CN115848365A - Vehicle controller, vehicle and vehicle control method - Google Patents
Vehicle controller, vehicle and vehicle control method Download PDFInfo
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Abstract
The application provides a vehicle controller, a vehicle and a vehicle control method, and relates to the technical field of vehicles. Wherein, the vehicle controller includes processing module and control module, processing module with control module links, wherein: the processing module is used for: determining a sampling point area for vehicle path planning based on a kinematic model of a vehicle, and determining a plurality of target sampling points in the sampling point area according to the obstacle condition in the driving range of the vehicle; determining a plurality of paths based on the plurality of target sampling points, and selecting a target path from the plurality of paths; the control module is used for: and controlling the vehicle to run according to the target path. The method and the device can reduce time consumption of path planning.
Description
Technical Field
The application relates to the technical field of vehicles, in particular to a vehicle controller, a vehicle and a vehicle control method.
Background
With the development of the automatic driving technology, how to effectively avoid obstacles and reasonably plan a driving path is the key of the automatic driving technology. Dynamic planning is a typical method that can be used for path planning. When the path planning is performed based on the dynamic planning method, the acquisition of the sampling points is a key step. At present, path planning is usually performed by a Dijkstra algorithm based on graph search, and the time consumption for performing the path planning is long.
Disclosure of Invention
The application provides a vehicle controller, a vehicle and a vehicle control method.
According to a first aspect of the present application, there is provided a vehicle controller comprising a processing module and a control module, the processing module and the control module being connected, wherein:
the processing module is used for: determining a sampling point area for vehicle path planning based on a kinematic model of a vehicle, and determining a plurality of target sampling points in the sampling point area according to the obstacle condition in the driving range of the vehicle; determining a plurality of paths based on the plurality of target sampling points, and selecting a target path from the plurality of paths;
the control module is used for: and controlling the vehicle to run according to the target path.
According to a second aspect of the present application, there is provided a vehicle including the vehicle controller of the first aspect.
According to a third aspect of the present application, there is provided a vehicle control method including:
determining a sampling point area for vehicle path planning based on a kinematic model of a vehicle, and determining a plurality of target sampling points in the sampling point area according to the obstacle condition in the driving range of the vehicle; determining a plurality of paths based on the plurality of target sampling points, and selecting a target path from the plurality of paths;
and controlling the vehicle to run according to the target path.
According to a fourth aspect of the application, there is provided a computer program product comprising a computer program or instructions which, when executed by a processor, implements the method according to the third aspect.
In an embodiment of the present application, the processing module is configured to: determining a sampling point area for vehicle path planning based on a kinematic model of a vehicle, and determining a plurality of target sampling points in the sampling point area according to the obstacle condition in the driving range of the vehicle; determining a plurality of paths based on the plurality of target sampling points, and selecting a target path from the plurality of paths; the control module is used for: and controlling the vehicle to run according to the target path. In this way, the path planning is performed based on the kinematic model of the vehicle and the obstacle condition in the driving range of the vehicle, so that the kinematic characteristics of the vehicle and the influence of the obstacle on the sampling point are considered during the path planning, and the time consumed by the path planning can be reduced.
Drawings
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
FIG. 2 is a schematic structural diagram of a vehicle controller provided in an embodiment of the present application;
fig. 3 is a schematic diagram of a path planning according to an embodiment of the present application;
fig. 4 is a second schematic diagram of a path planning provided by the embodiment of the present application;
fig. 5 is a third schematic diagram of a path planning provided by an embodiment of the present application;
fig. 6 is a fourth schematic diagram of a path planning according to an embodiment of the present application;
fig. 7 is a fifth schematic diagram of a path planning provided by the embodiment of the present application;
FIG. 8 is a schematic flow chart diagram illustrating a vehicle control method provided by an embodiment of the present application;
fig. 9 is a schematic structural diagram of a vehicle according to an embodiment of the present application.
Detailed Description
FIG. 1 shows a schematic block diagram of an example electronic device 100 that may be used to implement embodiments of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 1, the electronic apparatus 100 includes a computing unit 101 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 102 or a computer program loaded from a storage unit 108 into a Random Access Memory (RAM) 103. In the RAM 103, various programs and data necessary for the operation of the electronic apparatus 100 can also be stored. The computing unit 101, the ROM 102, and the RAM 103 are connected to each other via a bus 104. An input/output (I/O) interface 105 is also connected to bus 104.
A number of components in the electronic device 100 are connected to the I/O interface 105, including: an input unit 106 such as a keyboard, a mouse, or the like; an output unit 107 such as various types of displays, speakers, and the like; a storage unit 108, such as a magnetic disk, optical disk, or the like; and a communication unit 109 such as a network card, modem, wireless communication transceiver, etc. The communication unit 109 allows the electronic device 100 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
It should be noted that the vehicle controller in the embodiment of the present application may be the computing unit 101 in the electronic device 100.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a vehicle controller 200 according to an embodiment of the present application, and as shown in fig. 2, the vehicle controller 200 includes a processing module 201 and a control module 202, where the processing module 201 is connected to the control module 202, where:
the processing module 201 is configured to: determining a sampling point area for vehicle path planning based on a kinematic model of a vehicle, and determining a plurality of target sampling points in the sampling point area according to the obstacle condition in the driving range of the vehicle; determining a plurality of paths based on the plurality of target sampling points, and selecting a target path from the plurality of paths;
the control module 202 is configured to: and controlling the vehicle to run according to the target path.
The obstacle condition in the driving range of the vehicle can be acquired by monitoring equipment such as a radar and a camera which are arranged on the vehicle, and/or can be acquired by monitoring equipment such as a radar and a camera which are arranged on a road, and/or can be acquired by monitoring equipment such as a radar and a camera which are arranged on other vehicles; this embodiment does not limit this.
In addition, the obstacle condition in the driving range of the vehicle may include at least one of: no obstacle exists in the driving range of the vehicle; a stationary obstacle is present within the driving range of the vehicle; there are dynamic obstacles within the driving range of the vehicle. A stationary obstacle refers to an obstacle that maintains a stationary state. A dynamic obstacle refers to an obstacle that is in motion.
In addition, the determining a sampling point region for vehicle path planning based on the vehicle kinematic model may include determining boundary sampling points corresponding to a plurality of moments after a current moment based on the vehicle kinematic model; and determining a region formed by enclosing the boundary sampling points corresponding to the plurality of moments as a sampling point region for vehicle path planning.
Illustratively, the kinematic model of the vehicle includes the following formula:
wherein x (t) is the x coordinate value of the vehicle under the geodetic coordinate system at the time of t (for example, 2s,4s,6s, 8s); x (0) is a starting point x coordinate value of the vehicle in a geodetic coordinate system; v (t) is the vehicle speed at time t;
is the vehicle course angle at the time t; />
The initial course angle of the vehicle is defined, and y (t) is a y coordinate value of the vehicle under a geodetic coordinate system at the moment t; y (0) is a coordinate value of a starting point y of the vehicle in a geodetic coordinate system; />
Is the steering angle of the vehicle; l is the vehicle axle length; a is the acceleration of the vehicle.
Illustratively, as shown in FIG. 3, in the meantimeThe acceleration of the vehicle 11 may be set to a maximum acceleration, e.g., 2 m/s, when defining the boundary sample point 12 2 The steering angle of the vehicle is the maximum steering angle, and may be designed, for example, as
When a path set with the time length of 8s at most needs to be generated, the maximum acceleration and the maximum steering angle are input into a kinematic model of the vehicle, and boundary sampling points (x (t), y (t)) at 2s,4s,6s, and 8s are calculated respectively. A sampling point region 13 is formed by enclosing boundary sampling points 12 at 2s,4s,6s and 8s.
The determining of the multiple paths based on the multiple target sampling points may be determining multiple paths based on the multiple target sampling points and the current position point of the vehicle, where the multiple paths correspond to the multiple target sampling points one to one, and as shown in fig. 4, each of the multiple paths is composed of the current position point of the vehicle 11 and the target sampling point in the sampling point area 13. And taking the current position point of the vehicle as the starting point of each path, and taking the target sampling point corresponding to each path as the end point of each path. For each target sample point and the current location point (x) of the vehicle 0 ，y 0 ) The curve can be generated based on a polynomial or a path generating function to connect the current position point of the vehicle and the target sampling points to obtain a path corresponding to each target sampling point. Illustratively, for each target sample point and the current location point (x) of the vehicle 0 ，y 0 ) And fifthly polynomials can be adopted for connection to obtain the path corresponding to each target sampling point.
In the related art, there are many algorithms related to automatic driving path planning, such as an artificial potential field method, RRT based on sampling, PRM based on sampling combined with graph search, dijkstra based on graph search, optimization algorithm based on reference line planning, and so on. There are many algorithms for generating paths according to sampling points, such as RRT algorithm, PRM algorithm, and so on. However, these algorithms have certain disadvantages, such as too long planning time in complex scenes, and the planned path does not conform to the kinematics of the vehicle. According to the embodiment of the application, dynamic sampling driving path planning based on the time-space domain is realized, on the basis of the sampling path planning method, the influence of the kinematics and time of the vehicle and the motion states of other obstacles on the sampling point is considered, efficient point scattering sampling is carried out under the time-space domain, the time consumption based on the sampling path planning can be reduced, and the comfort and effectiveness based on the sampling path planning are improved.
In an embodiment of the present application, the processing module is configured to: determining a sampling point area for vehicle path planning based on a kinematic model of a vehicle, and determining a plurality of target sampling points in the sampling point area according to the obstacle condition in the driving range of the vehicle; determining a plurality of paths based on the plurality of target sampling points, and selecting a target path from the plurality of paths; the control module is used for: and controlling the vehicle to run according to the target path. In this way, the path planning is performed based on the kinematic model of the vehicle and the obstacle condition in the driving range of the vehicle, so that the kinematic characteristics of the vehicle and the influence of the obstacle on the sampling point are considered during the path planning, and the time consumed by the path planning can be reduced.
Optionally, the processing module is specifically configured to at least one of:
under the condition that the obstacle condition in the driving range of the vehicle is no obstacle, determining a plurality of target sampling points in the sampling point area based on a first preset acceleration value and a kinematic model of the vehicle;
determining a plurality of target sampling points in the sampling point area based on a second preset acceleration value, a kinematic model of the vehicle and the position of the static obstacle under the condition that the obstacle condition in the driving range of the vehicle is that the static obstacle exists;
and when the obstacle condition in the driving range of the vehicle is that a dynamic obstacle exists, determining a plurality of target sampling points in the sampling point area based on the predicted motion trail of the dynamic obstacle.
In this embodiment, when the obstacle condition in the driving range of the vehicle is no obstacle, a static obstacle or a dynamic obstacle, different strategies are respectively adopted to generate a plurality of target sampling points in the sampling point area, so that the generated target sampling points are adapted to the scene where the vehicle is located, and the success rate of path planning can be improved.
Optionally, the processing module is specifically configured to:
and under the condition that the obstacle condition in the driving range of the vehicle is no obstacle, determining a first steering angle value based on a first preset acceleration value, and determining a plurality of target sampling points in the sampling point area based on the first steering angle value and a kinematic model of the vehicle.
Wherein the first preset acceleration value can be designed according to experience, and the first preset acceleration value a y As a comfortable lateral acceleration, for example, the first preset acceleration value may be designed to be 1 m/s according to experience 2 ，-1 m/s 2 ，0 m/s 2 The first steering angle value can be calculated according to the following formula
：
Wherein the first steering angle value
The target sampling points (x (t), y (t)) of the vehicle at a plurality of moments after the current moment can be respectively calculated by setting the acceleration of the vehicle as a first preset acceleration value and the steering angle of the vehicle as a first steering angle value, and inputting the first preset acceleration value and the first steering angle value into formulas (1) to (4) in a kinematic model of the vehicle. For example, as shown in fig. 5, when it is necessary to generate a path set whose duration is at most 8s, the first preset acceleration value and the first steering angle value are input to equations (1) to (4) in the kinematic model of the vehicle 11, and the target sampling points 14 at 2s,4s,6s, and 8s of the vehicle after the current time are calculated, respectively. Target sampling point 14 is located in the sampling point areaWithin domain 13. The target sampling points at the plurality of time instants constitute a set of positions that can be comfortably reached by the vehicle.
In this embodiment, when the obstacle condition in the driving range of the vehicle is no obstacle, the first steering angle value is determined based on the first preset acceleration value, and the plurality of target sampling points in the sampling point region are determined based on the first steering angle value and the kinematic model of the vehicle, so that for a scene in which there is no obstacle in the driving range of the vehicle, the kinematics of the vehicle is considered in generating the sampling points, and the first preset acceleration value can be set to a value of comfortable lateral acceleration, so that the sampling points can be restrained according to the value of comfortable lateral acceleration, thereby improving the comfort of the output path.
Optionally, the processing module is specifically configured to:
determining a second steering angle value based on a second preset acceleration value under the condition that the obstacle condition in the driving range of the vehicle is that a static obstacle exists, and determining a plurality of first sampling points in the sampling point area based on the second steering angle value and a kinematic model of the vehicle;
determining a plurality of target sampling points in the sampling point area based on the plurality of first sampling points and the position of the static obstacle.
Wherein the second preset acceleration value can be designed empirically, the second preset acceleration value a y As a comfortable lateral acceleration, for example, the second preset acceleration value may be designed to be 1 m/s according to experience 2 ，-1 m/s 2 ，0 m/s 2 The second steering angle value can be calculated according to the following formula
：
Wherein the second steering angle value
Can be used forAs the comfortable front wheel turning angle, when the target sampling point is determined, the acceleration of the vehicle may be set to a second preset acceleration value, the steering angle of the vehicle may be set to a second steering angle value, and the second preset acceleration value and the second steering angle value may be input to equations (1) to (4) in a kinematic model of the vehicle, and first sampling points (x (t), y (t)) of the vehicle at a plurality of times after the current time are calculated, respectively. Illustratively, when a path set having a time length of at most 8s needs to be generated, a second preset acceleration value and a second steering angle value are input to equations (1) to (4) in the kinematic model of the vehicle, and first sampling points (x (t), y (t)) at 2s,4s,6s,8s of the vehicle after the current time are calculated, respectively. The first sample point at each time instant constitutes a set of locations that the vehicle can comfortably reach.
In addition, the determining of the plurality of target sampling points in the sampling point region based on the plurality of first sampling points and the position of the stationary obstacle may be, as shown in fig. 6, removing a first sampling point, which is located at a position less than a first preset threshold from the stationary obstacle 15, from the plurality of first sampling points in the sampling point region 13, and using the removed first sampling point as the target sampling point. The first preset threshold may be designed according to actual scenes, such as 10 meters, 20 meters, or 30 meters, etc.
For a scene with a static obstacle in a vehicle driving range, sampling points are mainly generated according to a behavior decision direction, the position of the obstacle and the kinematic characteristics of the vehicle. Generating a sampling point area according to the kinematics characteristics of the vehicle; and then according to the position of the obstacle and the behavior decision direction, carrying out scattering point sampling near one side of the obstacle in the sampling point area to obtain a position set of a target sampling point.
In this embodiment, in a case where the obstacle condition in the travel range of the vehicle is the presence of a stationary obstacle, determining a second steering angle value based on a second preset acceleration value, and determining a plurality of first sampling points within the sampling point region based on the second steering angle value and a kinematic model of the vehicle; determining a plurality of target sampling points in the sampling point area based on the plurality of first sampling points and the position of the static obstacle. Therefore, for a scene with a static obstacle in a vehicle driving range, the position of the obstacle and the kinematic characteristics of the vehicle are considered in the generation of the sampling points, so that the selection of the sampling points is restricted in a target area range, the generation of unnecessary sampling points is reduced, and the planning efficiency of path planning is improved.
Optionally, the processing module is specifically configured to:
determining a plurality of second sampling points in the sampling point area based on the predicted movement track of the dynamic obstacle when the obstacle condition in the driving range of the vehicle is that the dynamic obstacle exists;
and determining a plurality of target sampling points in the sampling point area based on the predicted motion track of the vehicle and the plurality of second sampling points.
As shown in fig. 7, positions of the dynamic obstacle at a plurality of times (e.g., t0, t1, t2, t 3) after the current time may be predicted based on the predicted motion trajectory of the dynamic obstacle, and a plurality of second sampling points within the sampling point area may be determined according to the predicted positions of the dynamic obstacle. The method can perform scattering point sampling according to the predicted position of the dynamic obstacle, and determine a plurality of second sampling points in the sampling point area. For example, as shown in fig. 7, a position point that is a second preset threshold from the predicted position of the dynamic obstacle 16 may be taken as a second sampling point. The second preset threshold may be designed according to the actual scene, for example, 10 meters, 20 meters, or 30 meters, etc.
In addition, the position of the vehicle at a plurality of times after the current time may be predicted based on the predicted movement trajectory of the vehicle. And if the predicted position of the vehicle is near the second sampling point, for example, the distance between the second sampling point and the second sampling point is less than a third preset threshold value, rejecting the second sampling point, and determining a plurality of second sampling points after rejection processing as target sampling points. The third preset threshold may be designed according to the actual scene, such as 2 meters, 5 meters, or 10 meters, and so on.
In a scene with a dynamic obstacle in the vehicle driving range, the sampling points are mainly generated according to the kinematic characteristics of the vehicle and the motion track of the dynamic obstacle. Generating a sampling point area according to the kinematics characteristic of the vehicle; according to the motion position of the dynamic barrier at the time t (for example, 2s,4s,6s and 8s), performing scattering sampling to obtain a position set of a plurality of second sampling points; then judging whether the vehicle can reach the vicinity of the sampling point at the time of t or not according to the motion state of the vehicle, if so, rejecting the sampling point, and otherwise, reserving the sampling point; and obtaining a position set of the target sampling point after the elimination processing. Therefore, point scattering sampling is carried out according to the motion state of the obstacle and the motion state of the vehicle, and the safety and the reasonability of path planning are improved.
In this embodiment, when the obstacle condition in the travel range of the vehicle is the presence of a dynamic obstacle, a plurality of second sampling points within the sampling point area are determined based on the predicted movement trajectory of the dynamic obstacle; and determining a plurality of target sampling points in the sampling point area based on the predicted motion track of the vehicle and the plurality of second sampling points. Therefore, for a scene with a dynamic obstacle in a vehicle driving range, the generation of the sampling point takes the kinematics of the vehicle and the motion trail of the dynamic obstacle into consideration, and the reasonability of the generation of the sampling point is monitored, so that the planning efficiency of path generation is improved, and the safety of an output path is also improved.
Optionally, the processing module is specifically configured to:
respectively determining boundary sampling points corresponding to a plurality of moments after the current moment based on a kinematic model of the vehicle, the maximum steering angle value and the maximum acceleration value;
and determining a region formed by enclosing the boundary sampling points corresponding to the plurality of moments as a sampling point region for vehicle path planning.
The formula for determining the boundary sampling points corresponding to a plurality of moments after the current moment based on the kinematic model, the maximum steering angle value and the maximum acceleration value of the vehicle may be as follows:
wherein x (t) is the x coordinate value of the vehicle under the geodetic coordinate system at the time of t (for example, 2s,4s,6s, 8s); x (0) is a starting point x coordinate value of the vehicle in a geodetic coordinate system; v (t) is the vehicle speed at time t;
is the vehicle course angle at the time t; />
The initial course angle of the vehicle is defined, and y (t) is a y coordinate value of the vehicle under a geodetic coordinate system at the moment t; y (0) is a coordinate value of a starting point y of the vehicle in a geodetic coordinate system; />
Is the maximum speed of the vehicle, can be ^ based>
；/>
For the maximum steering angle of the vehicle, it can be designed, for example, as->
(ii) a L is the vehicle axle length; />
For maximum acceleration of the vehicle, it may be designed, for example, to be 2 m/s 2 。
In the embodiment, boundary sampling points corresponding to a plurality of moments after the current moment are respectively determined based on a kinematic model of a vehicle, a maximum steering angle value and a maximum acceleration value; and determining a region formed by enclosing the boundary sampling points corresponding to the moments as a sampling point region for planning a vehicle path. Therefore, the sampling point area is determined according to the vehicle kinematics characteristic, effective constraint is carried out on the sampling points, and the path planning efficiency can be improved.
Optionally, the processing module is specifically configured to:
inputting the maximum steering angle value and the maximum acceleration value into a course angle calculation module of a kinematic model of a vehicle to obtain a maximum course angle value of the vehicle;
inputting the maximum course angle value, the maximum acceleration value and a first time length into a coordinate calculation module of the kinematics model to obtain a vehicle coordinate value corresponding to the first time, wherein the first time length is a time interval between the first time and the current time, and the first time is any one of a plurality of times after the current time;
and determining boundary sampling points corresponding to the plurality of moments based on the vehicle coordinate values corresponding to the first moment.
Wherein the plurality of moments after the current moment may include a moment 2s after the current moment, a moment 4s after the current moment, a moment 6s after the current moment, a moment 8s after the current moment, and the like; alternatively, the plurality of times after the current time may include a time 3s after the current time, a time 6s after the current time, a time 9s after the current time, a time 12s after the current time, and the like; alternatively, the plurality of times after the current time may include a time 4s after the current time, a time 8s after the current time, a time 12s after the current time, a time 16s after the current time, a time 20s after the current time, and the like; this embodiment does not limit this.
In addition, the heading angle calculation module of the kinematic model of the vehicle may include formula (7), and the coordinate calculation module of the kinematic model of the vehicle may include formula (8), formula (5), and formula (6). The maximum steering angle value and the maximum acceleration value may be input to equation (7) to obtain theA maximum heading angle value of the vehicle, wherein in equation (7):
(ii) a The maximum acceleration value and the first time length can be input into formula (8) to obtain v (t); and inputting the v (t), the maximum course angle value and the first time length into the formula (5) and the formula (6) to obtain a vehicle coordinate value corresponding to the first time.
In the embodiment, the maximum steering angle value and the maximum acceleration value are input into a course angle calculation module of a kinematic model of a vehicle to obtain the maximum course angle value of the vehicle; inputting the maximum course angle value, the maximum acceleration value and a first time length into a coordinate calculation module of the kinematics model to obtain a vehicle coordinate value corresponding to the first time, wherein the first time length is a time interval between the first time and the current time, and the first time is any one of a plurality of times after the current time; and determining boundary sampling points corresponding to the plurality of moments based on the vehicle coordinate values corresponding to the first moment. Therefore, the boundary sampling points can be determined through the kinematic model of the vehicle, and the sampling point area formed by enclosing the boundary sampling points accords with the kinematic characteristics of the vehicle.
Optionally, the processing module is specifically configured to:
determining an evaluation index value of each of the plurality of paths based on at least one of a first index value, a second index value and a third index value of each of the plurality of paths, the first index value being used for characterizing the degree of lateral deviation of the path relative to the current position of the vehicle, the second index value being used for characterizing the comfort level of the path, and the third index value being used for characterizing the degree of longitudinal deviation of the path relative to the expected longitudinal length;
and selecting a target path based on the evaluation index value of each path.
Wherein the first index value may be determined based on the path lateral shift amount.
In one embodiment, the calculation formula of the first index value offset _ cost may be as follows:
wherein, offset is a path lateral offset, and offset _ weight is a predetermined penalty weight for the path lateral offset.
Wherein the second index value comfort _ cost may be determined based on a second derivative of the lateral path offset with respect to the longitudinal distance and a first derivative of the lateral path offset with respect to the longitudinal distance.
Wherein the third index value may be determined based on a difference between the longitudinal distance and the expected longitudinal length.
In one embodiment, the calculation formula of the third index value length _ cost may be as follows:
the path _ length is a longitudinal distance, the target _ length is an expected longitudinal length, and the length _ weight is a preset penalty weight for longitudinal deviation of the path.
In one embodiment, the evaluation index value total _ cost of each path may be: total _ cost = offset _ cost + comfort _ cost + length _ cost.
The target path may be a path with the smallest evaluation index value among the plurality of paths.
In the embodiment, an evaluation index value of each path is determined based on at least one of a first index value, a second index value and a third index value of each path in the plurality of paths, wherein the first index value is used for representing the transverse deviation degree of the path relative to the current position of the vehicle, the second index value is used for representing the comfort degree of the path, and the third index value is used for representing the longitudinal deviation degree of the path relative to the expected longitudinal length; and selecting a target path based on the evaluation index value of each path. In this way, the plurality of paths are evaluated in consideration of at least one of the lateral deviation degree, the longitudinal deviation degree and the path comfort degree, and the target path is selected from the plurality of paths, so that the rationality of path planning can be improved.
Optionally, the processing module is further specifically configured to:
determining a second derivative of the path transverse offset of each path to the longitudinal distance;
determining a first derivative of the path lateral offset to the longitudinal distance;
determining the second index value based on a product of a square value of the longitudinal speed of the vehicle and the second derivative and a product of the longitudinal acceleration of the vehicle and the first derivative.
In one embodiment, the second index value comfort _ cost may be calculated as follows:
wherein,
approximating a lateral acceleration value for a second derivative of the path lateral offset to the longitudinal distance; />
Is the longitudinal velocity; />
Approximating a lateral velocity value for a first derivative of the path lateral offset to the longitudinal distance; />
Is the longitudinal acceleration; comfort _ weight is a preset weight.
In this embodiment, a second derivative of the path lateral offset of each path with respect to the longitudinal distance is determined; determining a first derivative of the path lateral offset to the longitudinal distance; determining the second index value based on a product of a square of a longitudinal speed of the vehicle and the second derivative and a product of the longitudinal speed and the first derivative. In this way, the comfort level of the selected target route can be made higher by the second index value.
According to an embodiment of the present application, there is also provided a vehicle control method, as shown in fig. 8, including the steps of:
step 301: determining a sampling point area for vehicle path planning based on a kinematic model of a vehicle, and determining a plurality of target sampling points in the sampling point area according to the obstacle condition in the driving range of the vehicle; determining a plurality of paths based on the plurality of target sampling points, and selecting a target path from the plurality of paths;
step 302: and controlling the vehicle to run according to the target path.
Optionally, the determining a plurality of target sampling points in the sampling point region according to the obstacle condition in the driving range of the vehicle includes:
under the condition that the obstacle condition in the driving range of the vehicle is no obstacle, determining a plurality of target sampling points in the sampling point area based on a first preset acceleration value and a kinematic model of the vehicle;
determining a plurality of target sampling points in the sampling point area based on a second preset acceleration value, a kinematic model of the vehicle and the position of the static obstacle under the condition that the obstacle condition in the driving range of the vehicle is that the static obstacle exists;
and when the obstacle condition in the driving range of the vehicle is that a dynamic obstacle exists, determining a plurality of target sampling points in the sampling point area based on the predicted motion track of the dynamic obstacle.
Optionally, the determining, when the obstacle condition in the driving range of the vehicle is no obstacle, a plurality of target sampling points in the sampling point region based on a first preset acceleration value and a kinematic model of the vehicle includes:
and under the condition that the obstacle condition in the driving range of the vehicle is no obstacle, determining a first steering angle value based on a first preset acceleration value, and determining a plurality of target sampling points in the sampling point area based on the first steering angle value and a kinematic model of the vehicle.
Optionally, the determining, when the obstacle condition in the driving range of the vehicle is the presence of a stationary obstacle, a plurality of target sampling points in the sampling point region based on a second preset acceleration value, a kinematic model of the vehicle, and a position of the stationary obstacle includes:
determining a second steering angle value based on a second preset acceleration value under the condition that the obstacle condition in the driving range of the vehicle is that a static obstacle exists, and determining a plurality of first sampling points in the sampling point area based on the second steering angle value and a kinematic model of the vehicle;
determining a plurality of target sampling points in the sampling point area based on the plurality of first sampling points and the position of the static obstacle.
Optionally, the determining, in a case that the obstacle condition in the driving range of the vehicle is that a dynamic obstacle exists, a plurality of target sampling points in the sampling point area based on the predicted motion trajectory of the dynamic obstacle includes:
determining a plurality of second sampling points in the sampling point area based on the predicted movement track of the dynamic obstacle when the obstacle condition in the driving range of the vehicle is that the dynamic obstacle exists;
and determining a plurality of target sampling points in the sampling point area based on the predicted motion track of the vehicle and the plurality of second sampling points.
Optionally, the determining a sampling point region for vehicle path planning based on the vehicle kinematics model includes:
respectively determining boundary sampling points corresponding to a plurality of moments after the current moment based on a kinematic model, a maximum steering angle value and a maximum acceleration value of the vehicle;
and determining a region formed by enclosing the boundary sampling points corresponding to the plurality of moments as a sampling point region for vehicle path planning.
Optionally, the determining, based on the kinematic model of the vehicle, the maximum steering angle value, and the maximum acceleration value, boundary sampling points corresponding to a plurality of moments after the current moment respectively includes:
inputting the maximum steering angle value and the maximum acceleration value into a course angle calculation module of a kinematic model of a vehicle to obtain a maximum course angle value of the vehicle;
inputting the maximum course angle value, the maximum acceleration value and a first time length into a coordinate calculation module of the kinematic model to obtain a vehicle coordinate value corresponding to the first time, wherein the first time length is a time interval between the first time and the current time, and the first time is any one of a plurality of times after the current time;
and determining boundary sampling points corresponding to the plurality of moments based on the vehicle coordinate values corresponding to the first moment.
Optionally, the determining a plurality of paths based on the plurality of target sampling points and selecting a target path from the plurality of paths includes:
determining an evaluation index value of each of the plurality of paths based on at least one of a first index value, a second index value and a third index value of each of the plurality of paths, the first index value being used for characterizing the degree of lateral deviation of the path relative to the current position of the vehicle, the second index value being used for characterizing the comfort level of the path, and the third index value being used for characterizing the degree of longitudinal deviation of the path relative to the expected longitudinal length;
and selecting a target path based on the evaluation index value of each path.
Optionally, before determining the evaluation index value of each of the plurality of paths based on at least one of the first index value, the second index value, and the third index value of each of the plurality of paths, the method further includes:
determining a second derivative of the path transverse offset of each path to the longitudinal distance;
determining a first derivative of the path lateral offset to the longitudinal distance;
determining the second index value based on a product of a square value of the longitudinal speed of the vehicle and the second derivative and a product of the longitudinal acceleration of the vehicle and the first derivative.
According to the technical scheme, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and the customs of public sequences is not violated.
According to an embodiment of the present application, there is also provided a computer program product comprising a computer program or instructions which, when executed by a processor, implements the vehicle control method in an embodiment of the present application.
The vehicle control methods of the present application may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described herein are performed in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, a network appliance, a user equipment, a core network appliance, an OAM, or other programmable device.
The computer program or instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer program or instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that integrates one or more available media. The usable medium may be a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape; optical media such as digital video disks; but also semiconductor media such as solid state disks. The computer readable storage medium may be volatile or nonvolatile storage medium, or may include both volatile and nonvolatile types of storage media.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
According to an embodiment of the present application, there is also provided a vehicle including the vehicle controller according to the embodiment of the present application, and optionally, as shown in fig. 9, the vehicle 400 may include a calculation unit 401, a ROM402, a RAM403, a bus 404, an I/O interface 405, an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. For the specific implementation of the above parts, reference may be made to the description of the parts of the electronic device in the above embodiments, and details are not described herein again to avoid repetition.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (11)
1. A vehicle controller, comprising a processing module and a control module, the processing module and the control module being connected, wherein:
the processing module is used for: determining a sampling point area for vehicle path planning based on a kinematic model of a vehicle, and determining a plurality of target sampling points in the sampling point area according to the obstacle condition in the driving range of the vehicle; determining a plurality of paths based on the plurality of target sampling points, and selecting a target path from the plurality of paths;
the control module is used for: and controlling the vehicle to run according to the target path.
2. The vehicle controller of claim 1, wherein the processing module is specifically configured to at least one of:
under the condition that the obstacle condition in the driving range of the vehicle is no obstacle, determining a plurality of target sampling points in the sampling point area based on a first preset acceleration value and a kinematic model of the vehicle;
determining a plurality of target sampling points in the sampling point area based on a second preset acceleration value, a kinematic model of the vehicle and the position of the static obstacle under the condition that the obstacle condition in the driving range of the vehicle is that the static obstacle exists;
and when the obstacle condition in the driving range of the vehicle is that a dynamic obstacle exists, determining a plurality of target sampling points in the sampling point area based on the predicted motion track of the dynamic obstacle.
3. The vehicle controller of claim 2, wherein the processing module is specifically configured to:
and under the condition that the obstacle condition in the driving range of the vehicle is no obstacle, determining a first steering angle value based on a first preset acceleration value, and determining a plurality of target sampling points in the sampling point area based on the first steering angle value and a kinematic model of the vehicle.
4. The vehicle controller of claim 2, wherein the processing module is specifically configured to:
determining a second steering angle value based on a second preset acceleration value under the condition that the obstacle condition in the driving range of the vehicle is that a static obstacle exists, and determining a plurality of first sampling points in the sampling point area based on the second steering angle value and a kinematic model of the vehicle;
determining a plurality of target sampling points within the sampling point area based on the plurality of first sampling points and the position of the stationary obstacle.
5. The vehicle controller of claim 2, wherein the processing module is specifically configured to:
determining a plurality of second sampling points in the sampling point area based on the predicted motion track of the dynamic obstacle when the obstacle condition in the driving range of the vehicle is that the dynamic obstacle exists;
determining a plurality of target sampling points within the sampling point region based on the predicted motion trajectory of the vehicle and the plurality of second sampling points.
6. The vehicle controller of claim 1, wherein the processing module is specifically configured to:
respectively determining boundary sampling points corresponding to a plurality of moments after the current moment based on a kinematic model of the vehicle, the maximum steering angle value and the maximum acceleration value;
and determining a region formed by enclosing the boundary sampling points corresponding to the plurality of moments as a sampling point region for vehicle path planning.
7. The vehicle controller of claim 6, wherein the processing module is specifically configured to:
inputting the maximum steering angle value and the maximum acceleration value into a course angle calculation module of a kinematic model of a vehicle to obtain a maximum course angle value of the vehicle;
inputting the maximum course angle value, the maximum acceleration value and a first time length into a coordinate calculation module of the kinematic model to obtain a vehicle coordinate value corresponding to the first time, wherein the first time length is a time interval between the first time and the current time, and the first time is any one of a plurality of times after the current time;
and determining boundary sampling points corresponding to the plurality of moments based on the vehicle coordinate values corresponding to the first moment.
8. The vehicle controller of claim 1, wherein the processing module is specifically configured to:
determining an evaluation index value of each of the plurality of paths based on at least one of a first index value, a second index value and a third index value of each of the plurality of paths, the first index value being used for characterizing a lateral deviation degree of the path relative to a current position of the vehicle, the second index value being used for characterizing a comfort degree of the path, and the third index value being used for characterizing a longitudinal deviation degree of the path relative to an expected longitudinal length;
and selecting a target path based on the evaluation index value of each path.
9. The vehicle controller of claim 8, wherein the processing module is further configured to:
determining a second derivative of the path transverse offset of each path to the longitudinal distance;
determining a first derivative of the path lateral offset to the longitudinal distance;
determining the second index value based on a product of a square value of the longitudinal speed of the vehicle and the second derivative and a product of the longitudinal acceleration of the vehicle and the first derivative.
10. A vehicle characterized in that the vehicle includes a vehicle controller according to any one of claims 1 to 9.
11. A vehicle control method, characterized by comprising:
determining a sampling point area for vehicle path planning based on a kinematic model of a vehicle, and determining a plurality of target sampling points in the sampling point area according to the obstacle condition in the driving range of the vehicle; determining a plurality of paths based on the plurality of target sampling points, and selecting a target path from the plurality of paths;
and controlling the vehicle to run according to the target path.
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