a first aspect, the present application provides a method for determining an optimal driving trajectory of a vehicle, the method comprising:
when the optimal running track is required to be updated, the positions of obstacles around the target vehicle and the speed of the target vehicle are acquired;
Inputting the position and the vehicle speed into a pre-trained machine learning model to obtain a first area, wherein the first area is an area which does not comprise an obstacle;
constructing a solving function according to the starting state, the ending state and the comfort index requirement of the target vehicle;
and according to the solving function, determining the optimal running track of the target vehicle in the first area.
In a second aspect, the present application provides a device for determining an optimal travel track of a vehicle, the device comprising:
An acquisition unit configured to acquire a position of an obstacle around a target vehicle and a vehicle speed of the target vehicle when an optimal running track needs to be updated;
The input unit is used for inputting the position and the vehicle speed acquired by the acquisition unit into a pre-trained machine learning model to obtain a first area, wherein the first area is an area which does not comprise an obstacle;
The construction unit is used for constructing a solving function according to the starting state, the ending state and the comfort index requirement of the target vehicle;
And the determining unit is used for determining the optimal running track of the target vehicle in the first area obtained by the input unit according to the solving function constructed by the construction unit.
In a third aspect, the present application provides a terminal for running a program, where the terminal executes the method for determining an optimal running track of a vehicle according to the first aspect when running.
In a fourth aspect, the present application provides a storage medium, where the storage medium is used to store a computer program, where the computer program when running controls a device where the storage medium is located to execute the method for determining the optimal driving track of the vehicle according to the first aspect.
By means of the technical scheme, the application provides a method and a device for determining the optimal running track of a vehicle, and in the method and the device, before solving by using a solving function, the positions of obstacles around a target vehicle and the speed of the target vehicle can be input into a machine learning model trained in advance to obtain a first area, and then the optimal running track of the target vehicle is determined in the first area according to the solving function. Because the obtained first area is a part of the surrounding environment of the target vehicle and the first area is an area which does not comprise an obstacle, the method and the device substantially remove the invalid solution corresponding to the area with the obstacle in the surrounding environment of the target vehicle, reduce the calculated amount of calculation by using the invalid solution and shorten the calculation time.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
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In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for determining an optimal driving track of a vehicle according to the present disclosure;
FIG. 2 is a schematic illustration of a first region of the present disclosure;
FIG. 3 is a flow chart of another method for determining an optimal driving trajectory of a target vehicle according to the present disclosure;
FIG. 4 is a flow chart of a method for determining an optimal driving trajectory based on a plurality of available sampling rules according to the present disclosure;
FIG. 5 is a schematic illustration of a second area of the present disclosure;
FIG. 6 is a schematic illustration of an optimal travel path of a target vehicle according to the present disclosure;
FIG. 7 is a flow chart of a method for determining an optimal driving trajectory based on an available sampling rule according to the present disclosure;
FIG. 8 is a flow chart of a method for training a machine learning model according to the present disclosure;
FIG. 9 is a flow chart of a method for training a machine learning model according to the present disclosure;
FIG. 10 is a schematic diagram of a device for determining an optimal driving trajectory of a vehicle according to the present disclosure;
fig. 11 is a schematic structural view of another device for determining an optimal driving track of a vehicle according to the present disclosure.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.
With the development of technology, automatic driving plays a vital role in the field of intelligent automobiles, and the core of the automatic driving method is how to determine an optimal driving path of a vehicle, so that the vehicle can drive according to the optimal driving path. The optimal running path is defined as a running path which is not collided with the obstacle and meets the kinematic constraint, the environmental constraint and the time constraint of the vehicle in the known starting state, the termination state, the obstacle distribution in the environment and the comfort index requirement of the vehicle.
In the prior art, a method for solving an optimal running track of a vehicle generally comprises the steps of firstly obtaining a surrounding environment of the vehicle, taking the surrounding environment of the vehicle as a solving area, and then constructing a corresponding solving space based on a sampling result of the solving area. Then, according to the starting state, the ending state, the obstacle distribution in the environment and the comfort index requirements of the vehicle, a solution function is constructed, a solution conforming to the solution function is found in a solution space, and the optimal running track of the vehicle is constructed based on the solutions.
However, the above procedure is to construct a solution space based on the surrounding environment of the vehicle, and thus a large number of solutions are included in the constructed solution space, so that the solution is performed in the solution space using the solution function, which requires a large amount of computing resources and takes a long time to calculate.
In order to solve the above problems, the embodiment of the application provides a method for determining an optimal driving track of a vehicle, which can reduce the used computing resources and shorten the computing time. The specific implementation steps are shown in fig. 1, including:
Step 101, when the optimal running track needs to be updated, the positions of obstacles around the target vehicle and the speed of the target vehicle are obtained.
Wherein the target vehicle is a vehicle currently running. The obstacle includes objects such as pedestrians, vehicles, numbers, road teeth, etc., and can be said to be all objects except the target vehicle.
In a specific embodiment of this step, when the target vehicle needs to navigate to the destination, a navigation route needs to be planned first, and then the target vehicle is controlled to travel according to the navigation route. At this time, since the navigation route is generally long and there are a large number of obstacles on the navigation route, in order for the target vehicle to safely travel to the destination through the navigation route, the navigation route may be divided into a plurality of points, thereby allowing the target vehicle to be based on. For example, the current position of the target vehicle is used as an initial position of the track planning, the position corresponding to the point through which the target vehicle passes is used as a final position of the track planning, and then the track planning is performed according to the initial position and the final position. When the target vehicle travels according to the planned optimal travel track between the initial position and the final position, the position of the obstacle on the road may change, for example, the corresponding positions of the pedestrian, the vehicle and the like may change, which may cause the target vehicle to collide with the obstacle, so that the track planning needs to be periodically performed, that is, the optimal travel track of the target vehicle is periodically updated, so as to prevent the target vehicle from colliding with the obstacle. Thus, when the optimum travel locus needs to be updated, the position of the obstacle around the target vehicle and the vehicle speed of the target vehicle are acquired.
It should be noted that, the position of the obstacle may be obtained by collecting the surrounding environment of the target vehicle by the camera installed on the target vehicle and identifying the picture taken by the camera. The speed of the target vehicle may be obtained by reading the data of the wheel speed device.
Step 102, inputting the position and the vehicle speed into a pre-trained machine learning model to obtain a first area.
The first area is an area without an obstacle, the first area includes an optimal running track of the target vehicle, and the first area is specifically shown in fig. 2. The greater the vehicle speed, the farther the first region is from the target vehicle, and the smaller the vehicle speed, the closer the first region is to the target vehicle.
In the specific embodiment of this step, since the machine learning model trained in advance is trained on the region excluding the obstacle, the first region obtained based on the machine learning model trained in advance is also the region excluding the obstacle.
And step 103, constructing a solving function according to the starting state, the ending state and the comfort index requirement of the target vehicle.
The starting state includes information such as a starting position, a starting speed, a starting acceleration, etc. of the target vehicle, and the ending state includes information such as an ending position, an ending speed, an ending acceleration, etc. of the target vehicle. The comfort index is data for evaluating the comfort of the driver during traveling, for example, in general, the shorter the time for the target vehicle to reach the end position, the higher the comfort of the driver, and the longer the time for the target vehicle to reach the end position, the longer the comfort of the driver.
In the specific implementation mode of the step, according to the starting state, the ending state and the comfort index of the target vehicle, the kinematic constraint, the environment constraint, the time constraint and the target function are constructed, and the kinematic constraint, the environment constraint and the time constraint are abstracted into the target function to obtain the solving function.
And 104, determining the optimal running track of the target vehicle in the first area according to the solving function.
In a specific embodiment of this step, if only one sampling rule is stored, this sampling rule may be used to sample the first region, to obtain a sampling result, and construct a solution space according to the sampling result. And finding solutions conforming to the solving function in the solution space, and constructing the optimal driving rule of the target vehicle based on the solutions. If multiple sampling rules are stored, determining an optimal driving track of the target vehicle in the first area according to the multiple sampling rules and the solving function.
In the embodiment of the application, before solving by using the solving function, the positions of the obstacles around the target vehicle and the speed of the target vehicle can be input into a pre-trained machine learning model to obtain a first area, and then the optimal running track of the target vehicle is determined in the first area according to the solving function. Because the obtained first area is a part of the surrounding environment and the first area is an area which does not comprise the obstacle, the embodiment of the application substantially eliminates the invalid solution corresponding to the area with the obstacle in the surrounding environment, reduces the calculated amount of calculation by using the invalid solution and shortens the calculation time.
Further, the embodiment of the present application further provides a method for determining an optimal driving track of a target vehicle, where the method specifically introduces "determining, in a first area, an optimal driving track of a target vehicle according to a solution function" in step 104 in the embodiment shown in fig. 1, and the specific steps are as shown in fig. 3, and include:
In step 201, a minimum sampling density for sampling the first region is determined from the sampling densities corresponding to the plurality of sampling rules.
Wherein, there are a plurality of sampling rules in the target vehicle, each sampling rule corresponds to a sampling density, and the sampling density is related to the initial speed of the target vehicle at the starting point and the sampling time. For example, the greater the initial velocity, the greater the sampling density, and the smaller the initial velocity, the smaller the sampling density. The larger the sampling time, the larger the sampling density, the smaller the sampling time, and the smaller the sampling density.
In a specific embodiment of this step, the sampling density corresponding to each sampling rule is determined according to the starting speed of the target vehicle and the sampling time corresponding to each sampling rule. Thus, among sampling densities corresponding to the plurality of sampling rules, a minimum sampling density for sampling the first region is determined.
Among the sampling densities corresponding to the plurality of sampling rules, the sampling rule corresponding to the sampling density larger than the minimum sampling density may not sample the first region. The fact that the sampling rule cannot sample the first area in this step means that after the first area is sampled by using the sampling rule, a sampling point is obtained in the first area, and a second sampling point cannot be obtained.
In step 202, an available sampling rule is determined from among a plurality of sampling rules based on a minimum sampling density.
The available sampling rule is a rule that can sample the first area, and the sampling rule corresponding to the minimum sampling density is smaller than or equal to the sampling rule that can sample the first area, so the available sampling rule includes a sampling rule corresponding to the minimum sampling density and a sampling rule corresponding to a sampling density smaller than the minimum sampling density.
In a specific embodiment of this step, the sampling rule corresponding to the minimum sampling density and the sampling rule corresponding to the sampling density smaller than the minimum sampling density are determined as the available sampling rules.
In step 203, an optimal driving track of the target vehicle is determined in the first area according to the available sampling rules and solving functions.
In a specific embodiment of the step, if the available sampling rule is one, a solution space can be directly obtained according to the available sampling rule and the first area, and then the solution is performed in the solution space, so that an optimal running track of the target vehicle is obtained. If the available sampling rules are multiple, a solution space is obtained according to the available sampling rules with larger sampling density and the first area, and then a smaller solution area is constructed based on a solution conforming to a solution function in the solution space. And determining the optimal driving rule of the target vehicle in a smaller solving area according to the available sampling rule and the solving function with smaller sampling density. In this way, in the above process, a smaller solving area is determined according to the available sampling rule with larger sampling density, so that the area range where the optimal running track is located can be further reduced, and the calculated amount and the calculated time for calculation by using the invalid solution are reduced.
Further, the available sampling rules determined in step 202 shown in fig. 3 may include one sampling rule or may include a plurality of sampling rules. When the determined available sampling rules comprise a plurality of sampling rules, the area where the optimal running track is located can be further reduced according to the plurality of sampling rules, and the calculated amount and the calculated time for calculating by using the invalid solution are reduced, so that the calculated amount and the calculated time are reduced on the premise of not losing the optimal running track. The embodiment of the application is illustrated by taking an example that the available sampling rules comprise two sampling rules, namely a low-density sampling rule and a high-density sampling rule. Thus, when the available sampling rules include at least a low-density sampling rule and a high-density sampling rule, step 203 "determining an optimal travel track of the target vehicle in the first region according to the available sampling rules and the solving function" shown in fig. 4 includes:
in step 301, a low density solution space is determined according to the low density sampling rule and the first region.
The low-density solution space comprises a plurality of solutions, each solution comprises position information, speed information, acceleration information, time information and the like, and each solution corresponds to one sampling point.
In a specific embodiment of the step, a low-density sampling rule is used for sampling the first region to obtain a sampling result, and a low-density solution space corresponding to the first region is constructed according to sampling points in the sampling result.
Step 302, constructing a second region according to a solution conforming to the solving function in the low-density solution space.
The second area is an area where the optimal running track of the target vehicle is located, and the second area is far smaller than the first area, and the second area determined by the method is shown in fig. 5.
In a specific embodiment of this step, since each solution corresponds to one sampling point, the corresponding sampling point can be determined according to the solution conforming to the solution function in the low-density solution space, and further, the second area including the sampling points is determined according to the sampling points.
Step 303, determining the optimal running track of the target vehicle according to the high-density sampling rule, the solving function and the second area.
In a specific embodiment of the step, the second region is sampled according to a high-density sampling rule to obtain a sampling result, and a high-density solution space is constructed according to each sampling point in the sampling result. Solutions conforming to the solving function are determined in the high-density solution space, and the optimal running track of the target vehicle is determined according to the solutions, so that the determined optimal running track of the target vehicle is specifically shown in fig. 6.
In the embodiment of the application, a low-density solution space is determined according to a low-density sampling rule and the first region, a second region is constructed according to a solution conforming to a solving function in the low-density solution space, and then an optimal running track of a target vehicle is determined according to the high-density sampling rule, the solving function and the second region. In this way, since the embodiment of the application determines the optimal driving track of the target vehicle in the second area smaller than the first area, the invalid solution in the solution space is reduced, and the calculation amount and calculation time for calculation by using the invalid solution are further reduced.
Meanwhile, when the available sampling rule determined in step 202 shown in fig. 3 includes only one sampling rule, the sampling rule can be only a high-density sampling rule. Thus, when the available sampling rules include only high-density sampling rules, the specific steps of step 203 "determining an optimal travel track of the target vehicle in the first area according to the available sampling rules and the solving function" shown in fig. 7 include:
And step 401, carrying out fine sampling on the first area according to a high-density sampling rule to obtain a high-density sampling result.
In the prior art, there is a method of performing fine sampling on the first region to obtain a high-density sampling result and determining a high-density solution space, which is not described in detail herein.
Step 402, determining a high-density solution space according to the high-density sampling result.
And step 403, determining the points included in the optimal running track according to the solution conforming to the solving function in the high-density solution space.
In the specific implementation manner of this step, since each solution corresponds to one sampling point, the sampling points corresponding to the solutions are determined according to the solutions conforming to the solution function in the high-density solution space, and these points are the points included in the optimal driving track.
And step 404, constructing the optimal running track of the target vehicle according to the points included in the optimal running track.
In a specific embodiment of this step, an optimal running track of the target vehicle is constructed according to the position information included in the points included in the optimal running track.
In the embodiment of the application, when the first area can be sampled by using the high-density sampling rule, but the first area cannot be sampled by using other sampling rules, the calculation amount based on the high-density sampling rule is smaller, and the calculation can be directly performed based on the high-density sampling rule.
In addition, in step 201 shown in fig. 3, when the minimum sampling density for sampling the first area cannot be determined from the sampling densities corresponding to the sampling rules, it is indicated that all the surrounding of the target vehicle is an obstacle, and the target vehicle cannot plan a driving route to the termination point, so that in order to ensure the safety of the target vehicle, a driver needs to be notified to take over the vehicle. Specifically, when the minimum sampling density for sampling the first region cannot be determined from among the plurality of sampling densities, alarm information is issued for prompting a driver of the target vehicle to take over the target vehicle.
Of course, when it is detected that the travel route from the target vehicle to the termination point cannot be planned, the target vehicle may be directly braked to stop the target vehicle in order to ensure the safety of the target vehicle and related personnel.
Meanwhile, in order to make no obstacle in the first area output by the pre-trained machine learning model, the embodiment of the application further provides a method for training the machine learning model, where the method is used to train the pre-set machine learning model and obtain the pre-trained machine learning model in step 102 in fig. 1, and specific steps are as shown in fig. 8, and include:
in step 501, the position of the target vehicle where surrounding obstacles are collected in various sample environments and the vehicle speed in the sample environments are obtained.
The execution of this step is similar to that of step 101 in fig. 1, and will not be described in detail here.
Step 502, determining a first sample area under each sample environment according to the position of the surrounding obstacle collected by the target vehicle under each sample environment.
Wherein the first sample region is a region excluding an obstacle.
In a specific embodiment of this step, the technician circles a region in each sample environment, which may be traveled by the target vehicle and does not include an obstacle, based on the position of the obstacle in each sample environment, with a lane line on the road where the target vehicle is located as a boundary, and uses the region as a first sample region in each sample environment.
Step 503, training the machine learning model according to the positions, the vehicle speeds and the first sample areas of the obstacles corresponding to the various sample environments, so as to obtain a pre-trained machine learning model.
In a specific embodiment of the step, one sample environment is selected randomly from a plurality of sample environments, the position and the vehicle speed of an obstacle corresponding to the sample environment are acquired, and the position and the vehicle speed are input into a machine learning model to output a first prediction area. And training the machine learning model according to the first prediction area and the first sample area to obtain a trained machine learning model, thereby completing one training. Then, training the trained machine learning model by using the method until a preset training process is completed, so as to obtain a pre-trained machine learning model.
Meanwhile, in order to make the first area output by the pre-trained machine learning model be the area where the optimal driving track is located, the embodiment of the present application further provides a method for training the machine learning model, where the method is used for training the pre-set machine learning model, and obtains the pre-trained machine learning model in step 102 in fig. 1, and specific steps are as shown in fig. 9, including:
in step 601, the position of the target vehicle where surrounding obstacles are collected in various sample environments, the vehicle speed in the sample environments, and the solution space constructed based on each sample environment are acquired.
The method for acquiring the position and speed of the target vehicle in the various sample environments in this step is shown in step 501 in fig. 8, and the method for constructing the solution space based on each sample environment is similar to steps 401 to 402 in fig. 7, and is not repeated here.
Step 602, constructing a solving function according to the starting state, the ending state and the comfort index requirement of the target vehicle.
This step is similar to step 103 described in fig. 1 and will not be described in detail here.
And 603, obtaining the optimal running track of the target vehicle in various sample environments based on the solution spaces and the solution functions corresponding to the various sample environments.
This step is similar to step 403 described in fig. 7 and will not be described in detail here.
In step 604, a first sample area in each sample environment is determined based on the optimal travel track of the target vehicle in the various sample environments.
In a specific embodiment of this step, based on the optimal running track of the target vehicle in various sample environments, the first sample area in each sample environment is determined by taking the road route of the target vehicle on the road as a node.
Step 605, training the machine learning model according to the obstacle positions, the vehicle speeds and the first sample areas corresponding to various sample environments to obtain a pre-trained machine learning model.
The implementation of this step is similar to step 503 and will not be described in detail here.
Further, as an implementation of the method embodiments shown in fig. 1 to 9, the embodiment of the present application provides a device for determining an optimal driving track of a vehicle, which can reduce the used computing resources and shorten the computing time. The embodiment of the device corresponds to the foregoing method embodiment, and for convenience of reading, details of the foregoing method embodiment are not described one by one in this embodiment, but it should be clear that the device in this embodiment can correspondingly implement all the details of the foregoing method embodiment. As shown in fig. 10, the apparatus includes:
An acquisition unit 701 for acquiring a position of an obstacle around a target vehicle and a vehicle speed of the target vehicle when an update of an optimal travel track is required;
An input unit 702, configured to input the position and the vehicle speed acquired by the acquiring unit 701 into a machine learning model trained in advance, to obtain a first area, where the first area is an area that does not include an obstacle;
A construction unit 703, configured to construct a solution function according to the starting state, the target state, and the comfort index requirement of the target vehicle;
a determining unit 704, configured to determine an optimal driving track of the target vehicle in the first area obtained by the input unit 702 according to the solving function constructed by the constructing unit 703.
Further, as shown in fig. 11, the determining unit 704 includes:
A first determining module 7041, configured to determine, among sampling densities corresponding to a plurality of sampling rules, a minimum sampling density for sampling the first area;
a second determining module 7042, configured to determine, according to the minimum sampling density determined by the first determining module 7041, an available sampling rule among a plurality of sampling rules;
a third determining module 7043, configured to determine, in the first area, an optimal driving track of the target vehicle according to the available sampling rule and the solution function determined by the second determining module 7042.
Further, as shown in fig. 11, the available sampling rules include at least a low-density sampling rule and a high-density sampling rule, and the third determining module 7043 is further configured to:
determining a low-density solution space according to the low-density sampling rule and the first region;
constructing a second region according to a solution conforming to the solving function in the low-density solution space;
and determining the optimal running track of the target vehicle according to the high-density sampling rule, the solving function and the second area.
Further, as shown in fig. 11, the plurality of sampling rules includes a high-density sampling rule, and the third determining module 7043 is further configured to:
According to the high-density sampling rule, carrying out fine sampling on the first area to obtain a high-density sampling result;
determining a high-density solution space according to the high-density sampling result;
determining the points included in the optimal running track according to the solution conforming to the solving function in the high-density solution space;
and constructing the optimal running track of the target vehicle according to the points included in the optimal running track.
Further, as shown in fig. 11, the apparatus further includes an early warning unit 705 for issuing alarm information for prompting a driver of the target vehicle to take over the target vehicle when a minimum sampling density for sampling the first region cannot be determined among a plurality of sampling densities.
Further, as shown in fig. 11, the apparatus further includes a first training unit 706, where the first training unit 706 includes:
A first acquisition module 7061 for acquiring a position where the target vehicle acquires surrounding obstacles in various sample environments and a vehicle speed in the sample environments;
a fourth determining module 7062, configured to determine a first sample area in each sample environment according to a position where the target vehicle acquired by the first acquiring module 7061 acquires a surrounding obstacle in each sample environment, where the first sample area is an area that does not include an obstacle;
the first training module 7063 is configured to train the machine learning model according to the positions and the vehicle speeds of the obstacles corresponding to the various sample environments acquired by the first acquiring module 7061 and the first sample area determined by the fourth determining module 7062, so as to obtain a pre-trained machine learning model.
Further, as shown in fig. 11, the apparatus further includes a second training unit 707, where the second training unit 707 includes:
A second acquisition module 7071 for acquiring positions where the target vehicle acquired surrounding obstacles in various sample environments, vehicle speeds in the sample environments, and a solution space constructed based on each sample environment;
a construction module 7072, configured to construct a solution function according to the starting state, the target state, and the comfort index requirement of the target vehicle;
A fifth determining module 7073, configured to determine an optimal driving track of the target vehicle in various sample environments based on the solution spaces corresponding to the various sample environments acquired by the second acquiring module 7071 and the solution function constructed by the constructing module 7072;
a sixth determining module 7074 configured to determine a first sample area in each sample environment based on the optimal running track of the target vehicle in the various sample environments determined by the fifth determining module 7073;
The second training module 7075 is configured to train the machine learning model according to the obstacle positions, the vehicle speeds and the first sample areas determined by the sixth determining module 7074 corresponding to the various sample environments acquired by the second acquiring module 7071, so as to obtain a pre-trained machine learning model.
Further, an embodiment of the present application further provides a processor, where the processor is configured to execute a program, where the program executes the method for determining the optimal driving track of the vehicle described in fig. 1 to 9.
Further, an embodiment of the present application further provides a storage medium, where the storage medium is configured to store a computer program, where the computer program controls a device where the storage medium is located to execute the method for determining the optimal driving track of the vehicle described in fig. 1 to 9.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
It will be appreciated that the relevant features of the methods and apparatus described above may be referenced to one another. In addition, the "first", "second", and the like in the above embodiments are for distinguishing the embodiments, and do not represent the merits and merits of the embodiments.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not described in detail herein.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, the present application is not directed to any particular programming language. It will be appreciated that the teachings of the present application described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present application.
Furthermore, the memory may include volatile memory, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), in a computer readable medium, the memory including at least one memory chip.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or 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, embedded processor, 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (9)
1. A method of determining an optimal driving trajectory for a vehicle, the method comprising:
when the optimal running track is required to be updated, the positions of obstacles around the target vehicle and the speed of the target vehicle are acquired;
Inputting the position and the vehicle speed into a pre-trained machine learning model to obtain a first area, wherein the first area is an area which does not comprise an obstacle;
constructing a solving function according to the starting state, the ending state and the comfort index requirement of the target vehicle;
According to the solving function, in the first area, determining an optimal running track of the target vehicle comprises: determining a minimum sampling density for sampling the first area in sampling densities corresponding to a plurality of sampling rules; determining an available sampling rule among a plurality of sampling rules according to the minimum sampling density; and determining the optimal running track of the target vehicle in the first area according to the available sampling rule and the solving function.
2. The method of claim 1, wherein the available sampling rules include at least a low density sampling rule and a high density sampling rule;
The determining, in the first region, an optimal driving track of the target vehicle according to the available sampling rule and the solving function, including:
determining a low-density solution space according to the low-density sampling rule and the first region;
constructing a second region according to a solution conforming to the solving function in the low-density solution space;
and determining the optimal running track of the target vehicle according to the high-density sampling rule, the solving function and the second area.
3. The method of claim 1, wherein the plurality of sampling rules comprises a high density sampling rule;
The determining, in the first region, an optimal driving track of the target vehicle according to the available sampling rule and the solving function, including:
According to the high-density sampling rule, carrying out fine sampling on the first area to obtain a high-density sampling result;
determining a high-density solution space according to the high-density sampling result;
determining the points included in the optimal running track according to the solution conforming to the solving function in the high-density solution space;
and constructing the optimal running track of the target vehicle according to the points included in the optimal running track.
4. The method according to claim 1, wherein the method further comprises:
and when the minimum sampling density for sampling the first area cannot be determined in a plurality of sampling densities, sending out alarm information, wherein the alarm information is used for prompting a driver of the target vehicle to take over the target vehicle.
5. The method according to claim 1, wherein the method further comprises:
Acquiring the positions of surrounding obstacles collected by the target vehicle in various sample environments and the vehicle speed in the sample environments;
Determining a first sample area in each sample environment according to the position of surrounding obstacles collected by the target vehicle in each sample environment, wherein the first sample area is an area which does not comprise the obstacles;
And training the machine learning model according to the positions, the vehicle speeds and the first sample areas of the obstacles corresponding to various sample environments to obtain a pre-trained machine learning model.
6. The method according to claim 1, wherein the method further comprises:
acquiring positions of surrounding obstacles acquired by the target vehicle in various sample environments, vehicle speeds in the sample environments and a solution space constructed based on each sample environment;
constructing a solving function according to the starting state, the ending state and the comfort index requirement of the target vehicle;
Determining the optimal running track of the target vehicle under various sample environments based on solution spaces and solving functions corresponding to the various sample environments;
determining a first sample area under each sample environment based on the optimal running track of the target vehicle under various sample environments;
And training the machine learning model according to the obstacle positions, the vehicle speeds and the first sample areas corresponding to various sample environments to obtain a pre-trained machine learning model.
7. An apparatus for determining an optimal travel path for a vehicle, the apparatus comprising:
An acquisition unit configured to acquire a position of an obstacle around a target vehicle and a vehicle speed of the target vehicle when an optimal running track needs to be updated;
The input unit is used for inputting the position and the vehicle speed acquired by the acquisition unit into a pre-trained machine learning model to obtain a first area, wherein the first area is an area which does not comprise an obstacle;
The construction unit is used for constructing a solving function according to the starting state, the ending state and the comfort index requirement of the target vehicle;
the determining unit is used for determining the optimal running track of the target vehicle in the first area obtained by the input unit according to the solving function constructed by the constructing unit;
Wherein the determining unit includes:
The first determining module is used for determining the minimum sampling density for sampling the first area in the sampling densities corresponding to the sampling rules;
the second determining module is used for determining available sampling rules in a plurality of sampling rules according to the minimum sampling density;
And the third determining module is used for determining the optimal running track of the target vehicle in the first area according to the available sampling rule and the solving function.
8. A terminal for running a program, wherein the terminal executes the method for determining an optimal running track of a vehicle according to any one of claims 1 to 6 when running.
9. A storage medium for storing a computer program, wherein the computer program when run controls a device in which the storage medium is located to perform the method of determining an optimal driving trajectory of a vehicle according to any one of claims 1 to 6.
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| CN202210422535.2A CN114815825B (en) | 2022-04-21 | 2022-04-21 | Method and device for determining optimal running track of vehicle |
| PCT/CN2022/115688 WO2023201952A1 (en) | 2022-04-21 | 2022-08-30 | Method and apparatus for determining optimal traveling trajectory of vehicle |
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