CN117091615A - Path planning method, path planning device, vehicle and storage medium - Google Patents
Path planning method, path planning device, vehicle and storage medium Download PDFInfo
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- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
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- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The application discloses a path planning method, a path planning device, a vehicle and a storage medium, wherein the method comprises the following steps: acquiring a plurality of sampling points; under a Cartesian coordinate system, determining path boundary information corresponding to a plurality of sampling points respectively, wherein the path boundary information corresponding to the sampling points comprises a first plane area determined based on the sampling points; determining a target obstacle; determining obstacle boundary information corresponding to the target obstacle under Cartesian coordinates, wherein the obstacle boundary information corresponding to the target obstacle comprises a second plane area determined based on the target obstacle; determining a target running space based on path boundary information and barrier boundary information respectively corresponding to the plurality of sampling points; and determining a planned path based on the target running space, the preset constraint condition and the preset optimization target. The technical scheme provided by the embodiment of the application can avoid the condition that one coordinate corresponds to a plurality of track points, so that the accuracy of path planning can be improved.
Description
Technical Field
The present application relates to the field of automatic driving technologies, and in particular, to a vehicle driving track tracking method, a control system, a vehicle, and a storage medium.
Background
The main function of path planning is to find a collision-free safety path from a starting point to an end point in a region within a specified range, and the collision-free safety path has a very important role in the technical field of automatic driving.
The path planning method provided by the related art is as follows: firstly, a vehicle and an environment are mapped to a Frenet coordinate system from a Cartesian coordinate system, then a road boundary is determined based on the Frenet coordinate system, a linear dynamic programming method is utilized, the path nearest obtained by linear dynamic programming with minimum track slope, minimum curvature and comfortable riding experience is used as an optimization target, the position, first order guide, second order guide and third order guide of a piecewise five-time polynomial connection point are used as equality constraint, the natural boundary constraint of the road and the boundary constraint of an obstacle are used as inequality constraint, a mathematical model for planning the path is established, and the optimal solution of the mathematical model is obtained as a planning path.
However, in the related art, the road boundary determined based on the Frenet coordinate system is in the form of a line segment (one-dimensional case), which cannot uniquely identify a trajectory point on the road, and thus, the accuracy of path planning is low.
Disclosure of Invention
The embodiment of the application provides a path planning method, a path planning device, a vehicle and a storage medium.
In a first aspect, some embodiments of the present application provide a path planning method, including: acquiring a plurality of sampling points; under a Cartesian coordinate system, determining path boundary information corresponding to a plurality of sampling points respectively, wherein the path boundary information corresponding to the sampling points comprises a first plane area determined based on the sampling points; determining a target obstacle; determining obstacle boundary information corresponding to the target obstacle under Cartesian coordinates, wherein the obstacle boundary information corresponding to the target obstacle comprises a second plane area determined based on the target obstacle; determining a target running space based on path boundary information and barrier boundary information respectively corresponding to the plurality of sampling points; and determining a planned path based on the target running space, the preset constraint condition and the preset optimization target.
In a second aspect, some embodiments of the present application provide a path planning apparatus, including: the sampling point acquisition module is used for acquiring a plurality of sampling points; the first determining module is used for determining path boundary information corresponding to a plurality of sampling points respectively under a Cartesian coordinate system, wherein the path boundary information corresponding to the sampling points comprises a first plane area determined based on the sampling points; an obstacle determination module for determining a target obstacle; a second determining module, configured to determine, in cartesian coordinates, obstacle boundary information corresponding to a target obstacle, where the obstacle boundary information corresponding to the target obstacle includes a second planar area determined based on the target obstacle; the driving space determining module is used for determining a target driving space based on path boundary information and obstacle boundary information which correspond to the sampling points respectively; and the path planning module is used for determining a planned path based on the target running space, the preset constraint condition and the preset optimization target.
In a third aspect, some embodiments of the present application also provide a vehicle comprising: one or more processors, memory, and one or more applications. Wherein one or more application programs are stored in the memory and configured to be executed by the one or more processors, the one or more program configured to perform the vehicle path planning method described above.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium having computer program instructions stored thereon. Wherein the computer program instructions are executable by the processor to perform the path planning method described above.
In a fifth aspect, an embodiment of the present application further provides a computer program product, which when executed, implements the path planning method described above.
The application provides a path planning method, which describes path boundary information determined based on each sampling point through a Cartesian coordinate system, describes barrier boundary information determined based on a target barrier through the Cartesian coordinate system, determines a target running space which can be used for a vehicle to run and does not generate collision accidents based on the path boundary information and the barrier boundary information, and finally determines a planned path according to the target running space, a preset constraint condition and a preset optimized target.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 shows a comparison diagram of a Frenet coordinate system representing a track point and a cartesian coordinate system representing a track point according to an embodiment of the present application.
Fig. 2 shows a schematic diagram of a vehicle provided by an embodiment of the present application.
Fig. 3 shows a flow chart of a path planning method according to an embodiment of the present application.
Fig. 4 is a schematic diagram of acquiring sampling points according to an embodiment of the present application.
Fig. 5 is a schematic diagram of determining a target driving space according to an embodiment of the present application.
Fig. 6 is a schematic diagram of determining a planned path according to an embodiment of the present application.
Fig. 7 is a flow chart illustrating another path planning method according to an embodiment of the present application.
Fig. 8 shows a block diagram of a path planning apparatus according to an embodiment of the present application.
Fig. 9 shows a block diagram of a vehicle according to an embodiment of the present application.
Fig. 10 shows a block diagram of a computer-readable storage medium according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present application and are not to be construed as limiting the present application.
In order to enable those skilled in the art to better understand the solution of the present application, the following description will make clear and complete descriptions of the technical solution of the present application in the embodiments of the present application with reference to the accompanying drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The inventor finds that when determining a road boundary and performing subsequent path planning under the Frenet coordinate system, since the Frenet coordinate system is in a one-dimensional form, there may be a situation that one coordinate corresponds to a plurality of track points on the road, for example, see part (a) in fig. 1, and in a u-turn scene, the track point a and the track point B are both represented by coordinates (x) under the Frenet coordinate system, and the track point on the road cannot be accurately represented, so that the path planning method provided by the prior art has a problem of lower precision.
Based on the above problems, the inventor devised a new path planning method, describing path boundary information determined based on each sampling point by a cartesian coordinate system, describing obstacle boundary information determined based on an object obstacle by a cartesian coordinate system, determining an object travel space for a vehicle to travel without collision accident based on the path boundary information and the obstacle boundary information, and finally determining a planned path according to the object travel space, a preset constraint condition and a preset optimization target, wherein any track point on a road can be uniquely represented by a combination of an abscissa and an ordinate because the cartesian coordinate system is in a two-dimensional form, for example, referring to part (B) in fig. 1, in a u-turn scene, track point a is represented by (x, y 1) in the cartesian coordinate system, and track point B is represented by coordinates (x, y 2) in the cartesian coordinate system. Compared with the Frenet coordinate system, the road boundary information is described only through one coordinate value, and the embodiment of the application can avoid the condition that one coordinate corresponds to a plurality of track points, so that the accuracy of path planning can be improved.
For the purpose of illustrating the application in detail, the application environment in the examples of the application is described with reference to the accompanying drawings. Referring to fig. 2, fig. 2 illustrates an application environment of a path planning method according to an embodiment of the present application, where the method is applied to a vehicle 200, and the vehicle 200 refers to a vehicle driven or towed by a power device for passengers or for transporting articles, and includes, but is not limited to, a car, a bus, and the like. The vehicle 200 includes a control system and a vehicle implement system.
In an embodiment of the present application, the control system 220 is configured to implement a path planning function. Optionally, the steps of the control system 220 implementing the path planning function are as follows: the control system 220 determines a driving space available for driving the vehicle under a cartesian coordinate system, obtains a preset constraint condition and a preset optimization target, constructs a mathematical model for solving the planned path based on the three elements, and then solves the mathematical model to determine the planned path.
The vehicle execution system is used for driving according to the planned path. The vehicle execution system may be a vehicle chassis actuator including (Electrical Power Steering, EPS), brake control system (Electronic Stability Program, ESP), and drive control system (Vehicle Control Unit, VCU), among others.
In some embodiments, the vehicle 200 further includes a positioning device for positioning the real-time position of the vehicle 200. Alternatively, the vehicle 200 acquires the current position as the travel start point by the positioning device. Optionally, the positioning device comprises a global navigation satellite system (Global Navigation Satellite System, GNSS). GNSS is a positioning system based on satellites for high-precision radio navigation that can provide accurate actual position information to the vehicle 200 anywhere in the world and near-earth space.
In some embodiments, vehicle 200 further includes a touch screen for a user to input either the last item or both of the last item or the start item. In some embodiments, the touch screen displays a user interface with a path planning function that includes an electronic map on which a user selects either or both of a travel end point or a travel start point. In other embodiments, the user interface includes a start point input box for inputting a travel start point and an end point input box for inputting a travel end point.
As shown in fig. 3, an embodiment of the present application provides a flowchart of a path planning method. The method comprises the following steps.
In step 301, a plurality of sampling points are acquired.
And the vehicle samples on the reference path according to the preset sampling distance to obtain a plurality of sampling points. The reference path may be a lane center line of a road, a historical driving track of a vehicle, or a historical driving track of other vehicles, which is not limited in the embodiment of the present application.
The preset sampling distance is preset according to experiments or experience, and is exemplified by 5 meters. The maximum sampling distance is set according to the accuracy requirements of the planned path and the computing power of the vehicle, and is illustratively 75 meters. The number of sampling points is determined according to the maximum sampling distance and the preset sampling distance, specifically, the number of sampling points is the ratio between the maximum sampling distance and the preset sampling distance, and in combination with the above example, the number of sampling points is 75/5=25.
The flow of the vehicle determining a plurality of sampling points is as follows: the vehicle determines the starting point position of the reference route as a first sampling point, then draws an arc based on the former sampling point as a circle center and the radius as a preset sampling distance, and determines the intersection point of the arc and the reference route as a latter sampling point. It should be noted that, if a broken line exists on the reference path and the distance between the target sampling point and the broken line endpoint is smaller than the preset sampling distance, the broken line endpoint is taken as the sampling point after the target sampling point. Referring to fig. 4, a schematic diagram of acquiring sampling points according to an embodiment of the present application is shown. And after determining a first sampling point on the broken line path, drawing a circle according to a preset sampling distance, wherein the intersection point of the circle and the broken line path is the next sampling point. The black dots in fig. 4 are sampling points.
In some embodiments, the vehicle acquires a plurality of sampling points after acquiring the path planning instructions. Optionally, the central control screen of the vehicle or the user interface displayed by the vehicle navigator includes an electronic map, a driving start point input box, a driving end point input box and a starting navigation control, and if a trigger instruction for starting the navigation control is received under the condition of determining the driving start point and the driving end point, the step of acquiring a plurality of sampling points is executed. The manner of determining the driving start point includes at least one of: the method includes the steps of acquiring a running start point selected on an electronic map, acquiring the running start point input in a running start point input box, and acquiring the current position of a vehicle as the running start point through a positioning device. The determination mode of the driving end point comprises at least one of the following steps: and acquiring a driving terminal selected on the electronic map and a driving terminal input in a driving terminal input box.
In the case where the travel route between the travel start point and the travel end point is long, the vehicle generally divides the travel route into a plurality of segments, and then performs path planning for each segment separately, thereby realizing finer path planning. In this case, when the planned path corresponding to the i-th travel route is determined, a plurality of sampling points are acquired from the i+1-th travel route to determine the planned path corresponding to the i+th travel route, i being a positive integer.
Step 302, obtaining path boundary information corresponding to each of the plurality of sampling points in a cartesian coordinate system.
The Cartesian coordinate system is a two-dimensional coordinate system, the path boundary information corresponding to the sampling points comprises two dimensions of transverse and longitudinal directions under the Cartesian coordinate system, the transverse and longitudinal directions are mutually perpendicular, and the longitudinal direction is the road direction. The embodiment of the application does not limit the origin position of the Cartesian coordinate system, and only takes the origin position as the position of the first sampling point as an example for explanation.
The path boundary information corresponding to the sampling point includes a first planar region determined based on the sampling point. The embodiment of the application does not limit the shape of the first plane area, and only takes the first plane area as a rectangular area as an example for explanation.
In a scenario where the first planar area is a rectangular area, the path boundary information corresponding to the sampling point may include coordinates of a center point of the first planar area in a cartesian coordinate system, a length, a width, and the like of the first planar area. In other possible implementations, the path boundary information corresponding to the sampling point may further include coordinates of at least two vertices of the first planar area in a cartesian coordinate system. It should be noted that, if the path boundary information corresponding to the sampling point includes coordinates of two vertices of the first plane area in a cartesian coordinate system, the two vertices are not adjacent to each other. The determination process of the path boundary information corresponding to the sampling point will be described in the following embodiments.
In the embodiment of the application, the path boundary information is described by two dimensions of transverse and longitudinal directions, any track point on the road can be uniquely represented by the combination of the abscissa and the ordinate, and compared with the Frenet coordinate system which describes the path boundary information by only one coordinate value, the embodiment of the application can avoid the condition that one coordinate corresponds to a plurality of track points, thereby improving the precision of path planning.
Step 303, determining a target obstacle.
The vehicle detects an obstacle on the reference path by the obstacle detection device, and then screens out a target obstacle from the detected obstacle. The obstacle detection device includes, but is not limited to: lidar, looking around cameras, and the like.
In some embodiments, the vehicle obtains location information of each obstacle, obtains a distance between the obstacle and each sampling point for each obstacle, and determines the obstacle as a target obstacle if the minimum distance is less than or equal to a preset distance. In other embodiments, the vehicle determines obstacle boundary information based on the position information of each obstacle, and then determines an obstacle having an area larger than a preset area as a target obstacle, in which an overlap area exists between the obstacle boundary information and the path boundary information corresponding to each sampling point. The preset distance and the preset area are set according to actual requirements, and the embodiment of the application is not limited to this.
Step 304, determining obstacle boundary information corresponding to the target obstacle in a Cartesian coordinate system.
It should be noted that, in the embodiment of the present application, the sampling points and the target obstacle are in the same coordinate system, so as to simplify the subsequent calculation process. The coordinate system of the target obstacle comprises two dimensions of a transverse dimension and a longitudinal dimension, the transverse dimension is perpendicular to the longitudinal dimension, the longitudinal dimension is the road direction, and the original point position is the position of the first sampling point.
The boundary information corresponding to the target obstacle includes a second planar area determined based on the target obstacle. The shape of the second planar area in the embodiment of the present application is not limited, and only a rectangular area is taken as an example for explanation.
In the case where the second planar area is a rectangular area, the obstacle boundary information corresponding to the target obstacle may include coordinates of a center point of the second planar area in a cartesian coordinate system, a length, a width, and the like of the second planar area. In other possible implementations, the obstacle boundary information corresponding to the target obstacle may further include coordinates of at least two vertices of the second planar area in a cartesian coordinate system. If the obstacle boundary information corresponding to the target obstacle includes coordinates of two vertices of the first planar area in a cartesian coordinate system, the two vertices are not adjacent to each other. The determination process of the obstacle boundary information corresponding to the target obstacle will be described in the following embodiments.
In step 305, a target traveling space is determined based on the path boundary information and the obstacle boundary information corresponding to each of the plurality of sampling points.
The target travel space is a travel space that satisfies the travel requirement of the vehicle and that does not collide with the target obstacle. The determination process of the target travel space will be explained in the following embodiments.
Step 306, determining a planned path based on the target driving space, the preset constraint condition and the preset optimization target.
The preset constraint condition refers to a constraint condition that the planned path is expected to satisfy. The preset optimization objective refers to an objective that is expected to be achieved when driving on the planned path. The vehicle builds a planning path model based on the target running space, preset constraint conditions and preset optimization targets, and then solves the planning path model to obtain a planning path. Optionally, the vehicle solves the path planning model based on the CVXOPT optimization library to obtain one or more optimal solutions, and each optimal solution corresponds to one planning path.
It should be noted that, because the time required for solving the path planning model is very long, in order to avoid the occurrence of the situation that the main thread is blocked, the vehicle additionally opens a thread to complete the solving of the path planning model. The thread may be started after receiving the path planning instruction, or may be started after the path planning model is built, which is not limited in the embodiment of the present application.
In summary, according to the technical solution provided in the embodiments of the present application, path boundary information determined based on each sampling point is described by a cartesian coordinate system, obstacle boundary information determined based on an object obstacle is described by a cartesian coordinate system, an object running space in which a vehicle can run and no collision accident occurs is determined based on the path boundary information and the obstacle boundary information, and finally, a planned path is determined according to the object running space, a preset constraint condition and a preset optimization target.
The following describes a process of determining path boundary information corresponding to the sampling point. In an alternative embodiment provided based on the embodiment of fig. 3, step 302 may alternatively be implemented as a sub-step as follows.
In step 302a, for the ith sampling point, a first projection point of the ith sampling point on the first road boundary and a second projection point of the ith sampling point on the second road boundary are determined, where i is a positive integer.
In the case where one lane exists in a road, the first road boundary and the second road boundary are extended lines of both sides of the road. Under the condition that a plurality of lanes exist in a road, a first road boundary is an extension line of one side of the road, and a second road boundary is a lane line; the second road boundary is an extension line of one side of the road, and the first road boundary is a lane line; alternatively, the first road boundary and the second road boundary are both lane lines.
The first projection point of the ith sampling point on the first road boundary refers to a point which is on the first road boundary and is perpendicular to the first road boundary by a connecting line with the ith sampling point. The second projection point of the ith sampling point on the second road boundary refers to a point on the second road boundary and the connecting line of the ith sampling point is perpendicular to the second road boundary.
Optionally, in the case where i is 1, for the i-th sampling point, the vehicle starts searching from the set starting point of the first road boundary to obtain a first projection point of the 1-th sampling point on the first road boundary, and starts searching from the set starting point of the second road boundary to obtain a second projection point of the 1-th sampling point on the second road boundary. The setting start point of the first road boundary and the setting start point of the second road boundary are set according to experiments or experience, which is not limited in the embodiment of the present application.
Optionally, under the condition that i is greater than 1, searching on the first road boundary by taking a first projection point of the (i-1) th sampling point on the first road boundary as a starting position aiming at the i sampling point to obtain a first projection point of the i sampling point on the first road boundary; and searching on the second road boundary by taking the second projection point of the (i-1) th sampling point on the second road boundary as a starting position to obtain the second projection point of the i-th sampling point on the second road boundary. By the above method, the search from the set starting point of the first road boundary or the second road boundary is avoided, and the search efficiency of the projection points (the first projection point and the second projection point) is improved.
In step 302b, a center point corresponding to the ith sampling point is determined based on the ith sampling point, the first projection point and the second projection point.
Optionally, the vehicle first determines a midpoint of a line between the first projection point and the second projection point, and then aligns the midpoint with the ith sampling point, where the aligned point is the center point corresponding to the ith sampling point. Wherein, aligning the midpoint with the i-th sampling point means that the line connecting the midpoint with the i-th sampling point is parallel to a specified direction, and the specified direction is perpendicular to the road direction. If the midpoint and the ith sampling point are in the same rectangular coordinate system, and the vertical axis of the coordinate system is the road direction and the horizontal axis is perpendicular to the road direction, aligning the midpoint with the ith sampling point means that the ordinate of the midpoint is set as the ordinate of the ith sampling point.
Step 302c, obtaining a first constraint distance in a first direction and a second constraint distance in a second direction.
The first direction and the second direction are perpendicular to each other. Optionally, the first direction is a road direction, and the second direction is perpendicular to the road direction. The first constraint distance may be preset, and is, for example, 5 meters. The second preset distance is actually set according to the width of the vehicle, and the second constraint distance is 1.3 times the vehicle width, for example. For example, when the vehicle width is 1.5 m, the second constraint distance is 1.3×1.5=1.95 m.
In step 302d, path boundary information corresponding to the ith sampling point is determined based on the center point corresponding to the ith sampling point, the first constraint distance and the second constraint distance.
Optionally, the vehicle uses the center point corresponding to the ith sampling point as the center point of the first plane area, the first constraint distance is the length direction of the first plane area, the second constraint distance is the width direction of the second plane area, and finally the first plane area is determined.
The determination process of the obstacle boundary information corresponding to the target obstacle is similar to the path boundary information corresponding to the sampling point. In an alternative embodiment provided based on the embodiment shown in fig. 3, step 304 may alternatively be implemented as a sub-step as follows.
Step 304a, for the target obstacle, determining a third projection point of the target obstacle on the first road boundary and a fourth projection point of the target obstacle on the second road boundary.
The vehicle determines the key point of the target obstacle, then takes the projection point of the key point on the first road boundary as a third projection point and takes the projection point of the key point on the second road boundary as a fourth projection point. The key point of the target obstacle may be the center point or any vertex of the target obstacle, which is not limited in the embodiment of the present application.
In some embodiments, for a target obstacle, the vehicle first determines a sampling point with the smallest distance to the target obstacle, then searches for a third projection point in two directions based on a first projection point of the sampling point on a first road boundary, and searches for a fourth projection point in two directions based on a second projection point of the sampling point on a second road boundary. The two directions refer to the road direction and the opposite direction of the road direction. By the above method, the search from the set starting point of the first road boundary or the second road boundary can be avoided, and the search efficiency of the projection points (the third projection point and the fourth projection point) can be improved.
Step 304b, determining a center point corresponding to the target obstacle based on the key point of the target obstacle, the third projection point and the fourth projection point.
Optionally, the vehicle first determines a midpoint of a line between the third projection point and the fourth projection point, and then aligns the midpoint with a key point of the target obstacle, where the aligned point is a center point corresponding to the target obstacle. The alignment may be explained with reference to step 302b, and will not be described here.
Step 304c, obtaining a third constraint distance in the first direction and a fourth constraint distance in the second direction.
The third constraint distance and the fourth constraint distance may be preset, and the third constraint distance is, for example, 5 meters. The fourth constraint distance is 1.0 times the vehicle width. For example, when the vehicle width is 1.5 m, the fourth constraint distance is 1.3×1.0=1.3 m.
Step 304d, determining the obstacle boundary information corresponding to the target obstacle based on the center point corresponding to the target obstacle, the third constraint distance and the fourth constraint distance.
Optionally, the vehicle uses the center point corresponding to the target obstacle as the center point of the second planar area, the third constraint distance is the length direction of the second planar area, the fourth constraint distance is the width direction of the second planar area, and finally the second planar area is determined.
The following describes a process of determining path boundary information corresponding to the sampling point. In an alternative embodiment provided based on the embodiment shown in fig. 3, step 305 may alternatively be implemented as a sub-step as follows.
In step 305a, m drivable regions are determined based on the path boundary information and the obstacle boundary information corresponding to the plurality of sampling points, where m is an integer greater than 2.
And when the path boundary information corresponding to the sampling point and the obstacle boundary information do not have an overlapping area, determining the path boundary information corresponding to the sampling point as a travelable area.
And determining a non-overlapping area between the path boundary information corresponding to the sampling point and the obstacle boundary information as a drivable area under the condition that the path boundary information corresponding to the sampling point and the obstacle boundary information have an overlapping area and the area of the overlapping area is smaller than a preset area.
In step 305b, n candidate driving regions are determined based on the m driving possible regions and a preset reference line, where n is an integer greater than 1 and less than m.
After determining the m drivable regions, the vehicle first determines a reference line. Alternatively, the vehicle may determine the center line of the first drivable region as the reference line, or may take as the reference line the straight line passing through the largest number of drivable regions.
After determining the reference line, the vehicle selects n candidate travel regions among the m travel-capable regions, wherein the vehicle preferentially approaches the travel-capable region of the reference line as the candidate travel region.
In the embodiment of the application, the drivable space is longitudinally and transversely formed into a two-dimensional array, a path search with a plane area as a basic node unit is established based on the array, and the situation that an obstacle bypasses from a large space when changing a road line can be avoided as close to a reference line as possible on the premise of ensuring the path length.
In step 305c, when each of the m candidate travel regions adjacent to each other in the target direction satisfies the communication condition, the plurality of candidate travel regions are determined as the target travel space.
The target direction is the road direction. In some embodiments, the vehicle expands the candidate travel area in the target direction by a preset length, after which the connectivity determination is made. Specifically, the vehicle solves an inequality linear programming model corresponding to 8 sides of the two adjacent candidate driving areas, if the inequality linear programming model has a solution, the two adjacent candidate driving areas are indicated to meet the communication condition, and if the inequality linear programming model has no solution, the two adjacent candidate driving areas are indicated to not meet the communication condition.
Referring to fig. 5, the rectangular frame in fig. 5 is a drivable region, wherein the solid rectangular frame is a candidate driving region selected according to a reference line. The arrows between the adjacent rectangular frames in the road direction are used for indicating that the adjacent rectangular frames meet the communication condition, and the plurality of solid line rectangular frames form the target running space.
Referring to fig. 6, a schematic diagram of path planning provided by one embodiment of the present application is shown. After the target travel space is determined, a path point is determined for each candidate travel area in the target travel space, and the path points for each candidate travel area constitute the planned path. In fig. 6, each of the path points connected by the broken-line arrows constitutes a planned path.
The preset constraints are explained below. In some embodiments, the preset constraints include at least one of: boundary constraints, start constraints, end constraints, and continuity constraints.
In some embodiments, the vehicle determines the boundary constraint based on path boundary information corresponding to each of the plurality of sampling points.
Optionally, the vehicle uses the 1 st sampling point as an origin, the longitudinal axis as the road direction, the transverse axis is perpendicular to the road direction to establish a cartesian coordinate system, and then an inequality is established based on two parallel sides (parallel to the road direction) of the path boundary information corresponding to the sampling point and the cartesian coordinate system. For example, if the abscissa of the point on the first of the two parallel sides is x1 and the abscissa of the point on the second of the two parallel sides is-x 1, then the inequality is determined as-x 1< x < x1, and the inequality indicates that the abscissa of the trajectory point on the planned path should be greater than-x 1 and less than x1.
Optionally, the vehicle may also establish a segmentation inequality based on the target travel space and the cartesian coordinate system described above. Specifically, the vehicle acquires two parallel sides (parallel to the road direction) of each candidate traveling area in the vehicle acquisition target traveling space, and establishes a segmentation inequality based on the parallel sides of each candidate traveling area and the above-described cartesian coordinate system. For example, the abscissa of the point on the first parallel side of the first to fifth candidate regions is x2, the abscissa of the point on the second parallel side of the first to fifth candidate regions is-x 2, the ordinate of the point on the third parallel side (perpendicular to the road direction and farther from the origin) of the fifth candidate region is y1, the abscissa of the point on the first parallel side of the sixth candidate region is x3, the abscissa of the point on the second parallel side of the sixth candidate region is 0, and the ordinate of the point on the third parallel side (perpendicular to the road direction and farther from the origin) of the sixth candidate region is y2, then the piecewise inequality is constructed as follows:
In some embodiments, the vehicle determines a starting point constraint in the preset constraints based on a current position of the vehicle or a starting position in the historical driving trajectory.
The starting point constraints include constraints of the starting point location, constraints of the heading of the vehicle at the starting point location, constraints of the curvature of the vehicle at the starting point location. The constraint on the starting point position is represented by a function value of the function, and the constraint on the heading of the vehicle at the starting point position is represented by a first order derivative of the function. The constraint of the curvature of the vehicle at the starting point position is represented by a second derivative of the function.
Alternatively, the vehicle determines the starting point constraint condition based on the current position of the vehicle in the case where the history running track is not acquired or deviation from the history running track is large. In the case of determining the starting point constraint condition based on the current position of the vehicle, the unitized direction vector should be used as the constraint condition of the heading of the vehicle at the starting point position in the case of determining the starting point constraint condition based on the current position of the vehicle, the vehicle firstly estimates the current curvature by using the steering wheel angle of the vehicle model, then builds a parabola with the curvature being the current curvature and the first order being 1 at a predetermined position, then rotationally translates the parabola so that the end point of the parabola coincides with the current position and the direction of the vehicle, and then calculates the second order of the function.
In some embodiments, the vehicle determines an end constraint among the preset constraints based on the road direction and the path direction of the planned path.
Optionally, the end point constraint includes a constraint of an end point position, a constraint of a heading of the vehicle at the end point position. The constraint condition of the end position is represented by a function value of the function, and the constraint condition of the heading of the vehicle at the end position is represented by a first order derivative of the function.
The constraint condition of the end point position is to ensure that the function value of the function is identical to the path end point. The constraint condition of the heading of the vehicle at the end position is to ensure that the value of the first-order derivative of the function is a unitized forward direction vector, i.e. the path direction of the planned path is the same as the road direction.
In some embodiments, the continuity constraint condition in the preset constraint condition is determined based on parameter information of curve connection points of two adjacent curves.
Optionally, the vehicle determines a plurality of curves based on the plurality of sampling points, and the continuity constraint condition to be satisfied by two adjacent curves in the plurality of curves includes at least one of: the curve end point of the ith curve is coincident with the curve start point of the (i+1) th curve, the curve end point of the ith curve is identical to the curve start point of the (i+1) th curve in direction, and the curve end point of the ith curve is identical to the curve start point of the (i+1) th curve in curvature. In the embodiment of the application, in the case that the maximum sampling distance is 75 meters, the segmentation curve can be set to 3, and each segment covers 25 meters.
The fact that the curve end point of the ith curve coincides with the curve start point of the (i+1) th curve means that the function values of the ith curve and the (i+1) th curve at the curve connection point are the same. The fact that the curve end point of the ith curve is the same as the curve start point of the (i+1) th curve means that the first order guide of the ith curve and the (i+1) th curve at the curve connection point is the same. The curve end point of the ith curve and the curve start point of the (i+1) th curve are the same as the second derivative of the (i+1) th curve at the curve connection point. Further, to ensure that the steering wheel changes continuously at the curve connection point of the adjacent two curves, the following constraints may also be set: the third order leads of the ith curve and the (i+1) th curve at the curve connection point are the same.
The preset constraints are explained below. In some embodiments, the preset optimization objective includes at least one of: the planned path is close to a preset reference line, the planned path is close to the road direction, the steering wheel control angle when the vehicle runs on the planned path is smaller than a preset angle, and the steering wheel control times when the vehicle runs on the planned path are smaller than a preset times.
The planned path is close to the road direction and is used for relieving the problem of path callback caused by understeer when turning and oversteer when changing the road, the expression form is that the path is wave-shaped all the time after turning or changing the road, and the amplitude can be gradually relieved along with the extension of the linear distance.
Optionally, the vehicle measures whether the planned path is proximate to a predetermined reference line by squaring the difference between the path point and the reference point. The smaller the square of the difference between the path point and the reference point, the closer the planned path is to the preset reference line, and the larger the square of the difference between the path point and the reference point, the larger the deviation between the planned path and the preset reference line.
Optionally, the vehicle measures whether the planned path is proximate to the road direction by squaring a product of a first vector representing a first derivative of the path point and the second vector. Wherein the second direction is a unit vector along the road direction. The smaller the square of the product of the first vector and the second vector, the closer the planned path is to the road direction. The larger the square of the product of the first vector and the second vector, the larger the deviation of the planned path from the road direction.
Optionally, the vehicle measures whether the steering wheel control angle when the vehicle is driving on the planned path is smaller than a preset angle through second derivative squaring integration. The steering wheel control angle when the vehicle is traveling on the planned path means a turning angle of the steering wheel when the vehicle is traveling on the planned path. Specifically, the second derivative squaring integral has a positive correlation with the steering wheel control angle, and the smaller the second derivative squaring integral is, the smaller the steering wheel control angle is. The greater the second derivative squaring integral, the greater the steering wheel control angle.
Optionally, the vehicle measures whether the steering wheel control times when the vehicle runs on the planned path are smaller than the preset times through three-order derivative squaring integration. The number of steering wheel control times when the vehicle travels on the planned route means the number of steering wheel rotations when the vehicle travels on the planned route. Specifically, the third-order squaring integral has a positive correlation with the steering wheel control times, and the smaller the third-order squaring integral is, the smaller the steering wheel control times are. The larger the third order derivative squaring integral, the larger the steering wheel control number.
Optionally, the vehicle performs a weighted summation of the four targets to determine a final optimization target. It should be noted that the weighting coefficients of the four optimization targets are different in different scenes. In one example, in the u-turn scene, the weight of the optimization target, which is that the planned path is close to a preset reference line, is 1, the weight of the optimization target, which is that the planned path is close to the road direction, is 100, the weight of the optimization target, which is that the steering wheel control angle is smaller than the preset angle when the vehicle runs on the planned path, is 1, and the weight of the optimization target, which is that the steering wheel control frequency is smaller than the preset frequency when the vehicle runs on the planned path, is 1. Under the driving scene along the road, the weight of an optimization target, which is a planned path close to a preset reference line, is 5, the weight of an optimization target, which is a planned path close to the road direction, is 100, the weight of an optimization target, which is a steering wheel control angle smaller than a preset angle when the vehicle drives on the planned path, is 1, and the weight of an optimization target, which is a steering wheel control frequency smaller than a preset frequency when the vehicle drives on the planned path, is 1.
Referring to fig. 7, a flowchart of a path planning method according to an embodiment of the application is shown. The method comprises the following steps:
in step 701, a plurality of sampling points are acquired.
In step 702, path boundary information corresponding to each of the plurality of sampling points is determined in a cartesian coordinate system, where the path boundary information corresponding to the sampling points includes a first plane area determined based on the sampling points.
In step 703, a target obstacle is determined.
In step 704, in cartesian coordinates, obstacle boundary information corresponding to the target obstacle is determined, where the obstacle boundary information corresponding to the target obstacle includes a second planar area determined based on the target obstacle.
Step 705, determining the target driving space based on the path boundary information and the obstacle boundary information corresponding to the plurality of sampling points.
Step 706, searching for an end point of the planned path based on the target travel space and at least one of the preset reference line, the preset curvature requirement and the preset road direction requirement.
The preset curvature requirement means that the curvature of the planned path at the end point is smaller than a preset radius. The preset radius refers to a turning radius of the vehicle, which may be actually set according to the operating condition of the vehicle, and is exemplified by a preset radius of 6 meters.
The preset road direction requirement refers to the direction of the planned path approaching the road. Optionally, the vehicle measures whether the planned path is close to the road direction by the absolute value of the dot multiplication of the local path vector and the unit vector to the right of the bounding box, and the smaller the absolute value of the dot multiplication of the local path vector and the unit vector to the right of the bounding box, the closer the planned path is to the road direction, and in addition, the cosine value of the angle between the local path vector and the unit vector to the right of the bounding box needs to be less than or equal to a preset value, which is set according to experiments or experience, and the preset value is 0.6, for example.
If the distance between the searched path end point and the nearest sampling point is smaller than the preset distance, the nearest sampling point is used as the end point of the planned path, and if the distance between the searched path start point and the nearest sampling point is larger than the preset distance, the searched path end point is used as the end point of the planned path. The preset distance is set according to experiments or experience, and the embodiment of the present application is not limited thereto. The preset distance is, for example, 1 meter.
When the destination is within or immediately behind the obstacle, the planned route solved at this time cannot satisfy the boundary condition of the destination or an excessive curvature occurs at the destination position, and such a route cannot travel, so it is necessary to search for the destination in advance to avoid the occurrence of the above.
Step 707, determining a planned path based on the target driving space, the preset constraint condition, the preset optimization target and the end point of the planned path.
The vehicle builds a planned path model based on the target running space, the preset constraint condition, the preset optimization target and the end point of the planned path, and then solves the planned path model to obtain the planned path.
In summary, according to the technical scheme provided by the embodiment of the application, the end point of the planned path is searched according to at least one of the preset reference line, the preset curvature requirement and the preset road direction requirement and the target driving space, so that the end point of the path planning can be flexibly adjusted, and the problem that the path planning cannot be solved is avoided.
Referring to fig. 8, an embodiment of the present application further provides a path planning apparatus, where the path planning apparatus includes:
the sampling point acquisition module 810 is configured to acquire a plurality of sampling points.
The first determining module 820 is configured to determine path boundary information corresponding to each of the plurality of sampling points in a cartesian coordinate system, where the path boundary information corresponding to the sampling points includes a first plane area determined based on the sampling points.
The obstacle determination module 830 is configured to determine a target obstacle.
The second determining module 840 is configured to determine, in cartesian coordinates, obstacle boundary information corresponding to the target obstacle, where the obstacle boundary information corresponding to the target obstacle includes a second planar area determined based on the target obstacle.
The driving space determining module 850 is configured to determine the target driving space based on path boundary information and obstacle boundary information corresponding to the plurality of sampling points, respectively.
The path planning module 860 is configured to determine a planned path based on the target driving space, a preset constraint condition, and a preset optimization target.
In summary, according to the technical solution provided in the embodiments of the present application, path boundary information determined based on each sampling point is described by a cartesian coordinate system, obstacle boundary information determined based on an object obstacle is described by a cartesian coordinate system, an object running space in which a vehicle can run and no collision accident occurs is determined based on the path boundary information and the obstacle boundary information, and finally, a planned path is determined according to the object running space, a preset constraint condition and a preset optimization target.
In some embodiments, the first determining module 820 is configured to determine, for the ith sampling point, a first projection point of the ith sampling point on the first road boundary and a second projection point of the ith sampling point on the second road boundary, where i is a positive integer; determining a center point corresponding to the ith sampling point based on the ith sampling point, the first projection point and the second projection point; acquiring a constraint distance in a first direction and a constraint distance in a second direction, wherein the first direction and the second direction are mutually perpendicular; and determining path boundary information corresponding to the ith sampling point based on the center point corresponding to the ith sampling point, the constraint distance in the first direction and the constraint distance in the second direction.
In some embodiments, in the case where i is greater than 1, the first determining module 820 is specifically configured to, for the i-th sampling point: searching on the first road boundary by taking the first projection point of the (i-1) th sampling point on the first road boundary as a starting position to obtain the first projection point of the i-th sampling point on the first road boundary; and searching on the second road boundary by taking the second projection point of the (i-1) th sampling point on the second road boundary as a starting position to obtain the second projection point of the i-th sampling point on the second road boundary.
In some embodiments, the apparatus further comprises: an endpoint search module (not shown). The terminal point searching module is used for searching the terminal point of the planning path based on at least one of a preset reference line, a preset curvature requirement and a preset road direction requirement and the target running space; the preset curvature requirement means that the curvature of the planned path at the end point is smaller than a preset radius, and the preset road direction requirement means that the planned path is close to the road direction. The path planning module 860 is configured to determine a planned path based on the target driving space, a preset constraint condition, a preset optimization target, and an end point of the planned path.
In some embodiments, the driving space determining module 850 is configured to determine m drivable regions based on path boundary information and obstacle boundary information corresponding to the plurality of sampling points, where m is an integer greater than 2; determining n candidate driving areas based on m driving areas and a preset reference line, wherein n is an integer greater than 1 and less than m; in the case where each of the m candidate traveling regions satisfies the communication condition in the candidate traveling regions adjacent in the target direction, the plurality of candidate traveling regions are determined as the target traveling space, and the target direction is the traveling direction of the vehicle.
In some embodiments, the apparatus further comprises: a constraint determination module (not shown). Constraint condition determining module for: determining boundary constraint conditions in preset constraint conditions based on path boundary information corresponding to the sampling points respectively; or/and determining a starting point constraint condition in preset constraint conditions based on the current position of the vehicle or the starting position in the historical driving track; or/and determining an end constraint condition in preset constraint conditions based on the road direction and the path direction of the planned path; or/and determining the continuity constraint condition in the preset constraint conditions based on the parameter information of the curve connection points of the two adjacent curves.
In some embodiments, the apparatus further comprises: an optimization objective determination module (not shown). An optimization target determining module, configured to: determining a preset optimization target, wherein the preset optimization target comprises at least one of the following: the planned path is close to a preset reference line, the planned path is close to the road direction, the steering wheel control angle when the vehicle runs on the planned path is smaller than a preset angle, and the steering wheel control times when the vehicle runs on the planned path are smaller than a preset times.
Referring to fig. 9, there is shown a vehicle 900 according to an embodiment of the present application, the vehicle 900 includes: one or more processors 910, a memory 920, and one or more applications. Wherein one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the method of determining the parking path described above.
Processor 910 may include one or more processing cores. The processor 910 utilizes various interfaces and lines to connect various portions of the overall battery management system, perform various functions of the battery management system, and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 920, and invoking data stored in the memory 920. Alternatively, the processor 910 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 910 may integrate one or a combination of several of a central processing unit 910 (Central Processing Unit, CPU), an image processor 910 (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for being responsible for rendering and drawing of display content; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 910 and may be implemented solely by a single communication chip.
The Memory 920 may include a random access Memory 920 (Random Access Memory, RAM), or may include a Read-Only Memory 920. Memory 920 may be used to store instructions, programs, code, sets of codes, or instruction sets. The memory 920 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing various method embodiments described below, and the like. The storage data area may also store data created by the electronic device map in use (e.g., phonebook, audiovisual data, chat log data), and the like.
Referring to fig. 10, an embodiment of the present application is further provided with a computer readable storage medium 1000, where the computer readable storage medium 1000 stores computer program instructions 1010, and the computer program instructions 1010 may be invoked by a processor to perform the method described in the above embodiment.
The computer readable storage medium may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Optionally, the computer readable storage medium comprises a non-volatile computer readable storage medium (non-transitory computer-readable storage medium). The computer readable storage medium 1000 has storage space for computer program instructions 1010 that perform any of the method steps described above. The computer program instructions 1010 may be read from or written to one or more computer program products.
Although the present application has been described in terms of the preferred embodiments, it should be understood that the present application is not limited to the specific embodiments, but is capable of numerous modifications and equivalents, and alternative embodiments and modifications of the embodiments described above, without departing from the spirit and scope of the present application.
Claims (10)
1. A method of path planning, the method comprising:
acquiring a plurality of sampling points;
determining path boundary information corresponding to a plurality of sampling points respectively under a Cartesian coordinate system, wherein the path boundary information corresponding to the sampling points comprises a first plane area determined based on the sampling points;
determining a target obstacle;
determining obstacle boundary information corresponding to the target obstacle under Cartesian coordinates, wherein the obstacle boundary information corresponding to the target obstacle comprises a second planar area determined based on the target obstacle;
Determining a target running space based on path boundary information and the obstacle boundary information respectively corresponding to a plurality of sampling points;
and determining a planned path based on the target running space, a preset constraint condition and a preset optimization target.
2. The method according to claim 1, wherein determining path boundary information corresponding to each of the plurality of sampling points in a cartesian coordinate system includes:
for an ith sampling point, determining a first projection point of the ith sampling point on a first road boundary and a second projection point of the ith sampling point on a second road boundary, wherein i is a positive integer;
determining a center point corresponding to the ith sampling point based on the ith sampling point, the first projection point and the second projection point;
obtaining a constraint distance in a first direction and a constraint distance in a second direction, wherein the first direction and the second direction are mutually perpendicular;
and determining path boundary information corresponding to the ith sampling point based on the center point corresponding to the ith sampling point, the constraint distance in the first direction and the constraint distance in the second direction.
3. The method according to claim 2, wherein, in case i is greater than 1, the determining, for an i-th sampling point, a first projection point of the i-th sampling point on a first road boundary and a second projection point of the i-th sampling point on a second road boundary, comprises:
For the i-th sampling point:
searching on the first road boundary by taking a first projection point of the (i-1) th sampling point on the first road boundary as a starting position to obtain a first projection point of the i-th sampling point on the first road boundary;
and searching on the second road boundary by taking a second projection point of the (i-1) th sampling point on the second road boundary as a starting position to obtain a second projection point of the i-th sampling point on the second road boundary.
4. The method of claim 1, wherein prior to determining a planned path based on the target travel space, a preset constraint, and a preset optimization objective, further comprising:
searching for an end point of the planned path based on at least one of a preset reference line, a preset curvature requirement and a preset road direction requirement, and the target travel space; the preset curvature requirement means that the curvature of the planned path at the end point is smaller than a preset radius, and the preset road direction requirement means that the planned path is close to the road direction;
the determining a planned path based on the target running space, a preset constraint condition and a preset optimization target includes:
And determining the planned path based on the target running space, the preset constraint condition, the preset optimization target and the end point of the planned path.
5. The method according to claim 1, wherein the determining the target travel space based on the path boundary information and the obstacle boundary information corresponding to the plurality of sampling points, respectively, includes:
determining m drivable regions based on path boundary information and the obstacle boundary information respectively corresponding to a plurality of sampling points, wherein m is an integer greater than 2;
determining n candidate driving areas based on the m driving areas and a preset reference line, wherein n is an integer greater than 1 and less than m;
and determining a plurality of candidate traveling areas as the target traveling space in a case where each of the m candidate traveling areas satisfies a communication condition in a target direction, the target direction being a traveling direction of the vehicle.
6. The method according to any one of claims 1 to 5, wherein before determining a planned path based on the target travel space, a preset constraint condition, and a preset optimization target, further comprising:
Determining boundary constraint conditions in the preset constraint conditions based on path boundary information corresponding to the sampling points respectively; or/and the like,
determining a starting point constraint condition in the preset constraint conditions based on the current position of the vehicle or the starting position in the historical driving track; or/and the like,
determining an endpoint constraint condition in the preset constraint conditions based on the road direction and the path direction of the planned path; or/and the like,
and determining the continuity constraint condition in the preset constraint conditions based on the parameter information of the curve connection points of the two adjacent curves.
7. The method according to any one of claims 1 to 5, wherein before determining a planned path based on the target travel space, a preset constraint condition, and a preset optimization target, further comprising:
determining the preset optimization objective, wherein the preset optimization objective comprises at least one of the following: the planned path is close to a preset reference line, the planned path is close to the road direction, the steering wheel control angle when the vehicle runs on the planned path is smaller than a preset angle, and the steering wheel control times when the vehicle runs on the planned path are smaller than preset times.
8. A path planning apparatus, the apparatus comprising:
the sampling point acquisition module is used for acquiring a plurality of sampling points;
the first determining module is used for determining path boundary information corresponding to a plurality of sampling points respectively under a Cartesian coordinate system, wherein the path boundary information corresponding to the sampling points comprises a first plane area determined based on the sampling points;
an obstacle determination module for determining a target obstacle;
a second determining module, configured to determine, in cartesian coordinates, obstacle boundary information corresponding to the target obstacle, where the obstacle boundary information corresponding to the target obstacle includes a second planar area determined based on the target obstacle;
the driving space determining module is used for determining a target driving space based on path boundary information and the obstacle boundary information which correspond to the sampling points respectively;
and the path planning module is used for determining a planned path based on the target running space, the preset constraint condition and the preset optimization target.
9. A vehicle, characterized in that the vehicle comprises:
one or more processors;
a memory;
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the method of any of claims 1-7.
10. A computer readable storage medium having stored therein computer program instructions which are callable by a processor to perform the method according to any one of claims 1-7.
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