WO2023024542A1 - 车辆决策规划方法、装置、设备及介质 - Google Patents

车辆决策规划方法、装置、设备及介质 Download PDF

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Publication number
WO2023024542A1
WO2023024542A1 PCT/CN2022/088532 CN2022088532W WO2023024542A1 WO 2023024542 A1 WO2023024542 A1 WO 2023024542A1 CN 2022088532 W CN2022088532 W CN 2022088532W WO 2023024542 A1 WO2023024542 A1 WO 2023024542A1
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Prior art keywords
obstacle
information
grid
lane
vehicle
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PCT/CN2022/088532
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English (en)
French (fr)
Inventor
邬杨明
王锡贵
王珺旸
蔡祺生
周小成
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驭势科技（北京）有限公司
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Priority claimed from CN202110984268.3A external-priority patent/CN113682300B/zh
Priority claimed from CN202111095293.2A external-priority patent/CN115817464A/zh
Application filed by 驭势科技（北京）有限公司 filed Critical 驭势科技（北京）有限公司
Priority to EP22859896.7A priority Critical patent/EP4368465A1/en
Priority to KR1020247002509A priority patent/KR20240025632A/ko
Publication of WO2023024542A1 publication Critical patent/WO2023024542A1/zh
Priority to US18/433,445 priority patent/US20240174221A1/en

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/085—Taking automatic action to adjust vehicle attitude in preparation for collision, e.g. braking for nose dropping
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/0098—Details of control systems ensuring comfort, safety or stability not otherwise provided for
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
    • B60W2050/0052—Filtering, filters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/30—Road curve radius
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/53—Road markings, e.g. lane marker or crosswalk
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/20—Static objects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/40—High definition maps

Definitions

  • the present disclosure relates to the technical field of unmanned driving, and in particular to a vehicle decision planning method, device, equipment and medium.
  • the automatic driving system needs to plan a smooth, safe and vehicle-passable path to ensure that the vehicle will not collide with obstacles.
  • the perception module of the automatic driving system will output two types of obstacles, one is a convex hull obstacle with rich semantic information, and the other is a grid obstacle without semantic information.
  • the decision planning module can make obstacle decisions more conveniently, but for grid obstacles with high discreteness and a large number, it is difficult for the decision planning module to make obstacle decisions conveniently and quickly , which makes it difficult for the decision planning module to make obstacle decisions for mixed types of obstacles.
  • the present disclosure provides a vehicle decision planning method, device, equipment and medium.
  • An embodiment of the present disclosure provides a vehicle decision-making planning method, including:
  • obstacle decisions are made, including:
  • the target convex-hull obstacle includes the first convex-hull obstacle and/or the The second convex hull obstacle.
  • the drivable area is generated based on obstacle decisions, including:
  • the environment awareness information includes at least two of lane information, obstacle information and vehicle information, and the obstacle information includes static obstacle information and/or dynamic obstacle information;
  • lane decision semantic information Based on the environment perception information, determine lane decision semantic information of each lane, wherein the lane decision semantic information includes passing time cost and safety cost;
  • a drivable area is generated.
  • An embodiment of the present disclosure provides a vehicle decision planning device, including:
  • the base coordinate system generator is used to generate the base coordinate system
  • a guideline generator configured to generate a guideline under the base coordinate system to determine a rough future trajectory of the vehicle
  • An obstacle decision maker configured to perform obstacle decision-making under the constraints of the guide line
  • Driving space generator for generating drivable areas based on obstacle decisions.
  • the obstacle decider includes:
  • An information acquisition module configured to acquire road information, first grid obstacle information of the first grid obstacle, and first convex hull obstacle information of the first convex hull obstacle;
  • a preprocessing module configured to preprocess the first grid obstacle based on the road information and the first grid obstacle information to obtain a second grid obstacle, wherein the second grid obstacle The number of grid obstacles is less than the number of the first grid obstacles;
  • a type conversion module configured to convert the second grid obstacle into a second convex hull obstacle
  • An avoidance decision-making module configured to make an avoidance decision for the target convex-hull obstacle based on the target convex-hull obstacle information of the target convex-hull obstacle, wherein the target convex-hull obstacle includes the first convex-hull obstacle object and/or the second convex obstacle.
  • the drive space generator includes:
  • a perception information acquisition module configured to acquire environment perception information, wherein the environment perception information includes at least two of lane information, obstacle information and vehicle information, and the obstacle information includes static obstacle information and/or dynamic Obstacle information;
  • a lane decision semantic information determination module configured to determine the lane decision semantic information of each lane based on the environment perception information, wherein the lane decision semantic information includes passing time cost and safety cost;
  • a drivable area generating module configured to generate a drivable area based on the lane decision semantic information.
  • An embodiment of the present disclosure provides an electronic device, including:
  • the memory is connected in communication with the one or more processors, the memory stores instructions executable by the one or more processors, and the instructions are executed by the one or more processors , the electronic device is used to implement the vehicle decision planning method provided in any embodiment of the present disclosure.
  • An embodiment of the present disclosure provides a computer-readable storage medium on which computer-executable instructions are stored.
  • the computer-executable instructions When executed by a computing device, they can be used to implement the vehicle decision-making plan provided by any embodiment of the present disclosure. method.
  • FIG. 1 is a schematic flowchart of a vehicle decision planning method provided by an embodiment of the present disclosure
  • FIG. 2 is a functional block diagram of a decision planning module provided by an embodiment of the present disclosure
  • FIG. 3 is a schematic diagram of an applicable scenario of a vehicle decision planning method provided by an embodiment of the present disclosure
  • FIG. 4 is a schematic flowchart of a part of a vehicle decision-making planning method provided by an embodiment of the present disclosure
  • FIG. 5 is a functional module block diagram of an obstacle decision maker provided by an embodiment of the present disclosure.
  • FIG. 6 is a schematic diagram of a road bounding box provided by an embodiment of the present disclosure.
  • FIG. 7 is a scene diagram of an avoidance decision provided by an embodiment of the present disclosure.
  • FIG. 8 is a partial flowchart of another vehicle decision planning method provided by an embodiment of the present disclosure.
  • FIG. 9 is a scene diagram corresponding to the lane passing time cost provided by the embodiment of the present disclosure.
  • FIG. 10 is an ST diagram of lane safety judgment provided by an embodiment of the present disclosure.
  • FIG. 11 is a schematic diagram of the discretization of the boundary of the drivable area provided by the embodiment of the present disclosure.
  • FIG. 12 is a scene diagram corresponding to the lane width traffic cost provided by the embodiment of the present disclosure.
  • FIG. 13 is a schematic diagram of updating a drivable area based on preset traffic rules provided by an embodiment of the present disclosure
  • FIG. 14 is a schematic diagram of updating a drivable area with kinematics and dynamics constraints of a vehicle provided by an embodiment of the present disclosure
  • Fig. 15 is a schematic diagram of an obstacle semantic information and a preset safe area update drivable area provided by an embodiment of the present disclosure
  • FIG. 16 is a schematic diagram of generating a frenet bounding box provided by an embodiment of the present disclosure.
  • Fig. 17 is a schematic diagram of static obstacle information, obstacle decision semantic information and ray tracing algorithm updating the drivable area provided by an embodiment of the present disclosure
  • FIG. 18 is a block diagram of functional modules of an obstacle decision maker in a vehicle decision planning device provided by an embodiment of the present disclosure
  • FIG. 19 is a block diagram of functional modules of the driving space generator in the vehicle decision planning device provided by an embodiment of the present disclosure.
  • FIG. 20 is a schematic structural diagram of an electronic device suitable for realizing the implementation manners of the present disclosure provided by the embodiments of the present disclosure.
  • Fig. 1 is a schematic flowchart of a vehicle decision planning method provided by an embodiment of the present disclosure. This method is suitable for obstacle decision-making and drivable area generation of unmanned vehicles. As shown in Figure 1, the method includes the following steps:
  • the base coordinate system may be a frenet coordinate system.
  • obstacle decision-making is made on the obstacle on the approximate travel trajectory, so as to avoid the obstacle.
  • the obstacle decision it can be determined whether the vehicle needs to pass the obstacle on the left side, pass the obstacle on the right side, or follow the obstacle, so as to determine the driving area of the vehicle.
  • Fig. 2 shows a block diagram of the functional modules of the decision planning module.
  • the decision planning module 1 may include a constraint generation unit 11 , a trajectory generation unit 12 and a trajectory smoothing unit 13 .
  • the constraint generating unit 11 includes a base coordinate system generator 111 , a guiding line generator 112 , an obstacle decision maker 113 and a driving space generator 114 .
  • the base coordinate system generator 111 is used to generate a base coordinate system, such as a frenet coordinate system, etc.
  • the guideline generator 112 is used to generate a guideline to determine a general trajectory of the vehicle in the future
  • the obstacle decision maker 113 is used to perform Obstacle Decisions
  • Driving Space Generator is used to generate drivable areas based on obstacle decisions.
  • the trajectory generation unit 12 is used to generate the driving trajectory of the unmanned vehicle according to the drivable area
  • the trajectory smoothing unit 13 is used to smooth the driving trajectory.
  • the obstacle decision maker 113 is specifically configured to obtain road information, the first grid obstacle information of the first grid obstacle, and the first convex hull obstacle information of the first convex hull obstacle; based on the road information and the first grid obstacle information, preprocess the first grid obstacle to obtain the second grid obstacle; convert the second grid obstacle into the second convex hull obstacle; based on the target convex hull obstacle
  • the target convex hull obstacle information of the object is used to make an avoidance decision for the target convex hull obstacle.
  • the driving space generator 114 is specifically used to obtain environmental awareness information; based on the environmental awareness information, determine the lane decision semantic information of each lane, wherein the lane decision semantic information includes passing time cost and safety cost; based on Lane decision semantic information to generate drivable areas.
  • the embodiment of the present disclosure provides a vehicle decision-making planning method, which is suitable for unmanned vehicles to detect static obstacles and/or dynamic obstacles such as grid obstacles and convex hull obstacles in the road environment.
  • Fig. 3 shows the applicable scenario of the vehicle decision-making planning method.
  • convex hull obstacles 200 including static obstacles and dynamic obstacles
  • grid obstacles 300 in front of the unmanned vehicle 100.
  • the car 100 can convert the type of the grid obstacle 300 into a convex hull type by obtaining the obstacle information of the convex hull obstacle 200 and the grid obstacle 300, so as to realize the unification of the convex hull obstacle 200 and the grid obstacle 300 decision making.
  • This method can be applied to unmanned vehicles, and is specifically applied to the decision planning module in the automatic driving system of unmanned vehicles. Based on the vehicle decision-making planning method provided by the embodiments of the present disclosure, unified decision-making for mixed-type obstacles can be realized, and obstacle decision-making can be made conveniently and quickly.
  • FIG. 4 is a partial flowchart of a vehicle decision planning method provided by an embodiment of the present disclosure. As shown in Figure 4, making an obstacle decision (or a decision-making method for avoiding an obstacle) includes the following steps:
  • the grid obstacle is a grid type obstacle
  • the convex hull obstacle is a convex hull type obstacle
  • the road information can be acquired through a high-precision map or a vehicle-mounted camera, and the road information can include road boundary information and road curvature information.
  • obstacle information can be obtained through vehicle perception modules (such as on-board cameras and laser radars, etc.) and positioning modules. Obstacle information can include obstacle type information, obstacle size information, and obstacle location information.
  • the obstacle type information can be an obstacle type identifier, and the obstacle type is distinguished by pre-defining different obstacle type identifiers; the obstacle type information can also be an obstacle data format, and the vehicle perception module perceives the obstacle
  • the obstacle data is processed, and the data of different types of obstacles are stored in different obstacle data formats, so that the decision planning module can distinguish obstacle types through obstacle data formats when obtaining obstacle information, for example,
  • the obstacle data format of grid obstacles is ".ogm”
  • the obstacle data format of convex hull obstacles is ".mot”.
  • the first grid obstacle and the first convex hull obstacle are determined to obtain the first grid obstacle information and the first convex hull obstacle information.
  • Fig. 5 shows a block diagram of the functional modules of the obstacle decision maker.
  • the obstacle decision maker 113 may include a grid obstacle processor 1131 , a way of passage decision maker 1132 and a convex hull obstacle filter 1133 .
  • the grid obstacle processor 1131 executes S220, based on the road information and the first grid obstacle information, preprocessing the first grid obstacle to obtain the second grid obstacle; and S230, the second grid obstacle information Grid obstacles are transformed into second convex hull obstacles.
  • preprocessing the first grid obstacle can be used to reduce the amount of data calculation and simplify the obstacle decision-making process, which may include at least one of the following steps: generating the grid obstacle outline of the first grid obstacle; generating the second grid obstacle An obstacle bounding box of a grid obstacle; filtering out the first grid obstacle located outside the road; and performing aggregation processing on the first grid obstacle located inside the road.
  • preprocessing is performed on the first grid obstacles, so that the number of second grid obstacles obtained after preprocessing is smaller than the number of first grid obstacles, so that the downstream modules can easily detect the obstacles calculate.
  • Embodiments of the present disclosure may reduce the number of first grid obstacles by filtering out first grid obstacles located outside the road.
  • preprocessing the first grid obstacle to obtain the second grid obstacle based on the road information and the first grid obstacle information may include the following steps:
  • filtering out the first grid obstacle located outside the road may include the following steps:
  • the road boundary is discretized into boundary points; based on the boundary points, a road bounding box is generated.
  • the shape of the road bounding box is based on the axisymmetric bounding box of the right-hand coordinate system of the unmanned vehicle body, as long as it can pass through the boundary point and cover the road, so that the subsequent determination of whether the first grid obstacle is located between the road outside judgment.
  • the road boundary and the road boundary curvature are determined; based on the road boundary curvature, the road boundary is discretized to obtain boundary point groups arranged at intervals along the road traffic direction, wherein each group of boundary points The group includes the corresponding left boundary point a and right boundary point a' in the transverse direction, and the transverse direction is perpendicular to the road traffic direction; based on any adjacent two groups of boundary point groups, generate the Rectangular box b, and use rectangular box b as the road bounding box.
  • one side is parallel to the driving direction of the self-driving car, that is, the driving direction x of the unmanned vehicle 100, and the other side is perpendicular to the driving direction of the self-driving car, that is, no one is driving.
  • the normal direction y in which the car 100 travels is driven.
  • the distance between two adjacent boundary points on the same road boundary is negatively correlated with the road boundary curvature, that is, the greater the curvature of the road boundary, the greater the degree of curvature, and the distance between two adjacent boundary points on the road boundary smaller.
  • the road boundary is discretized to obtain a group of boundary points arranged at intervals along the road traffic direction, including: taking the current position of the vehicle as the starting waypoint; obtaining the starting waypoint at A group of corresponding boundary points in the horizontal direction; based on the curvature of the road boundary, select the next road point along the direction of road traffic, where the distance between two adjacent road points is negatively correlated with the curvature of the road boundary; the next road point is used as the starting point Waypoint, return to execute to obtain a set of boundary point groups corresponding to the initial waypoint in the horizontal direction, until the distance from the up and down waypoints to the current position of the vehicle in the direction of road traffic is greater than the preset distance threshold, all currently obtained boundary point groups identified as boundary point groups.
  • the preset distance threshold may be determined according to the maximum range in which the vehicle perceives obstacles.
  • a road bounding box B R ⁇ b min ,b max ,b left,0 ,b left,1 ,b right,0 ,b right,1 ⁇
  • b min and b max are the minimum and maximum coordinate points of the road bounding box
  • b left, 0 ,b left,1 ,b right,0 ,b right,1 are the coordinate points on the left and right sides of the road respectively
  • the whole road can be represented by n road bounding boxes
  • the road bounding box sequence B road_list ⁇ B R0 ,B R1 ,L,B Rn ⁇ .
  • the road may be a route segment in the driving route of the vehicle, and the route segment where the vehicle is located can be determined according to the vehicle positioning information, and the road boundary is discretized, that is, the boundary of the route segment where the vehicle is located is discretized change.
  • the list of road boundary points is defined as S, and it is initialized as an empty table, and the road bounding box sequence B road_list is also initialized as empty, and the road boundary is discretized from the route segment where the vehicle is located, and the first road of the route segment is obtained point (it can be the current position of the vehicle), then obtain the left boundary point and right boundary point corresponding to the first waypoint in the horizontal direction, and add the current left boundary point and right boundary point to the list S; based on the road boundary curvature, Select the next waypoint along the direction of the road, check whether the distance from the first waypoint to the next waypoint along the direction of the road is less than or equal to the preset distance threshold, if the distance is less than or equal to the preset distance threshold, then get the next waypoint The corresponding left boundary point and right boundary point in the horizontal direction are added to the list S; based on the curvature of the road boundary, continue to select the next waypoint along the road traffic direction until the first waypoint is along the road traffic direction to the next way
  • a grid obstacle contour of the first grid obstacle is generated; and a grid obstacle bounding box is generated based on the grid obstacle contour.
  • the suzuki contour tracking algorithm is used to generate a closed contour figure of the obstacle in the first grid, that is, the contour of the obstacle in the grid.
  • the grid obstacle profile consists of n coordinate points.
  • x 0 , x 1 ,..., x n are the x coordinates of n coordinate points in the grid obstacle outline
  • y 0 , y 1 ,..., y n are the y coordinates of n coordinate points in the grid obstacle outline.
  • the embodiment of the present disclosure can perform two-stage collision detection on the first grid obstacle, so as to quickly and accurately determine the first grid obstacle located outside the road.
  • rough collision detection can be performed on grid obstacles first, so as to quickly filter out the first grid obstacle located outside the road, and reduce the calculation amount of collision detection; for the first collision detected by rough collision detection One grid obstacle, then perform fine collision detection, so as to further determine the first grid obstacle located outside the road, so as to ensure that the remaining first grid obstacles after filtering are all located inside the road.
  • the Euclidean distance to the grid obstacle bounding box is determined from the road bounding box The smallest target road bounding box; perform collision detection between the grid obstacle bounding box and the corresponding target road bounding box; if there is no collision between the grid obstacle bounding box and the corresponding target road bounding box, determine the grid obstacle bounding box The corresponding first grid obstacle is located outside the road.
  • the Euclidean distance from the road bounding box to the grid obstacle bounding box is relatively small, it indicates that the collision between the road bounding box and the grid obstacle bounding box is more likely.
  • the calculation amount of collision detection can be reduced, thereby speeding up obstacle decision-making speed. In some embodiments, it is only necessary to detect whether the vertices of the obstacle bounding box of the grid are located on or within the bounding box of the target road.
  • the first grid obstacle corresponding to the grid obstacle bounding box is located outside the road; when the grid obstacle bounding box When the vertex is located on or within the target road bounding box, it is determined that the first grid obstacle corresponding to the grid obstacle bounding box is located within the road.
  • collision detection is performed based on the boundary points of the target road bounding box and the grid obstacle bounding box by vector cross product, and it is determined whether the first grid obstacle corresponding to the grid obstacle bounding box is located outside the road. Specifically, determine the boundary point vector; determine the vertex vector of the grid obstacle bounding box; when the cross product of the vertex vector of the grid obstacle bounding box and the boundary point vector is greater than 0, determine the grid obstacle bounding box corresponding The first grid obstacle is located outside the road.
  • the boundary point vector includes the left boundary vector formed by the two left boundary points of the target road bounding box, and the right boundary vector formed by the two right boundary points of the target road bounding box, and the vertex vector of the grid obstacle bounding box is grid
  • the vector formed by the vertex of the grid obstacle bounding box and a boundary point of the target road bounding box, and the boundary point is a boundary point corresponding to the boundary point vector participating in the cross product operation, for example, when the vertex vector cross-products with the right boundary vector , the boundary point in the vertex vector is a boundary point corresponding to the right boundary vector.
  • grid obstacle bounding box B ⁇ P min ,P max ⁇
  • target road bounding box B R ⁇ b min ,b max ,b left,0 ,b left,1 ,b right,0 ,b right,1 ⁇
  • the left boundary vector v left b left,1 -b left,0
  • it can be determined that other vertices of the grid obstacle bounding box are on the right boundary of the road or on the left or right of the right boundary of the road. In this way, it can be determined whether the first grid obstacle corresponding to the grid obstacle bounding box is located outside the road.
  • the embodiments of the present disclosure may also reduce the number of first grid obstacles located in the road by performing aggregation processing on the first grid obstacles located in the road.
  • preprocessing the first grid obstacle to obtain the second grid obstacle based on the road information and the first grid obstacle information may also include the following steps:
  • the first grid obstacle located inside the road can be determined by the method of judging whether the first grid obstacle is located outside the road in the above embodiment, and details will not be repeated here.
  • the first obstacle bounding box of the first grid obstacle is generated; when two adjacent first obstacles When the Euclidean distance between object bounding boxes is less than the width of the vehicle, merge two adjacent first obstacle bounding boxes to generate a second obstacle bounding box; use the second obstacle bounding box as the first obstacle surround Box, return to execute when the Euclidean distance between two adjacent first obstacle bounding boxes is less than the vehicle width, merge two adjacent first obstacle bounding boxes to generate a second obstacle bounding box, until The Euclidean distance between the second obstacle bounding box and the adjacent first obstacle bounding box is greater than or equal to the vehicle width, or there is no first obstacle bounding box adjacent to the second obstacle bounding box.
  • a CLOSED table can be created and initialized as an empty table, a first obstacle bounding box is taken from the set contour of the first obstacle bounding box and added to the CLOSED table, and in the set set contour Delete the first obstacle bounding box, and then traverse the set set contour , once the Euclidean distance between the first obstacle bounding box in the set set contour and the first obstacle bounding box in the CLOSED table is less than the width of the vehicle, set The first obstacle bounding box of set contour is added to the CLOSED table, and aggregated with the first obstacle encircled by the Euclidean distance comparison in the CLOSED table, aggregated into a new first obstacle encircled, and then added to the CLOSED table The first obstacle bounding box is removed from the set set contour . This cycle repeats until the set set contour is empty, and then the aggregation processing of the first grid obstacle located in the road can be completed.
  • the embodiment of the present disclosure can also filter out the first grid obstacle located outside the road based on the road information and the first grid obstacle information, and determine The first grid obstacle located in the road, and aggregation processing is performed on the first grid obstacle located in the road. In this way, the number of first grid obstacles can be further reduced.
  • a fast convex hull algorithm may be used to convert the second grid obstacle into a second convex hull obstacle. In this way, unified decision-making for grid obstacles and convex hull obstacles can be realized.
  • This step can be executed by the traffic mode decider 1132 in FIG. 5 .
  • making an avoidance decision for the target convex hull obstacle includes: based on the target convex hull obstacle information, for the target meeting the preset filter condition Convex obstacles are marked with no avoidance labels or no lateral avoidance labels.
  • the trajectory generation unit 12 in FIG. 2 can ignore the target convex-hull obstacle, which can reduce trajectory generation.
  • the burden of obstacles processed by the unit 12 can increase the trajectory generation speed and improve the rationality of trajectory generation.
  • the preset filtering conditions include at least one of the following: the target convex-hull obstacle is located outside the road; the motion state of the target convex-hull obstacle satisfies the condition of no need for lateral avoidance; the target convex-hull obstacle is located on the leading line of the vehicle superior.
  • the target convex-hull obstacle information label the target convex-hull obstacle that satisfies the preset filtering conditions with no avoidance label or no lateral avoidance label, including: when the target convex-hull obstacle is outside the road, mark the target convex-hull obstacle Label the obstacle without avoidance; when the movement state of the obstacle with convex hull meets the condition of avoidance without lateral avoidance or the obstacle with convex hull is located on the leading line of the vehicle, label the obstacle with avoidance without lateral avoidance.
  • the target convex hull obstacle when the target convex hull obstacle is located outside the road, such as obstacle 1, the target convex hull obstacle has no influence on the normal driving of the driverless car 100 at all, and the target convex hull obstacle can be ignored at this time. package obstacles, and label the target convex hull obstacle without avoidance.
  • the driverless car 100 When the motion state of the target convex-hull obstacle satisfies the condition of no need for lateral avoidance, for example, when the target convex-hull obstacle crosses the road, such as a pedestrian crossing the road, the driverless car 100 only needs to wait for the pedestrian to pass, without generating a The trajectory of the detour can be marked on the pedestrian without a lateral avoidance label; another example is that the target convex obstacle changes lanes to the vehicle lane, or the longitudinal speed of the target convex obstacle is greater than the vehicle speed (such as adjacent lanes moving at high speed) Obstacles), without affecting the lane safety of the vehicle, the vehicle does not need to avoid laterally, and the above-mentioned target convex-hull obstacle can be marked with no need to avoid laterally; if the target convex-hull obstacle is located on the guidance line of the vehicle , if the obstacle 2 is located on the leading line c of the vehicle, the unmanned vehicle 100 does not need to avoid the obstacle 2 laterally,
  • the convex hull obstacle satisfying the preset filter condition can be filtered out through the convex hull obstacle filter 1133 in FIG. 5 .
  • the convex hull obstacle filter 1133 may include at least one of a road network filter based on obstacle frenet bounding boxes, a behavior semantic information filter, and a guide line filter.
  • the road network filter based on the obstacle frenet bounding box can quickly filter out the target convex hull obstacle located outside the road, and the road network filter based on the obstacle frenet bounding box can adopt the two-stage collision in the above embodiment Detection method to filter out the target convex hull obstacles located outside the road;
  • the behavioral semantic information filter can filter out the target convex hull obstacles that do not need to avoid and do not need to avoid laterally according to the semantic information contained in the target convex hull obstacles; guide lines
  • the filter filters out the target convex hull obstacles that collide with the guideline.
  • the embodiments of the present disclosure can also Make an avoidance decision of following, passing on the left or passing on the right.
  • marking the target convex hull obstacle with an avoidance label may also include: if the target convex hull obstacle is located on the vehicle guidance line, labeling the target convex hull obstacle If the target convex-hull obstacle is not located on the leading line of the vehicle, when the center of mass of the target convex-hull obstacle is on the left side of the leading line of the vehicle, mark the right-hand traffic on the target convex-hull obstacle label, when the center of mass of the target convex hull obstacle is on the right side of the leading line of the vehicle, mark the left traffic label on the target convex hull obstacle.
  • the obstacle 2 is located on the leading line c of the own vehicle, at this time, the unmanned vehicle 100 only needs to follow the obstacle 2, and label the obstacle 2 with a follow label.
  • the obstacle 3 is located in the road, but not on the leading line c of the own vehicle, and affects the safety of the lane of the driverless vehicle 100. At this time, it is necessary to pass from the left or right of the obstacle 3 to avoid the obstacle 3.
  • the relative position of the center of mass of the obstacle 3 and the guiding line c of the vehicle is detected. If the center of mass of the obstacle 3 is located on the right side of the guiding line c of the vehicle (as shown in FIG.
  • the second grid obstacle is converted into a second convex hull obstacle, that is, the grid Grid-type obstacles are converted into convex-hull-type obstacles to achieve unified decision-making for both grid-type and convex-hull type obstacles (that is, mixed-type obstacles), thereby simplifying obstacle decision-making for mixed-type obstacles
  • the process accelerates the obstacle decision-making process, so that the decision-making planning module can conveniently and quickly make obstacle decisions.
  • FIG. 8 is a partial flowchart of another vehicle decision planning method provided by an embodiment of the present disclosure.
  • the method is applicable to the situation where the unmanned vehicle generates a driving area for static obstacles and/or dynamic obstacles, and the method can be executed by a driving space generator.
  • generating a drivable area (or vehicle drivable area generation method) according to obstacle decision-making includes the following steps:
  • the environment perception information includes at least two of lane information, obstacle information and vehicle information
  • the obstacle information includes static obstacle information and/or dynamic obstacle information.
  • the lane information may include lane line information and road boundary information, which may be acquired by a vehicle-mounted camera
  • the obstacle information may include obstacle position information, obstacle size information, and obstacle motion information, wherein the obstacle position Information can be obtained by using high-precision maps and vehicle-mounted cameras/lidars, obstacle size information can be obtained by vehicle-mounted cameras, obstacle movement information can be obtained by vehicle-mounted cameras and/or laser radars
  • vehicle information can include vehicle location information and vehicle motion information, among which, the vehicle position information can be obtained by high-precision maps and vehicle positioning modules (such as GPS), and the vehicle motion information can be obtained by vehicle motion sensors (such as speed sensors and acceleration sensors, etc.) Obtain.
  • S320 Determine lane decision semantic information for each lane based on the environment perception information.
  • the semantic information of lane decision includes passing time cost and safety cost.
  • the passing time cost is used to characterize the traffic situation of the lane, for example, if the vehicle can pass through a lane quickly, the passing time of the lane is fast; the safety cost is used to characterize the safety of the lane.
  • the passing time cost of each lane can be determined according to the magnitude relationship between the vehicle's longitudinal speed and the obstacle's longitudinal speed.
  • the lane decision semantic information includes the passing time cost
  • the lane decision semantic information of each lane is determined based on the environment perception information, including: for each lane, based on the environment perception information, determining The collision time of an obstacle; the collision time is determined as the passing time cost.
  • the environmental perception information includes the position information of the vehicle and the longitudinal velocity information of the vehicle, as well as the obstacle position information and the longitudinal velocity information of the obstacle closest to the vehicle in each lane.
  • the position information of the vehicle and the obstacle According to the longitudinal speed information of the vehicle and the longitudinal speed information of the obstacle, it is judged whether the longitudinal speed of the obstacle is less than the longitudinal speed of the vehicle.
  • the longitudinal speed of the obstacle is less than the longitudinal speed of the vehicle, according to the longitudinal distance, the longitudinal speed information of the vehicle and the longitudinal speed information of the obstacle, the collision time when the vehicle collides with the obstacle in front is predicted, and the collision time is determined as the passing time cost.
  • the preset time length is determined as the passing time cost. Based on the above technical solution, the following formula can be used to calculate the passing time cost:
  • TCC is the passing time cost
  • v adv is the longitudinal velocity of the vehicle
  • v obs is the longitudinal velocity of the obstacle
  • tcc max is the preset duration, which is a fixed value and is greater than the collision time, for example, 1000 (here is only a numerical value , the unit is the same as the collision time, such as seconds or milliseconds, etc.).
  • the first obstacle in front of the vehicle is obstacle 1, and in the adjacent lane, the first obstacle in front of the vehicle is obstacle 2.
  • the longitudinal velocity of the vehicle is 5m/s
  • the longitudinal velocity of obstacle 1 is 1m/s
  • the longitudinal velocity of obstacle 2 is 10m/s.
  • the longitudinal velocity of the obstacle 1 is less than the longitudinal velocity of the vehicle, and the vehicle will collide with the obstacle 1.
  • the distance D between the vehicle and the obstacle 1 is determined to be 16m, and it can be determined according to the above formula
  • the collision time between the vehicle and the obstacle 1 is 4s, therefore, the passing time cost of the vehicle lane is 4.
  • the passing time cost of the adjacent lane is the preset time length, such as 10000. Therefore, it can be determined that the passing time cost of the adjacent lane is greater than the passing time cost of the own vehicle lane, that is, the passability of the adjacent lane is better.
  • the safety cost of each lane needs to be determined at the same time.
  • the lane decision semantic information includes the safety cost
  • the lane decision semantic information of each lane is determined based on the environment perception information, including: based on the lane information and the vehicle information, determining the vehicle lane and other lanes; for the vehicle For the lane, the first preset safety cost is determined as the safety cost; for other lanes, if it is determined based on the environmental perception information that the obstacle enters the danger zone of the vehicle within the preset time, the second preset safety cost is determined For the safety cost, if it is determined based on the environmental perception information that the obstacle does not enter the danger zone of the vehicle within the preset time, the first preset safety cost is determined as the safety cost, wherein the second preset safety cost It is different from the first preset security cost.
  • the environmental perception information includes lane information, own vehicle information and obstacle information. Based on the lane information and own vehicle information, the own vehicle lane and other lanes are determined. For the own vehicle lane, the own vehicle has absolute right of way by default, that is, the own vehicle Driveways are the safest. For other lanes, the safety judgment can be made on the obstacles in the observation area of the vehicle.
  • the preset time When it is predicted that the obstacles in the observation area of the vehicle will enter the dangerous area of the vehicle within a certain period of time in the future (that is, the preset time), it means The safety of the lane where the obstacle is at the current moment is low; when it is predicted that the obstacle in the observation area of the vehicle will not enter the danger zone of the vehicle for a period of time in the future, it means that the lane where the obstacle is at the current moment is relatively safe .
  • the safety of the lane corresponding to the second preset safety cost is lower than the safety of the lane corresponding to the first preset safety cost.
  • the second preset security cost is smaller than the first preset security cost.
  • a penalty mechanism may be used to assign values to the first preset security cost and the second preset security cost, for example, the first preset security cost is 0, and the second preset security cost is -100000.
  • the ST map (longitudinal displacement-time map) can be used to determine whether obstacles in other lanes will enter the danger zone of the vehicle in the future.
  • determine the ST map curve of the vehicle and the ST map curve of the obstacle based on the environmental perception information, determine the ST map curve of the vehicle and the ST map curve of the obstacle; determine the dangerous area of the vehicle based on the ST map curve of the vehicle; There is overlap in the dangerous area of the vehicle; if the ST map curve of the obstacle overlaps with the dangerous area of the vehicle within the preset time, it is determined that the obstacle enters the dangerous area of the vehicle within the preset time; otherwise, it is determined that the obstacle enters the dangerous area of the vehicle within the preset time did not enter the danger zone of the vehicle.
  • the ST graph curve of the own vehicle is the curve indicated by the own vehicle in the figure
  • the ST graph curve of the obstacle includes the obstacles 1, 2, 3 and 4 respectively indicated in the figure curve.
  • the dangerous area of the vehicle includes the dangerous area behind the vehicle (the area corresponding to the section L2) and the dangerous area in front of the vehicle (the area corresponding to the section L3)
  • the observation area of the vehicle includes the observation area behind the vehicle (the area corresponding to the section L1).
  • the preset time is T_e.
  • L1 is 100 meters
  • L2 is 20 meters
  • L3 is 10 meters
  • L4 is 100 meters
  • T_e is 6 seconds. Referring to Fig.
  • the ST diagram curve of the obstacle corresponding to obstacle 2 in the observation area behind the vehicle overlaps with the dangerous area behind the vehicle within the preset time T_e
  • the obstacle ST diagram curve corresponding to obstacle 1 in the observation area behind the vehicle does not overlap with the dangerous area behind the vehicle within the preset time T_e
  • the obstacle ST diagram curve corresponding to obstacle 3 in the observation area in front of the vehicle There is an overlap with the dangerous area ahead of the vehicle within the preset time T_e
  • the obstacle ST map curve corresponding to the obstacle 4 in the observation area ahead of the vehicle does not overlap with the dangerous area ahead of the vehicle within the preset time T_e.
  • the safety cost of the corresponding lane is the first 2.
  • Preset safety cost if obstacle 1 and obstacle 4 do not enter the danger zone of the vehicle within the preset time, the safety of the lane where obstacle 1 and obstacle 4 are located at the current moment is relatively high, that is, the safety of the corresponding lane
  • the sex cost is the first preset security cost.
  • the overlapping of the obstacle ST map curve with the own vehicle's danger zone includes the obstacle ST map curve being completely located in the own vehicle's danger zone, or a part of the obstacle ST map curve being located in the own vehicle's danger zone.
  • the obstacle is set to move at a constant speed
  • the ST graph curve of the obstacle is a straight line
  • the dangerous area of the vehicle is a parallelogram, all of which are convex-hull graphics.
  • the embodiments of the present disclosure can simultaneously generate a drivable area based on the passing time cost and the safety cost, thereby selecting a lane that takes both passing and safety into account.
  • each cost in the lane decision semantic information may be weighted and summed; based on the weighted sum result, a drivable area is generated.
  • a weighted sum is performed on the passing time cost and the security cost to obtain a weighted sum result. For example:
  • f is the weighted sum result (or weighted sum value)
  • f pass is the passing time cost
  • f safe is the security cost
  • w 1 is the weight of the passing time cost
  • w 2 is the weight of the safety cost.
  • w 1 and w 2 can be obtained according to simulation or actual vehicle test experiments. Based on this technical solution, the embodiment of the present disclosure may determine the lane with the largest weighted sum value as the drivable area.
  • the boundary of the drivable area is discretized to form drivable area boundary points, including left boundary points and right boundary points.
  • the drivable area may be discretized with a fixed resolution based on the Freyner coordinate system.
  • the left and right boundaries of the drivable area are generated with a fixed resolution based on the lane decision information and the curved Freyner coordinate system constructed by the center of the lane.
  • the left border represents L in the Freyner coordinate system
  • the upper and right bounds of the values represent the lower bounds of the L values in the Freyner coordinate system.
  • the vertical distance between two adjacent left boundary points or two adjacent right boundary points is the above-mentioned fixed resolution.
  • Figure 11 shows that the vehicle is in the right lane, and the result of the lane decision is also the right lane, so the left and right boundaries of the drivable area are shown in Figure 11. If the result of the lane decision is to change lanes, then the drivable area at this time includes two lanes.
  • the vehicle drivable area generation method determines the lane decision semantic information of each lane according to the environmental perception information, and converts the lane decision semantic information into the constraint boundary of the drivable area, taking into account passability and safety, and can quickly Generate a drivable area with high passability and safety, accelerate the generation of driving trajectories, and realize fast avoidance of obstacles; at the same time, both the passing time cost and the safety cost can represent the passing cost of dynamic obstacles, and the passing time cost It can also characterize the passage cost of static obstacles. Therefore, the technical solution of the present disclosure generates a drivable area based on the passage time cost and safety cost, and can realize the passage planning of dynamic obstacles and static obstacles at the same time, and is applicable to dynamic environments Handling of obstacles.
  • the lane decision semantic information also includes a traffic width cost
  • determining the lane decision semantic information of each lane based on the environment perception information includes: determining the minimum traffic width of the lane based on the lane information and static obstacle information; The minimum traffic width is determined as the traffic width cost.
  • the traffic width cost is used to represent the congestion of the lane by the static obstacles in front of the vehicle.
  • determining the minimum passage width of the lane based on the lane information and the static obstacle information includes: determining the maximum passage width of each static obstacle on the lane based on the lane information and the static obstacle information; The minimum value of the maximum traffic width of is determined as the minimum traffic width of the lane.
  • each static obstacle is projected into the Freyner coordinate system, and an SL bounding box of each obstacle is generated.
  • calculate the left and right passage widths of each static obstacle determine the maximum passage width of each static obstacle from the left and right passage widths, and determine the maximum passage width from all static obstacles
  • the greater the cost of the traffic width the smaller the degree of blockage of the lane by static obstacles. Exemplarily, as shown in FIG.
  • the vehicle lane includes obstacle 1 and obstacle 2
  • the adjacent lane includes obstacle 3 (obstacle 1, obstacle 2, and obstacle 3 are all static obstacles)
  • the maximum passage width of obstacle 1 is d1
  • the maximum passage width of obstacle 2 is d2
  • the maximum passage width of obstacle 3 is d3, where d1 is smaller than d2 and d2 is smaller than d3, therefore, the minimum passage width of the vehicle lane is d1, that is, the traffic width cost of the vehicle lane is d1, and the minimum traffic width of the adjacent lane is d3, that is, the traffic width cost of the adjacent lane is d3.
  • the degree of obstacle congestion in the adjacent lane is smaller than the degree of obstacle congestion in the own vehicle lane. Therefore, adjacent lanes can be further selected to generate drivable areas.
  • the optimal drivable area cannot be determined based on the costs in the lane decision semantic information, in order to ensure the stability of the vehicle trajectory, the optimal selection of this area is made by adding the stability cost to the lane decision semantic information.
  • Vehicle lanes generate drivable areas.
  • the lane decision semantic information also includes a stability cost, and based on the environment perception information, determining the lane decision semantic information of each lane includes: based on the lane information and the vehicle information, determining the vehicle lane and other lane; for the vehicle lane, the first preset stability cost is determined as the stability cost; for other lanes, the second preset stability cost is determined as the stability cost, wherein the second preset stability cost is the same as the first A default stability cost is different.
  • the first preset stability cost may be greater than the second preset stability cost, wherein the first preset stability cost may be 100, and the second preset stability cost may be 0.
  • each cost in the lane decision semantic information is weighted and summed to obtain the weighted sum result, which can be calculated by the following formula:
  • f narrow is the traffic width cost
  • f stable is the stability cost
  • w 3 is the weight of the traffic width cost
  • w 4 is the weight of the stability cost
  • the method further includes at least one of the following:
  • the obstacle decision semantic information includes obstacles passing on the left side or obstacles passing on the right side.
  • the preset traffic rules may include common traffic rules such as dashed and solid yellow lines, dashed and solid white lines, and lane indicator lines.
  • the vehicle will choose to change lanes, so the two-lane lane during the lane change process is firstly used as the drivable area, that is, the original drivable area.
  • the drivable area will be trimmed based on the preset traffic rules, and the drivable area defined by the black circles in Figure 13 is obtained, that is, the updated drivable area.
  • the technical solution of the present disclosure may also update the drivable area based on the kinematics and dynamics constraints of the vehicle.
  • the drivable area is updated by adding an additional drivable area when the vehicle temporarily takes the road.
  • the included angle between the heading angle of the vehicle and the heading angle of the road network is ⁇
  • the curvature of the vehicle’s lane position point is k r
  • k ADV is the trajectory curvature of the vehicle calculated by using the wheel angle ⁇ of the unmanned bicycle model, and the wheelbase of the vehicle is B, then k′ r is the curvature change rate of the road network.
  • k′ r 0.
  • the final Freyner lateral velocity of the vehicle is 0, so based on the Freyner coordinates Department of Kinesiology has In this way, the drivable area is expanded outwards based on the calculated additional drivable area, so as to update the drivable area.
  • the absolute safety of the drivable area cannot be guaranteed. Therefore, the drivable area may be affected by dynamic obstacles. The area is trimmed to ensure the safety of the remaining drivable area.
  • the obstacle semantic information may include information used to characterize the movement state of the obstacle, such as obstacle merging, obstacle crossing, obstacle parallel driving, and reverse driving. Based on the semantic information of obstacles, the car will automatically judge whether to avoid obstacles laterally.
  • the vehicle does not need to avoid the obstacle laterally; if it is determined based on the obstacle semantic information that the obstacle is too close to the vehicle lane, the vehicle needs to avoid the obstacle laterally.
  • a preset safety area is added on both sides of the drivable area (if the drivable area is adjacent to the road boundary, the preset safety area can only be increased inside the drivable area), and the obstacle prediction module is judged (The previous module, not involved in this disclosure) Whether the output trajectory of the obstacle occupies the preset safety zone, if the movement trajectory occupies the preset security zone, trim the part of the drivable area that occupies the corresponding position of the preset security zone , as shown in the clipping area in Figure 15, to update the drivable area.
  • the disclosed technical solution combines obstacle decision semantic information and ray tracing algorithms to accurately determine where static obstacles are located. area.
  • update the drivable area based on static obstacle information, obstacle decision semantic information and ray tracing algorithm including: determine the collision point between light and obstacles based on static obstacle information, obstacle decision semantic information and ray tracing algorithm , the collision point is within the drivable area; based on the collision point, the drivable area is updated.
  • determining the collision point between the light and the obstacle includes: determining the light source point and the ray projection direction based on the obstacle decision semantic information; Obstacle information, determine the range of light projection; based on the light source point, light projection direction and light projection range, scan the obstacle with light; determine the collision point between the light and the obstacle.
  • the ray tracing algorithm may use a ray casting algorithm based on the Gilbert–Johnson–Keerthi algorithm to improve solution accuracy.
  • the obstacle decision semantic information includes passing from the left side of the obstacle or passing from the right side of the obstacle.
  • the obstacle decision semantic information is passing from the left side of the obstacle, it is determined that the light source point is located on the left side of the static obstacle, and the light projection direction is perpendicular to the direction of the lane and towards the static obstacle; when the obstacle decision semantic information is from the obstacle
  • the object passes on the right side it is determined that the light source point is located on the right side of the static obstacle, and the light projection direction is perpendicular to the direction of the lane and facing the static obstacle.
  • the static obstacle information such as the location information and size information of the static obstacle, the area where the static obstacle is located can be determined, thereby determining the ray projection range.
  • the drivable area defined by each collision point is trimmed off.
  • the embodiment of the present disclosure does not limit the specific position of the light source point, and in some embodiments, the light source point may be located on a border point of the drivable area.
  • the embodiment of the present disclosure only uses the frenet bounding box to determine the ray projection range that can include the entire static obstacle, so as to ensure that the ray scans the obstacle completely, and the subsequently determined collision point is located on the static obstacle instead of Located on the boundary of the frenet bounding box.
  • there are two static obstacles in the drivable area namely obstacle 1 and obstacle 2.
  • obstacle decision semantic information of obstacle 1 it can be determined that the vehicle passes on the right side of obstacle 1
  • obstacle decision semantic information of obstacle 2 it can be determined that the vehicle passes on the left side of obstacle 2.
  • obstacle 1 based on the obstacle decision semantic information of obstacle 1, determine that the point light source is located at the right boundary point of the drivable area, and based on the static obstacle information of obstacle 1, determine the light projection range of the light source on obstacle 1 , that is, the above-mentioned ID range, so that the point light source can scan the obstacle 1 in sequence according to the ID range. Specifically, when the light collides with the obstacle 1, if the collision point is within the drivable area, the drivable area on the side away from the point light source is trimmed until the scan of the ray projection range is completed.
  • the solution accuracy of the area occupied by static obstacles in the drivable area can be improved, and the trafficability of the vehicle can be improved.
  • the embodiment of the present disclosure also provides a vehicle decision planning device.
  • the vehicle decision planning device includes a base coordinate system generator 111 , a guiding line generator 112 , an obstacle decision maker 113 and a driving space generator 114 .
  • the base coordinate system generator 111 is used to generate the base coordinate system;
  • the guide line generator 112 is used to generate the guide line under the base coordinate system to determine a general driving trajectory of the vehicle in the future;
  • the obstacle decision maker 113 used to make obstacle decisions under the constraints of the guide line;
  • the driving space generator 114 used to generate a drivable area according to the obstacle decisions.
  • Fig. 18 is a block diagram of functional modules of an obstacle decision maker in a vehicle decision planning device provided by an embodiment of the present disclosure.
  • the obstacle decision maker (or the decision-making device for avoiding obstacles) includes an information acquisition module 401 , a preprocessing module 402 , a type conversion module 403 and an avoidance decision-making module 404 .
  • the information acquiring module 401 is configured to acquire road information, first grid obstacle information of the first grid obstacle, and first convex hull obstacle information of the first convex hull obstacle;
  • the preprocessing module 402 is configured to preprocess the obstacles in the first grid based on the road information and the obstacle information in the first grid to obtain the obstacles in the second grid, wherein the number of obstacles in the second grid is less than the number of obstacles in the first grid The number of grid obstacles;
  • a type conversion module 403, configured to convert the second grid obstacle into a second convex hull obstacle
  • An avoidance decision-making module 404 configured to make an avoidance decision for the target convex-hull obstacle based on the target convex-hull obstacle information of the target convex-hull obstacle, wherein the target convex-hull obstacle includes the first convex-hull obstacle and/or the second convex-hull obstacle Two convex hull obstacles.
  • the preprocessing module 402 includes:
  • an obstacle filtering unit configured to filter out first grid obstacles located outside the road based on the road information and the first grid obstacle information; use the remaining first grid obstacles as second grid obstacles; and /or,
  • the obstacle aggregation unit is configured to determine the first grid obstacle located in the road based on the road information and the first grid obstacle information; perform aggregation processing on the first grid obstacle located in the road; The first grid obstacle acts as the second grid obstacle.
  • the obstacle filtering unit includes:
  • the road bounding box generation subunit is used to generate a road bounding box along the road traffic direction based on road information
  • a grid obstacle bounding box generating subunit configured to generate a grid obstacle bounding box of the first grid obstacle based on the information of the first grid obstacle;
  • the first grid obstacle subunit is used to determine the first grid obstacle located outside the road based on the grid obstacle bounding box and the road bounding box;
  • the first grid obstacle filtering subunit is configured to filter out first grid obstacles outside the road.
  • the road bounding box generating subunit is specifically used for:
  • the road boundary is discretized into boundary points
  • a road bounding box is generated.
  • the road bounding box generating subunit is specifically used for:
  • the road boundary is discretized, and the boundary point groups arranged at intervals along the road traffic direction are obtained.
  • Each group of boundary point groups includes the corresponding left boundary point and right boundary point in the horizontal direction, and the horizontal direction is perpendicular to the road direction of travel.
  • the road bounding box generating subunit is specifically used for:
  • a rectangular frame passing through each boundary point in any two adjacent boundary point groups is generated, and the rectangular frame is used as a road bounding box.
  • the road bounding box generating subunit is specifically used for:
  • the grid obstacle bounding box generating subunit is specifically used for:
  • the first grid obstacle subunit is specifically used for:
  • For each grid obstacle bounding box based on the grid obstacle bounding box and the road bounding box, determine the target road bounding box with the smallest Euclidean distance from the road bounding box to the grid obstacle bounding box;
  • the grid obstacle bounding box does not collide with the corresponding target road bounding box, it is determined that the first grid obstacle corresponding to the grid obstacle bounding box is located outside the road.
  • the device also includes:
  • the first grid obstacle position judging module is configured to judge whether the first grid obstacle corresponding to the grid obstacle bounding box is located outside the road if the grid obstacle bounding box collides with the corresponding target road bounding box.
  • the first grid obstacle position judging module is specifically used for:
  • the collision detection is performed by vector cross product, and it is judged whether the first grid obstacle corresponding to the grid obstacle bounding box is located outside the road.
  • the first grid obstacle position judging module is specifically used for:
  • the first grid obstacle position judging module is specifically used for:
  • the obstacle aggregation unit includes:
  • the first obstacle bounding box generating subunit is configured to generate a first obstacle bounding box of the first grid obstacle based on the first grid obstacle information of the first grid obstacle located in the road;
  • the second obstacle bounding box generation subunit is used to merge two adjacent first obstacle bounding boxes when the Euclidean distance between two adjacent first obstacle bounding boxes is less than the width of the vehicle to generate The second obstacle bounding box;
  • the execution unit which is used to use the second obstacle bounding box as the first obstacle bounding box, return to execute when the Euclidean distance between two adjacent first obstacle bounding boxes is smaller than the width of the vehicle, the two adjacent first obstacle bounding boxes
  • the first obstacle bounding box is merged to generate the second obstacle bounding box until the Euclidean distance between the second obstacle bounding box and the adjacent first obstacle bounding box is greater than or equal to the width of the vehicle, or there is no The first obstacle bounding box adjacent to the second obstacle bounding box.
  • avoidance decision module 404 includes:
  • the first decision-making unit is configured to, based on the target convex-hull obstacle information, label the target convex-hull obstacle that meets the preset filter condition without avoidance label or lateral avoidance label; and/or,
  • the second decision-making unit is configured to label the target convex-hull obstacle with an avoidance label based on the information of the target convex-hull obstacle and the guidance line of the vehicle, wherein the avoidance label includes a left pass label, a right pass label or a follow label.
  • the preset filtering conditions include at least one of the following:
  • the target convex hull obstacle is located outside the road;
  • the movement state of the target convex hull obstacle meets the condition of no need for lateral avoidance
  • the target convex obstacle is located on the leading line of the vehicle.
  • the first decision-making unit is specifically used to:
  • no lateral avoidance conditions include any of the following:
  • the target convex obstacle crosses the road
  • the target convex hull obstacle changes to the lane of the vehicle
  • the longitudinal speed of the target convex obstacle is greater than the speed of the vehicle.
  • the second decision-making unit is specifically used to:
  • target convex hull obstacle If the target convex hull obstacle is located on the leading line of the vehicle, mark the following label on the target convex hull obstacle;
  • the target convex-hull obstacle is not located on the leading line of the vehicle, when the center of mass of the target convex-hull obstacle is on the left side of the guiding line of the own vehicle, the right-hand traffic label is placed on the target convex-hull obstacle; when the target convex-hull obstacle When the center of mass of the object is on the right side of the vehicle's guiding line, mark the left passing label on the target convex hull obstacle.
  • Fig. 19 is a block diagram of functional modules of a driving space generator in a vehicle decision planning device provided by an embodiment of the present disclosure.
  • the driving space generator (or vehicle drivable area generation device) includes a perception information acquisition module 501 , a lane decision semantic information determination module 502 and a drivable area generation module 503 .
  • the perception information acquisition module 501 is used to obtain environment perception information, wherein the environment perception information includes at least two of lane information, obstacle information and own vehicle information, and the obstacle information includes static obstacle information and/or dynamic obstacle information item information;
  • Lane decision semantic information determination module 502 configured to determine the lane decision semantic information of each lane based on the environment perception information, wherein the lane decision semantic information includes passing time cost and safety cost;
  • a drivable area generation module 503 is configured to generate a drivable area based on the lane decision semantic information.
  • the lane decision semantic information determination module 502 is specifically used to:
  • the lane decision semantic information determination module 502 is also used for:
  • the preset duration is determined as the passing time cost.
  • the lane decision semantic information determination module 502 is specifically configured to:
  • the first preset safety cost is determined as the safety cost
  • the second preset safety cost is determined as the safety cost; Assuming that the vehicle has not entered the danger zone within a certain period of time, the first preset safety cost is determined as the safety cost, wherein the second preset safety cost is different from the first preset safety cost.
  • the lane decision semantic information determination module 502 is specifically used to:
  • the ST map curve of the obstacle overlaps with the dangerous zone of the vehicle within the preset time, it is determined that the obstacle has entered the dangerous zone of the vehicle within the preset time; otherwise, it is determined that the obstacle has not entered the dangerous zone of the vehicle within the preset time .
  • the lane decision semantic information also includes a traffic width cost, and the lane decision semantic information determination module 502 is specifically used for:
  • the lane decision semantic information determination module 502 is specifically used to:
  • the minimum value among the maximum passage widths of the static obstacles is determined as the minimum passage width of the lane.
  • the lane decision semantic information also includes a stability cost, and the lane decision semantic information determination module 502 is specifically used for:
  • the first preset stability cost is determined as the stability cost
  • a second preset stability cost is determined as the stability cost, wherein the second preset stability cost is different from the first preset stability cost.
  • the drivable area generating module 503 is specifically used to:
  • the above-mentioned device also includes:
  • the discretization module is used to discretize the boundary of the drivable area after the drivable area is generated based on the lane decision semantic information.
  • the above device further includes a drivable area update module, which is specifically used for at least one of the following update operations after generating the drivable area based on the lane decision semantic information:
  • the obstacle decision semantic information includes passing from the left side of the obstacle or from the right side of the obstacle.
  • the drivable area update module is specifically used for:
  • the drivable area update module is specifically used for:
  • the drivable area is updated.
  • the drivable area update module is specifically used for:
  • the vehicle decision-making planning device disclosed in the above embodiments can execute the vehicle decision-making planning method disclosed in the above embodiments, and has the same or corresponding beneficial effects. In order to avoid repetition, details are not repeated here.
  • An embodiment of the present disclosure also provides an electronic device, including: a memory and one or more processors; wherein, the memory is connected to the one or more processors in communication, and the memory stores information that can be executed by the one or more processors. Instructions, when the instructions are executed by one or more processors, the electronic device is used to implement the method described in any embodiment of the present disclosure.
  • FIG. 20 is a schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure.
  • an electronic device 600 includes a central processing unit (CPU) 601, which can operate according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage section 608 into a random access memory (RAM) 603 Instead, various processes in the aforementioned embodiments are executed.
  • the RAM 603 various programs and data necessary for the operation of the electronic device 600 are also stored.
  • the CPU 601 , ROM 602 , and RAM 603 are connected to each other via a bus 604 .
  • An input/output (I/O) interface 605 is also connected to the bus 604 .
  • the following components are connected to the I/O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 608 including a hard disk, etc. and a communication section 609 including a network interface card such as a LAN card, a modem, or the like.
  • the communication section 609 performs communication processing via a network such as the Internet.
  • a drive 610 is also connected to the I/O interface 605 as needed.
  • a removable medium 611 such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc. is mounted on the drive 610 as necessary so that a computer program read therefrom is installed into the storage section 608 as necessary.
  • the methods described above can be implemented as a computer software program.
  • the embodiments of the present disclosure include a computer program product including a computer program tangibly embodied on its readable medium, the computer program including program codes for executing the aforementioned obstacle avoidance method.
  • the computer program may be downloaded and installed from a network via the communication portion 609 and/or installed from a removable medium 611 .
  • each block in a roadmap or block diagram may represent a module, program segment, or portion of code that contains one or more executable instruction.
  • the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.
  • the units or modules involved in the embodiments described in the present disclosure may be implemented by means of software or hardware.
  • the described units or modules may also be set in the processor, and the names of these units or modules do not constitute limitations on the units or modules themselves in some cases.
  • an embodiment of the present disclosure also provides a computer-readable storage medium, which may be the computer-readable storage medium contained in the device described in the above implementation manner; computer-readable storage media stored in the device.
  • the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a computing device, they can be used to implement the method described in any embodiment of the present disclosure.
  • a road bounding box along the road traffic direction including:
  • the road bounding box is generated.
  • the road boundary is discretized into boundary points, including:
  • the road boundary is discretized to obtain boundary point groups arranged at intervals along the road traffic direction, wherein each group of boundary point groups includes corresponding left boundary points and right boundary points in the transverse direction, and the The transverse direction is perpendicular to the direction of road traffic.
  • the road boundary is discretized to obtain boundary point groups arranged at intervals along the road traffic direction, including:
  • generating a grid obstacle bounding box of the first grid obstacle includes:
  • the grid obstacle bounding box is generated based on the grid obstacle outline.
  • determining the first grid obstacle located outside the road includes:
  • the grid obstacle bounding box does not collide with the corresponding target road bounding box, it is determined that the first grid obstacle corresponding to the grid obstacle bounding box is outside the road.
  • the method also includes:
  • the grid obstacle bounding box collides with the corresponding target road bounding box, it is determined whether the first grid obstacle corresponding to the grid obstacle bounding box is located outside the road.
  • Judging whether the first grid obstacle corresponding to the grid obstacle bounding box is located outside the road includes:
  • the collision detection is performed by vector cross product, and it is judged whether the first grid obstacle corresponding to the grid obstacle bounding box is located outside the road.
  • Performing collision detection based on the boundary point of the target road bounding box and the grid obstacle bounding box by vector cross product, and judging whether the first grid obstacle corresponding to the grid obstacle bounding box is located outside the road include:
  • the method also includes:
  • Aggregating the first grid obstacles located in the road includes:
  • the second obstacle bounding box as the first obstacle bounding box, return to execute when the Euclidean distance between two adjacent first obstacle bounding boxes is smaller than the vehicle width, set the two adjacent first obstacle bounding boxes
  • the first obstacle bounding box is merged to generate the second obstacle bounding box until the Euclidean distance between the second obstacle bounding box and the adjacent first obstacle bounding box is greater than or equal to the width of the vehicle, or there is no The first obstacle bounding box adjacent to the second obstacle bounding box.
  • an avoidance decision is made for the target convex-hull obstacle, including:
  • the target convex-hull obstacle information Based on the target convex-hull obstacle information, label the target convex-hull obstacle that satisfies the preset filter condition without avoidance or lateral avoidance; and/or,
  • an avoidance label is attached to the target convex obstacle, wherein the avoidance label includes a left passing label, a right passing label or a following label.
  • the preset filtering conditions include at least one of the following:
  • the target convex hull obstacle is located outside the road;
  • the motion state of the target convex hull obstacle meets the condition of no need for lateral avoidance
  • the target convex obstacle is located on the leading line of the vehicle.
  • the target convex-hull obstacle that satisfies the preset filter condition is marked with no avoidance label or no lateral avoidance label, including:
  • the conditions that do not require lateral avoidance include any of the following:
  • the target convex hull obstacle crosses the road
  • the target convex hull obstacle changes lanes to the own vehicle lane
  • the longitudinal speed of the target convex hull obstacle is greater than the speed of the own vehicle.
  • label the target convex hull obstacle with an avoidance label including:
  • the follow-up label is applied to the target convex-hull obstacle
  • the target convex obstacle is not located on the vehicle guide line, when the center of mass of the target convex obstacle is on the left side of the vehicle guide line, mark the target convex obstacle with As for the right-side traffic label, when the center of mass of the target convex-hull obstacle is on the right side of the vehicle guiding line, the left-side traffic label is attached to the target convex-hull obstacle.
  • the method also includes:
  • the preset time length is determined as the passing time cost.
  • the method also includes:
  • the ST map curve of the obstacle overlaps with the dangerous zone of the own vehicle within the preset time, it is determined that the obstacle enters the dangerous zone of the own vehicle within the preset time; otherwise, it is determined that the obstacle is within the dangerous zone of the own vehicle. Did not enter the danger zone of the vehicle within the preset time.
  • the lane decision semantic information also includes traffic width cost, and based on the environment perception information, the lane decision semantic information of each lane is determined, including:
  • the minimum passage width is determined as the passage width cost.
  • determine the minimum traffic width of the lane including:
  • the minimum value among the maximum passage widths of the static obstacles is determined as the minimum passage width of the lane.
  • the lane decision semantic information also includes a stability cost, and based on the environment perception information, the lane decision semantic information of each lane is determined, including:
  • a second preset stability cost is determined as the stability cost, wherein the second preset stability cost is different from the first preset stability cost.
  • a drivable area is generated, including:
  • the method further includes:
  • the boundary of the drivable area is discretized.
  • the method further includes at least one of the following:
  • the drivable area is updated based on the static obstacle information, obstacle decision semantic information and a ray tracing algorithm, where the obstacle decision semantic information includes passing from the left side of the obstacle or passing from the right side of the obstacle.
  • the drivable area is updated, including:
  • the obstacle decision semantic information and the ray tracing algorithm determine the collision point between the light and the obstacle, and the collision point is located in the drivable area;
  • the drivable area is updated.
  • obstacle decision semantic information Based on the static obstacle information, obstacle decision semantic information and ray tracing algorithm, determine the collision point between the light and the obstacle, including:
  • a decision-making method for avoiding obstacles comprising:
  • the target convex-hull obstacle includes the first convex-hull obstacle and/or the The second convex hull obstacle.
  • a method for generating a vehicle drivable area comprising:
  • the environment awareness information includes at least two of lane information, obstacle information and vehicle information, and the obstacle information includes static obstacle information and/or dynamic obstacle information;
  • lane decision semantic information Based on the environment perception information, determine lane decision semantic information of each lane, wherein the lane decision semantic information includes passing time cost and safety cost;
  • a drivable area is generated.
  • the disclosure converts grid-type obstacles into convex-hull-type obstacles, and realizes unified decision-making for grid-type and convex-hull-type obstacles, thereby simplifying the obstacle decision-making process for mixed-type obstacles and speeding up
  • the obstacle decision-making process enables the decision-making planning module to make obstacle decisions conveniently and quickly, and has strong industrial applicability.

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Abstract

一种车辆决策规划方法、装置、设备及介质。该方法包括：生成基坐标系；在基坐标系下生成引导线，以决策车辆未来的一个大致行驶轨迹；在引导线的约束下进行障碍物决策；根据障碍物决策生成可行驶区域。该方法能够实现对栅格类型和凸包类型障碍物的统一决策，简化混合类型障碍物的障碍物决策流程，加速障碍物决策过程，使得决策规划模块能够方便、快速地进行障碍物决策。

Description

车辆决策规划方法、装置、设备及介质
本公开要求于2021年8月25日提交中国专利局、申请号为202110984268.3、发明名称为“避让障碍物的决策方法、装置、设备及介质”的中国专利申请的优先权，以及于2021年9月17日提交中国专利局、申请号为202111095293.2、发明名称为“车辆可行驶区域生成方法、装置、设备及介质”的中国专利申请的优先权，其全部内容通过引用结合在本公开中。
技术领域
本公开涉及无人驾驶技术领域，尤其涉及一种车辆决策规划方法、装置、设备及介质。
背景技术
随着车辆智能化技术的发展，无人车自动控制技术逐渐成为车辆研究领域的一个热点。自动驾驶***需要规划出平顺、安全以及车辆可通行的路径，保证车辆与障碍物不会发生碰撞。
通常，自动驾驶***的感知模块会输出两种类型的障碍物，一种是含有丰富语义信息的凸包障碍物，另一种是不含有语义信息的栅格障碍物。对于凸包障碍物而言，决策规划模块能够比较方便地进行障碍物决策，但是对于离散度较高且数量较为庞大的栅格障碍物而言，决策规划模块难以方便、快速地进行障碍物决策，从而导致决策规划模块对混合类型障碍物进行障碍物决策较为困难。
发明内容
为了解决上述技术问题或者至少部分地解决上述技术问题，本公开提供了一种车辆决策规划方法、装置、设备及介质。
本公开实施例提供了一种车辆决策规划方法，包括：
生成基坐标系；
在所述基坐标系下生成引导线，以决策车辆未来的一个大致行驶轨迹；
在所述引导线的约束下，进行障碍物决策；
根据障碍物决策生成可行驶区域。
在一些实施例中，进行障碍物决策，包括：
获取道路信息、第一栅格障碍物的第一栅格障碍物信息和第一凸包障碍物的第一凸包障碍物信息；
基于所述道路信息和所述第一栅格障碍物信息，对所述第一栅格障碍物进行预处理，得到第二栅格障碍物；
将所述第二栅格障碍物转换成第二凸包障碍物；
基于目标凸包障碍物的目标凸包障碍物信息，对所述目标凸包障碍物做出避让决策，其中，所述目标凸包障碍物包括所述第一凸包障碍物和/或所述第二凸包障碍物。
在一些实施例中，根据障碍物决策生成可行驶区域，包括：
获取环境感知信息，其中，所述环境感知信息包括车道信息、障碍物信息和本车信息中的至少两个，所述障碍物信息包括静态障碍物信息和/或动态障碍物信息；
基于所述环境感知信息，确定每个车道的车道决策语义信息，其中，所述车道决策语义信息包括通过时间代价和安全性代价；
基于所述车道决策语义信息，生成可行驶区域。
本公开实施例提供了一种车辆决策规划装置，包括：
基坐标系生成器，用于生成基坐标系；
引导线生成器，用于在所述基坐标系下生成引导线，以决策车辆未来的一个大致行驶轨迹；
障碍物决策器，用于在所述引导线的约束下，进行障碍物决策；
行驶空间生成器，用于根据障碍物决策生成可行驶区域。
在一些实施例中，障碍物决策器包括：
信息获取模块，用于获取道路信息、第一栅格障碍物的第一栅格障碍物信息和第一凸包障碍物的第一凸包障碍物信息；
预处理模块，用于基于所述道路信息和所述第一栅格障碍物信息，对所述第一栅格障碍物进行预处理，得到第二栅格障碍物，其中，所述第二栅格障碍物的数量小于所述第一栅格障碍物的数量；
类型转换模块，用于将所述第二栅格障碍物转换成第二凸包障碍物；
避让决策模块，用于基于目标凸包障碍物的目标凸包障碍物信息，对所述目标凸包障碍物做出避让决策，其中，所述目标凸包障碍物包括所述第一凸包障碍物和/或所述第二凸包障碍物。
在一些实施例中，行驶空间生成器包括：
感知信息获取模块，用于获取环境感知信息，其中，所述环境感知信息包括车道信息、障碍物信息和本车信息中的至少两个，所述障碍物信息包括静态障碍物信息和/或动态障碍物信息；
车道决策语义信息确定模块，用于基于所述环境感知信息，确定每个车道的车道决策语义信息，其中，所述车道决策语义信息包括通过时间代价和安全性代价；
可行驶区域生成模块，用于基于所述车道决策语义信息，生成可行驶区域。
本公开实施例提供了一种电子设备，包括：
存储器以及一个或多个处理器；
其中，所述存储器与所述一个或多个处理器通信连接，所述存储器中存储有可被所述一个或多个处理器执行的指令，所述指令被所述一个或多个处理器执行时，所述电子设备用于实现本公开任一实施例提供的车辆决策规划方法。
本公开实施例提供了一种计算机可读存储介质，其上存储有计算机可执行指令，当所述计算机可执行指令被计算装置执行时，可用来实现本公开任一实施例提供的车辆决策规划方法。
本公开实施例提供的技术方案与现有技术相比具有如下优点：
一、在对第一栅格障碍物进行预处理，得到第二栅格障碍物之后，通过将第二栅格障碍物转换成第二凸包障碍物，即将栅格类型的障碍物转换成凸包类型的障碍物，实现对栅格类型和凸包类型两种类型障碍物(即混合类型障碍物)的统一决策，从而能够简化混合类型障碍物的障碍物决策流程，加速障碍物决策过程，使得决策规划模块能够方便、快速地进行障碍物决策。
二、根据环境感知信息确定每个车道的车道决策语义信息，将车道决策语义信息转换成可行驶区域的约束边界，兼顾通过性和安全性，能够快速生成通过性和安全性高的可行驶区域，加速行驶轨迹的生成，实现对障碍物的快速避让；同时，通过时间代价和安全性代价均能够表征对动态障碍物的通行代价，且通过时间代价还能表征对静态障碍物的通行代价，因此，本公开技术方案基于通过时间代价和安全性代价生成可行驶区域，可以同时实现对动态障碍物和静态障碍物的通行规划，可适用动态环境中障碍物的处理。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。
为了更清楚地说明本公开实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，对于本领域普通技术人员而言，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本公开实施例提供的一种车辆决策规划方法的流程示意图；
图2为本公开实施例提供的决策规划模块的功能模块框图；
图3为本公开实施例提供的车辆决策规划方法适用场景的示意图；
图4为本公开实施例提供的一种车辆决策规划方法的部分流程示意图；
图5为本公开实施例提供的障碍物决策器的功能模块框图；
图6为本公开实施例提供的道路包围盒的示意图；
图7为本公开实施例提供的避让决策时的场景图；
图8为本公开实施例提供的另一种车辆决策规划方法的部分流程示意图；
图9为本公开实施例提供的车道通过时间代价对应的场景图；
图10为本公开实施例提供的车道安全性判断的ST图；
图11为本公开实施例提供的可行驶区域边界离散化的示意图；
图12为本公开实施例提供的车道宽度通行代价对应的场景图；
图13为本公开实施例提供的一种基于预设交通规则更新可行驶区域的示意图；
图14为本公开实施例提供的一种车辆的运动学和动力学约束更新可行驶区域的示意图；
图15为本公开实施例提供的一种障碍物语义信息和预设安全区更新可行驶区域的示意图；
图16为本公开实施例提供的生成frenet包围盒的示意图；
图17为本公开实施例提供的一种静态障碍物信息、障碍物决策语义信息和光线追踪算法更新可行驶区域的示意图；
图18为本公开实施例提供的车辆决策规划装置中的障碍物决策器的功能模块框图；
图19为本公开实施例提供的车辆决策规划装置中的行驶空间生成器的功能模块框图；
图20为本公开实施例提供的适于用来实现本公开实施方式的电子设备的结构示意图。
具体实施方式
为了能够更清楚地理解本公开的上述目的、特征和优点，下面将对本公开的方案进行进一步描述。需要说明的是，在不冲突的情况下，本公开的实施例及实施例中的特征可以相互组合。
在下面的描述中阐述了很多具体细节以便于充分理解本公开，但本公开还可以采用其他不同于在此描述的方式来实施；显然，说明书中的实施例只是本公开的一部分实施例，而不是全部的实施例。
图1为本公开实施例提供的一种车辆决策规划方法的流程示意图。该方法适用于无人驾驶汽车的障碍物决策以及可行驶区域的生成的情况。如图1所示，该方法包括如下步骤：
S110、生成基坐标系。
S120、在基坐标系下生成引导线，以决策车辆未来的一个大致行驶轨迹。
S130、在引导线的约束下，进行障碍物决策。
S140、根据障碍物决策生成可行驶区域。
本实施例中，通过生成基坐标系，使得后续的引导线、障碍物决策数据以及可行驶区域都在该基坐标系下生成，从而为车辆以及障碍物的定位提供基准。基坐标系可以为frenet坐标系。在生成引导线后，根据引导线指示的大致行驶轨迹，对该大致行驶轨迹上的障碍进行障碍物决策，从而对障碍物进行避让。之后，根据障碍物决策可以确定车辆需从障碍物左侧通过，还是从障碍物右侧通过，还是跟随障碍物，从而确定车辆的可行驶区域。
图2示出了决策规划模块的功能模块框图。如图2所示，决策规划模块1可包括约束生成单元11、轨迹生成单元12和轨迹平滑单元13。
在一些实施例中，约束生成单元11包括基坐标系生成器111、引导线生成器112、障碍物决策器113和行驶空间生成器114。其中，基坐标系生成器111用于生成基坐标系，如frenet坐标系等；引导线生成器112用于生成引导线，以决策车辆未来的一个大致行驶轨迹；障碍物决策器113用于进行障碍物决策；行驶空间生成器用于根据障碍物决策生成可行驶区域。在一些实施例中，轨迹生成单元12用于根据可行驶区域生成无人驾驶汽车的行驶轨迹；轨迹平滑单元13用于对行驶轨迹进行平滑处理。
在一些实施例中，障碍物决策器113具体用于获取道路信息、第一栅格障碍物的第一栅格障碍物信息和第一凸包障碍物的第一凸包障碍物信息；基于道路信息和第一栅格障碍物信息，对第一栅格障碍物进行预处理，得到第二栅格障碍物；将第二栅格障碍物转换成第二凸包障碍物；基于目标凸包障碍物的目标凸包障碍物信息，对目标凸包障碍物做出避让决策。
在一些实施例中，行驶空间生成器114具体用于获取环境感知信息；基于环境感知信息，确定每个车道的车道决策语义信息，其中，车道决策语义信息包括通过时间代价和安全性代价；基于车道决策语义信息，生成可行驶区域。
基于上述技术方案，本公开实施例提供了一种车辆决策规划方法，该方法适用于无人驾驶汽车对道路环境下的栅格障碍物和凸包障碍物等静态障碍物和/或动态障碍物进行决策的情况。图3示出了车辆决策规划方法的适用场景，参见图3，无人驾驶汽车100的前方存在凸包障碍物200(包括静态障碍物和动态障碍物)和栅格障碍物300，无人驾驶汽车100可通过获取凸包障碍物200和栅格障碍物300的障碍物信息，将栅格障碍物300的类型转换成凸包类型，从而实现凸包障碍物200和栅格障碍物300的统一决策。该方法可应用于无人驾驶汽车，具体应用于无人驾驶汽车自动驾驶***中的决策规划模块。基于本公开实施例提供的车辆决策规划方法，可实现混合类型障碍物的统一决策，方便、快速地进行障碍物决策。
基于上述技术方案，图4为本公开实施例提供的车辆决策规划方法的部分流程示意图。如图4所示，进行障碍物决策(或者避让障碍物的决策方法)包括如下步骤：
S210、获取道路信息、第一栅格障碍物的第一栅格障碍物信息和第一凸包障碍物的第一凸包障碍物信息。
本公开实施例中，栅格障碍物即栅格类型的障碍物，凸包障碍物即凸包类型的障碍物。
在一些实施例中，可通过高精度地图或车载摄像头获取道路信息，道路信息可包括道路边界信息和道路曲率信息等。同时，可通过车辆感知模块(例如车载摄像头和激光雷达等)和定位模块获取障碍物信息，障碍物信息可包括障碍物类型信息、障碍物尺寸信息和障碍物位置信息等。其中，障碍物类型信息可以为障碍物类型标识符，通过预先定义不同的障碍物类型标识符来区分障碍物类型；障碍物类型信息也可以为障碍物数据格式，车辆感知模块在感知到障碍物后，对障碍物数据进行处理，将不同类型障碍物的数据以不同的障碍物数据格式进行存储，决策规划模块从而在获取障碍物信息时，通过障碍物数据格式来区分障碍物类型，例如，栅格障碍物的障碍物数据格式为“.ogm”，凸包障碍物的障碍物数据格式为“.mot”。如此，基于障碍物类型信息，确定第一栅格障碍物和第一凸包障碍物，得到第一栅格障碍物信息和第一凸包障碍物信息。
S220、基于道路信息和第一栅格障碍物信息，对第一栅格障碍物进行预处理，得到第二栅格障碍物。
S230、将第二栅格障碍物转换成第二凸包障碍物。
图5示出了障碍物决策器的功能模块框图。如图5所示，障碍物决策器113可包括栅格障碍物处理器1131、通行方式决策器1132和凸包障碍物过滤器1133。
其中，栅格障碍物处理器1131执行S220、基于道路信息和第一栅格障碍物信息，对第一栅格障碍物进行预处理，得到第二栅格障碍物；以及S230、将第二栅格障碍物转换成第二凸包障碍物。
在S220中，对第一栅格障碍物进行预处理可用于减少数据计算量，简化障碍物决策流程，可包括以下至少一个步骤：生成第一栅格障碍物的栅格障碍物轮廓；生成第一栅格障碍物的障碍物包围盒；过滤掉位于道路之外的第一栅格障碍物；以及对位于道路内的第一栅格障碍物进行聚合处理。
在一些实施例中，对第一栅格障碍物进行预处理，以使预处理后得到的第二栅格障碍物的数量小于第一栅格障碍物的数量，从而便于下游模块对障碍物的计算。
本公开实施例可通过过滤掉位于道路之外的第一栅格障碍物来减少第一栅格障碍物的数量。在一些实施例中，基于道路信息和第一栅格障碍物信息，对第一栅格障碍物进行预处理，得到第二栅格障碍物，可包括如下步骤：
S221、基于道路信息和第一栅格障碍物信息，过滤掉位于道路之外的第一栅格障碍物。
在一些实施例中，基于道路信息和第一栅格障碍物信息，过滤掉位于道路之外的第一栅格障碍物，可包括如下步骤：
S2211、基于道路信息，生成沿道路通行方向的道路包围盒。
在一些实施例中，基于道路信息，将道路边界离散成边界点；基于边界点，生成道路包围盒。本公开实施例对道路包围盒的形状是基于无人车车体右手坐标系的轴对称包围盒，只要可以经过边界点且覆盖道路即可，以便后续进行第一栅格障碍物是否位于道路之外的判断。
具体的，参考图6，基于道路信息，确定道路边界和道路边界曲率；基于道路边界曲率，对道路边界进行离散化，得到沿道路通行方向间隔排布的边界点组，其中，每组边界点组包括在横向上对应的左边界点a和右边界点a’，横向垂直于道路通行方向；基于任意相邻两组边界点组，生成经过任意相邻两组边界点组中各边界点的矩形框b，并将矩形框b作为道路包围盒。
在一些实施例中，矩形框b相邻的两条边中，一条边平行于本车行驶方向，即无人驾驶汽车100的行驶方向x， 另一条边垂直于本车行驶方向，即无人驾驶汽车100行驶的法线方向y。同时，同一道路边界上的相邻两个边界点之间的距离与道路边界曲率负相关，即道路边界曲率越大，弯曲程度越大，道路边界上的相邻两个边界点之间的距离越小。如此，可保证道路包围盒完全覆盖道路，避免后续将部分位于道路内的第一栅格障碍物判定为位于道路之外而使其被过滤掉，从而避免对障碍物决策产生影响。
在一些实施例中，基于道路边界曲率，对道路边界进行离散化，得到沿道路通行方向间隔排布的边界点组，包括：以本车当前位置作为起始路点；获取起始路点在横向上对应的一组边界点组；基于道路边界曲率，沿道路通行方向选择下一路点，其中，相邻两个路点之间的距离与道路边界曲率负相关；将下一路点作为起始路点，返回执行获取起始路点在横向上对应的一组边界点组，直至在道路通行方向上下一路点到本车当前位置的距离大于预设距离阈值，将当前获得的所有边界点组确定为边界点组。其中，预设距离阈值可以根据车辆感知障碍物的最大范围确定。
基于上述技术方案，在本公开一具体实施例中，每4个边界点(道路左侧相邻两个边界点，道路右侧对应相邻两个边界点)能够生成一个道路包围盒B R＝{b min,b max,b left,0,b left,1,b right,0,b right,1}，其中，b min和b max分别是道路包围盒的最小、最大坐标点，b left,0,b left,1,b right,0,b right,1分别是道路的左侧和右侧的坐标点，那么可以整个道路可以使用n个道路包围盒表示，生成道路包围盒序列B road_list＝{B R0,B R1,L,B Rn}。本公开实施例中，道路可以是车辆行驶路线中的一个路线片段，可根据车辆定位信息确定本车所在的路线片段，对道路边界进行离散化，即为对本车所在的路线片段的边界进行离散化。示例性的，定义道路边界点的列表为S，且初始化为空表，道路包围盒序列B road_list也初始化为空，从本车所在的路线片段开始离散道路边界，获取路线片段的第一个路点(可为本车当前位置)，然后获取第一个路点在横向上所对应的左边界点和右边界点，将当前的左边界点和右边界点加入列表S；基于道路边界曲率，沿道路通行方向选择下一路点，检查第一个路点沿道路通行方向到下一路点的距离是否小于或等于预设距离阈值，如果该距离小于或等于预设距离阈值，则获取下一路点在横向上所对应的左边界点和右边界点并加入列表S；基于道路边界曲率，沿道路通行方向继续选择下一路点，以此直至第一个路点沿道路通行方向到下一路点的距离大于预设距离阈值，停止获取左边界点和右边界点，基于最后更新的列表S，生成路网包围盒序列B road_list。
S2212、基于第一栅格障碍物信息，生成第一栅格障碍物的栅格障碍物包围盒。
在一些实施例中，基于第一栅格障碍物信息，生成第一栅格障碍物的栅格障碍物轮廓；基于栅格障碍物轮廓生成栅格障碍物包围盒。
具体的，基于第一栅格障碍物信息，采用suzuki轮廓跟踪算法，将第一栅格障碍物生成封闭的轮廓图形，即栅格障碍物轮廓。如此，可避免对所有的原始点云栅格障碍物数据进行处理，可大大降低对处理器和传感器的硬件要求。示例性的，栅格障碍物轮廓Ω＝{p 0,p 1,L,p n}，p 0是栅格障碍物轮廓的一个坐标点，该栅格障碍物轮廓有n个坐标点组成。栅格障碍物包围盒B＝{P min,P max}，栅格障碍物包围盒的4个顶点可以由2个坐标点p min＝[x min,y min]和p max＝[x max,y max]的坐标值组成，其中：
Figure PCTCN2022088532-appb-000001
x 0,x 1,…，x n为栅格障碍物轮廓中n个坐标点的x坐标，y 0,y 1,…，y n为栅格障碍物轮廓中n个坐标点的y坐标。
S2213、基于栅格障碍物包围盒和道路包围盒，确定位于道路之外的第一栅格障碍物。
本公开实施例可对第一栅格障碍物进行两阶段的碰撞检测，以快速、准确地确定位于道路之外的第一栅格障碍物。示例性的，可以先对栅格障碍物进行粗糙碰撞检测，从而快速过滤掉位于道路之外的第一栅格障碍物，且减少碰撞检测的计算量；对于粗糙碰撞检测确定的发生碰撞的第一栅格障碍物，再进行精细碰撞检测，从而进一步确定位于道路之外的第一栅格障碍物，以确保过滤之后剩余的第一栅格障碍物均位于道路内。
对于上述粗糙碰撞检测，在一些实施例中，对于每个栅格障碍物包围盒，基于栅格障碍物包围盒和道路包围盒，从道路包围盒中确定到栅格障碍物包围盒欧氏距离最小的目标道路包围盒；将栅格障碍物包围盒与对应的目标道路包围盒进行碰撞检测；如果栅格障碍物包围盒与对应的目标道路包围盒没有碰撞，则确定栅格障碍物包围盒对应的第一栅格障碍物位于道路之外。道路包围盒到栅格障碍物包围盒的欧氏距离相对较小时，表明道路包围盒与栅格障碍物包围盒发生碰撞的可能性越大，如果上述欧式距离较小时所对应的道路包围盒与栅格障碍物包围盒都没发生碰撞，那么上述欧式距离较小时所对应的道路包围盒与栅格障碍物更不会发生碰撞。因此，通过从道路包围盒中确定到栅格障碍物包围盒欧氏距离最小的目标道路包围盒，与栅格障碍物包围盒进行碰撞检测，可减少碰撞检测的计算量，从而加快障碍物决策速度。在一些实施例中，只需检测栅格障碍物包围盒的顶点是否位于目标道路包围盒上或目标道路包围盒内即可。例如，当栅格障碍物包围盒的顶点均位于目标道路包围盒之外时，确定该栅格障碍物包围盒对应的第一栅格障碍物位于道路之外；当栅格障碍物包围盒的顶点位于目标道路包围盒上或目标道路包围盒内时，确定该栅格障碍物包围盒对应的第一栅格障碍物位于道路内。
对于上述精细碰撞检测，在一些实施例中，如果栅格障碍物包围盒与对应的目标道路包围盒发生碰撞，判断栅格障碍物包围盒对应的第一栅格障碍物是否位于道路之外。在一些实施例中，基于目标道路包围盒的边界点和栅格障碍物包围盒通过向量叉积进行碰撞检测，判断栅格障碍物包围盒对应的第一栅格障碍物是否位于道路之外。具体的，确定边界点向量；确定栅格障碍物包围盒的顶点向量；当栅格障碍物包围盒的顶点向量与边界点向量的叉积均大于0时，确定栅格障碍物包围盒对应的第一栅格障碍物位于道路之外。当栅格障碍物包围盒的顶点向量与边界点向量的叉积小于或等于0时，确定栅格障碍物包围盒对应的第一栅格障碍物位于道路内。其中，边界点向量包括目标道路包围盒的两个左边界点构成的左边界向量，以及目标道路包围盒的两个右边界点构成的右边界向量，栅格障碍物包围盒的顶点向量为栅格障碍物包围盒的顶点与目标道路包围盒的一边界点构成的向量，且该边界点为参与叉积运算的边界点向量对应的一个边界点，例如，顶点向量与右边界向量叉积时，顶点向量中的边界点为右边界向量对应的一个边界点。示例性的，栅格障碍物包围盒B＝{P min,P max}，目标道路包围盒B R＝{b min,b max,b left,0,b left,1,b right,0,b right,1}，左边界向量v left＝b left,1-b left,0，右边界向量v right＝b right,1-b right,0，然后遍历栅格障碍物包围盒B的四个顶点，构成四个顶点向量，将四个顶点向量分别与左边界向量或右边界向量叉积，根据叉积结果判断栅格障碍物包围盒对应的第一栅格障碍物位于道路之外。例如，B的其中一个顶点p 0＝[x min,y min]，那么右边界向量和顶点向量叉积c 1＝cross(p 0-b right,0,v right)，如果c 1＞0，那么顶点p 0在道路右边界的右侧；否则，顶点p 0在道路右边界上或道路右边界的左侧。同理，可判断栅格障碍物包围盒的其他顶点在道路右边界上或道路右边界的左侧或右侧。如此，能够判断栅格障碍物包围盒对应的第一栅格障碍物是否位于道路之外。
S2214、过滤掉位于道路之外的第一栅格障碍物。
S222、将剩余第一栅格障碍物作为第二栅格障碍物。
另外，本公开实施例也可通过对位于道路内第一栅格障碍物进行聚合处理来减少第一栅格障碍物的数量。在一些实施例中，基于道路信息和第一栅格障碍物信息，对第一栅格障碍物进行预处理，得到第二栅格障碍物，也可包括如下步骤：
S223、基于道路信息和第一栅格障碍物信息，确定位于道路内的第一栅格障碍物。
本实施例中位于道路内的第一栅格障碍物可通过上述实施例中判断第一栅格障碍物是否位于道路之外的方法确定，此处不再赘述。
S224、对位于道路内的第一栅格障碍物进行聚合处理。
在一些实施例中，基于位于道路内的第一栅格障碍物的第一栅格障碍物信息，生成其中第一栅格障碍物的第一障碍物包围盒；当相邻两个第一障碍物包围盒之间的欧氏距离小于本车宽度时，将相邻两个第一障碍物包围盒进行合并，生成第二障碍物包围盒；将第二障碍物包围盒作为第一障碍物包围盒，返回执行当相邻两个第一障碍物包围盒之间的欧氏距离小于本车宽度时，将相邻两个第一障碍物包围盒进行合并，生成第二障碍物包围盒，直至第二障 碍物包围盒与相邻的第一障碍物包围盒之间的欧氏距离大于或等于本车宽度，或者没有与第二障碍物包围盒相邻的第一障碍物包围盒。
示例性的，可创建一个CLOSED表，并将其初始化为空表，从第一障碍物包围盒的集合set contour中取出一个第一障碍物包围盒加入到CLOSED表中，并且在集合set contour中删除这个第一障碍物包围盒，然后遍历集合set contour，一旦集合set contour中的第一障碍物包围盒与CLOSED表中的第一障碍物包围盒的欧式距离小于本车宽度时，就将集合set contour的该第一障碍物包围盒加入到CLOSED表中，并与CLOSED表中进行欧式距离比较的第一障碍物包围进行聚合，聚合成新的第一障碍物包围，再将加入到CLOSED表中第一障碍物包围盒从集合set contour中删除。如此循环往复，直到集合set contour为空，便可完成对位于道路内的第一栅格障碍物的聚合处理。
S225、将聚合处理后的第一栅格障碍物作为第二栅格障碍物。
再者，本公开实施例也可既基于道路信息和第一栅格障碍物信息，过滤掉位于道路之外的第一栅格障碍物，又基于道路信息和第一栅格障碍物信息，确定位于道路内的第一栅格障碍物，并对位于道路内的第一栅格障碍物进行聚合处理。如此，可进一步减少第一栅格障碍物的数量。
基于上述实施例，在得到第二栅格障碍物之后，可采用快速凸包算法，将第二栅格障碍物转换成第二凸包障碍物。如此，能够实现对栅格障碍物和凸包障碍物的统一决策。
S240、基于目标凸包障碍物的目标凸包障碍物信息，对目标凸包障碍物做出避让决策。
本步骤可由图5中的通行方式决策器1132执行。在一些实施例中，、基于目标凸包障碍物的目标凸包障碍物信息，对目标凸包障碍物做出避让决策，包括：基于目标凸包障碍物信息，对满足预设过滤条件的目标凸包障碍物打上无需避让标签或无需横向避让标签。本公开实施例通过为满足预设过滤条件的目标凸包障碍物打上无需避让标签或无需横向避让标签，可使得图2中的轨迹生成单元12忽略该目标凸包障碍物，既可以减少轨迹生成单元12处理的障碍物的负担，提高轨迹生成速度，又可以提高轨迹生成的合理性。
在一些实施例中，预设过滤条件包括以下至少一种：目标凸包障碍物位于道路之外；目标凸包障碍物的运动状态满足无需横向避让条件；目标凸包障碍物位于本车引导线上。相应的，基于目标凸包障碍物信息，对满足预设过滤条件的目标凸包障碍物打上无需避让标签或无需横向避让标签，包括：当目标凸包障碍物位于道路之外时，为目标凸包障碍物打上无需避让标签；当目标凸包障碍物的运动状态满足无需横向避让条件或目标凸包障碍物位于本车引导线上时，为目标凸包障碍物打上无需横向避让标签。示例性的，参考图7，当目标凸包障碍物位于道路之外时，例如障碍物1，目标凸包障碍物对无人驾驶汽车100的正常行驶完全没有影响，此时可忽略该目标凸包障碍物，为该目标凸包障碍物打上无需避让标签。当目标凸包障碍物的运动状态满足无需横向避让条件时，例如目标凸包障碍物横穿道路时，如行人横穿马路，无人驾驶汽车100只需等待行人通过，无需生成一个在行人附近绕行的轨迹，可为该行人打上无需横向避让标签；又如目标凸包障碍物向本车车道变道，或者目标凸包障碍物的纵向速度大于本车速度(如相邻车道高速移动的障碍物)时，在不影响本车车道安全的情况下，本车无需横向避让，可为上述目标凸包障碍物打上无需横向避让标签；再如目标凸包障碍物位于本车引导线上时，如障碍物2位于本车引导线c上，无人驾驶汽车100也无需横向避让障碍物2，选择跟随障碍物2即可，可以理解的是，该障碍物2应为与无人驾驶汽车100同向移动的动态障碍物。
上述实施例中，可通过图5中的凸包障碍物过滤器1133过滤出满足预设过滤条件的目标凸包障碍物。在一些实施例中，凸包障碍物过滤器1133可包括基于障碍物frenet包围盒的路网过滤器、行为语义信息过滤器和引导线过滤器中的至少一种。其中，基于障碍物frenet包围盒的路网过滤器可快速过滤出位于道路之外的目标凸包障碍物，基于障碍物frenet包围盒的路网过滤器可采用上述实施例中通过两阶段的碰撞检测方法来过滤出位于道路之外的目标凸包障碍物；行为语义信息过滤器可根据目标凸包障碍物含有的语义信息，过滤出无需避让和无需横向避让的目标凸包障碍物；引导线过滤器可过滤出与引导线发生碰撞的目标凸包障碍物。
除了上述对目标凸包障碍物打上无需避让标签，以做出无需避让决策，以及对目标凸包障碍物打上无需横向避让标签，以做出无需横向避让决策，本公开实施例还可以对目标凸包障碍物做出跟随、左侧通行或右侧通行的避让决策。在一些实施例中，基于目标凸包障碍物信息和本车引导线，对目标凸包障碍物打上避让标签，也可以包括：如果目标凸包障碍物位于本车引导线上，则对目标凸包障碍物打上跟随标签；如果目标凸包障碍物没有位于本车引导线上，则当目标凸包障碍物的质心位于本车引导线的左侧时，对目标凸包障碍物打上右侧通行标签，当目标凸包障碍物的质心位于本车引导线的右侧时，对目标凸包障碍物打上左侧通行标签。
可继续参考图7，障碍物2位于本车引导线c上，此时无人驾驶汽车100只需跟随障碍物2即可，为障碍物2打上跟随标签。障碍物3位于道路内，没有位于本车引导线c上，且影响到无人驾驶汽车100的车道安全，此时需要从障碍物3左侧通行或右侧通行来避让障碍物3。基于本公开的技术方案，检测障碍物3的质心与本车引导线c的相对位置，如果障碍物3的质心位于本车引导线c的右侧(如图7所示)，则需要从障碍物3左侧通行，对障碍物3打上左侧通行标签；如果障碍物3的质心位于本车引导线c的左侧，则需要从障碍物3右侧通行，对障碍物3打上右侧通行标签。
本公开实施例提供的车辆决策规划方法在对第一栅格障碍物进行预处理，得到第二栅格障碍物之后，通过将第二栅格障碍物转换成第二凸包障碍物，即将栅格类型的障碍物转换成凸包类型的障碍物，实现对栅格类型和凸包类型两种类型障碍物(即混合类型障碍物)的统一决策，从而能够简化混合类型障碍物的障碍物决策流程，加速障碍物决策过程，使得决策规划模块能够方便、快速地进行障碍物决策。
随着车辆智能化技术的发展，无人车自动控制技术逐渐成为车辆研究领域的一个热点。自动驾驶***需要规划出平顺、安全以及车辆可通行的路径，保证车辆与障碍物不会发生碰撞。对于基于优化的规划算法中，Julius Ziegler提出的一种迪卡尔空间中的最优化方法，可以将一个规划问题转换成一个最优化问题。然而，该方法极大地增加了规划模块的计算负担，无法解决快速避障问题，降低了轨迹的生成速度，而且，该方法不适用动态环境中障碍物的处理。
针对上述技术问题，图8为本公开实施例提供的另一种车辆决策规划方法的部分流程示意图。该方法适用于无人驾驶汽车针对静态障碍物和/或动态障碍物生成可行驶区域的情况，该方法可由行驶空间生成器执行。如图8所示，根据障碍物决策生成可行驶区域(或者车辆可行驶区域生成方法)，包括如下步骤：
S310、获取环境感知信息。
其中，环境感知信息包括车道信息、障碍物信息和本车信息中的至少两个，障碍物信息包括静态障碍物信息和/或动态障碍物信息。在一些实施例中，车道信息可包括车道线信息和道路边界信息，可采用车载摄像头进行获取；障碍物信息可包括障碍物位置信息、障碍物尺寸信息和障碍物运动信息，其中，障碍物位置信息可采用高精地图和车载摄像头/激光雷达进行获取，障碍物尺寸信息可采用车载摄像头进行获取，障碍物运动信息可采用车载摄像头和/或激光雷达进行获取；本车信息可包括本车位置信息和本车运动信息，其中，本车位置信息可采用高精地图和本车定位模块(如GPS)进行获取，本车运动信息可采用本车运动传感器(如速度传感器和加速度传感器等)进行获取。
S320、基于环境感知信息，确定每个车道的车道决策语义信息。
其中，车道决策语义信息包括通过时间代价和安全性代价。通过时间代价用于表征车道的通行情况，例如，如果车辆能够快速通过一条车道，则该条车道的通行时间快；安全性代价用于表征车道的安全性。
在一些实施例中，可根据本车纵向速度和障碍物纵向速度的大小关系确定每个车道的通过时间代价。相应的，当车道决策语义信息包括通过时间代价时，基于环境感知信息，确定每个车道的车道决策语义信息，包括：对于每个车道，基于环境感知信息，确定本车与本车前方第一个障碍物的碰撞时间；将碰撞时间确定为通过时间代价。
具体的，环境感知信息包括本车位置信息和本车纵向速度信息，以及每个车道上距离本车最近的前方障碍物的障碍物位置信息和障碍物纵向速度信息，根据本车位置信息和障碍物位置信息，分别计算本车到各车道距离最近的前方障碍物的纵向距离；根据本车纵向速度信息和障碍物纵向速度信息，判断障碍物纵向速度是否小于本车纵向速度。在障碍物纵向速度小于本车纵向速度时，根据纵向距离、本车纵向速度信息和障碍物纵向速度信息，预测本车与前方障碍物发生碰撞时的碰撞时间，将该碰撞时间确定为通过时间代价。另外，如果本车前方无障碍物或者本车前方第一个障碍物的纵向速度大于或等于本车纵向速度，则将预设时长确定为通过时间代价。基于上述技术方案，可采用如下公式计算通过时间代价：
Figure PCTCN2022088532-appb-000002
其中，TCC为通过时间代价，v adv为本车纵向速度，v obs为障碍物纵向速度，tcc max为预设时长，其为一固定值，且大于碰撞时间，例如1000(此处仅为数值，单位与碰撞时间的单位相同，例如秒或毫秒等)。由该公式可知，本车前方第一个障碍物的纵向速度越小，通过时间代价越小，对应车道的通过性越差；当本车前方无障碍物或者本车前方第一个障碍物的纵向速度大于或等于本车纵向速度时，本车在对应车道不会与障碍物发生碰撞，对应车道的通过性最优。
示例性的，如图9所示，在同向车道中，本车车道上，本车前方第一个障碍物为障碍物1，相邻车道上，本车前方第一个障碍物为障碍物2。本车纵向速度为5m/s，障碍物1的纵向速度为1m/s，障碍物2的纵向速度为10m/s。对于本车车道，障碍物1的纵向速度小于本车纵向速度，本车与障碍物1将发生碰撞，此时，确定本车与障碍物1之间的距离D为16m，根据上述公式可确定本车与障碍物1的碰撞时间为4s，因此，本车车道的通过时间代价为4。而对于相邻车道，障碍物2的纵向速度大于本车纵向速度，本车与障碍物2不会发生碰撞，此时，相邻车道的通过时间代价为预设时长，如10000。由此，可确定相邻车道的通过时间代价大于本车车道的通过时间代价，即相邻车道的通过性较好。
在一些实施例中，为保证车辆的安全性，同时需确定每个车道的安全性代价。相应的，当车道决策语义信息包括安全性代价时，基于环境感知信息，确定每个车道的车道决策语义信息，包括：基于车道信息和本车信息，确定本车车道和其他车道；对于本车车道，将第一预设安全性代价确定为安全性代价；对于其他车道，如果基于环境感知信息，确定障碍物在预设时间内进入本车危险区，则将第二预设安全性代价确定为安全性代价，如果基于环境感知信息，确定障碍物在预设时间内未进入本车危险区，则将第一预设安全性代价确定为安全性代价，其中，第二预设安全性代价与第一预设安全性代价不同。
具体的，环境感知信息包括车道信息、本车信息和障碍物信息，基于车道信息和本车信息，确定本车车道和其他车道，对于本车车道，默认本车拥有绝对路权，即本车车道的安全性最高。对于其他车道，可对本车观察区内的障碍物进行安全性判断，当预测到本车观察区内的障碍物在未来一段时间(即预设时间)内会进入到本车危险区，则说明障碍物当前时刻所在车道的安全性较低；当预测到本车观察区内的障碍物在未来一段时间内不会进入到本车危险区，则说明障碍物当前时刻所在车道的安全性较高。
可以理解的是，第二预设安全性代价对应车道的安全性低于第一预设安全性代价对应车道的安全性。在一些实施例中，第二预设安全性代价小于第一预设安全性代价。在一些实施例中，可采用惩罚机制为第一预设安全性代价和第二预设安全性代价赋值，例如第一预设安全性代价为0，第二预设安全性代价为-100000。
基于上述技术方案，可采用ST图(纵向位移-时间图)来确定其他车道的障碍物在未来一段时间内是否会进入到本车危险区。在一些实施例中，基于环境感知信息，确定本车ST图曲线和障碍物ST图曲线；基于本车ST图曲线，确定本车危险区；判断预设时间内障碍物ST图曲线是否与本车危险区存在交叠；如果预设时间内障碍物ST图曲线与本车危险区存在交叠，则确定障碍物在预设时间内进入本车危险区；否则，确定障碍物在预设时间内未进入本车危险区。示例性的，如图10所示，本车ST图曲线为图中本车所指示的曲线，障碍物ST图曲线包括图中障碍物1、障碍物2、障碍物3和障碍物4分别指示的曲线。本车危险区包括本车后方危险区(区间L2对应的区域)和本车前方危险区(区间L3对应的区域)，本车观察区包括本车后方观察区(区间L1对应的区域)本车前方观察区(区间L4对应的区域)，预设时间为T_e。可选的，L1为100米，L2为20米，L3为10米，L4为100米，T_e为6秒。参见图10，根据本车ST图曲线和各障碍物ST图曲线可知，本车后方观察区的障碍物2对应的障碍物ST图曲线在预设时间T_e内与本车后方危险区存在交叠，本车后方观察区的障碍物1对应的障碍物ST图曲线在预设时间T_e内与本车后方危险区不存在交叠，本车前方观察区的障碍物3对应的障碍物ST图曲线在预设时间T_e内与本车前方危险区存在交叠，本车前方观察区的障碍物4对应的障碍物ST图曲线在预设时间T_e内与本车前方危险区不存在交叠。由此可知，障碍物2和障碍物3在预设时间内进入到了本车危险区，障碍物2和障碍物3在当前时刻所在车道的安全性较低，即对应车道的安全性代价为第二预设安全性代价；而障碍物1和障碍物4在预设时间内未进入本车危险区，障碍物1和障碍物4在当前时刻所在车道的安全性较高，即对应车道的安全性代价为第一预设安全性代价。需要说明的是，障碍物ST图曲线与本车危险区存在交叠包括障碍物ST图曲线完全位于本车危险区内，或者障碍物ST图曲线的一部分位于本车危险区内。上述实施例中，为了简化计算，设定障碍物以恒定的速度运动，障碍物ST图曲线是一条直线，本车危险区是一个平行四边形，均为凸包类型的图形，如此，可利用基于Gilbert–Johnson–Keerthi算法的碰撞检测算法，来快速计算障碍物在预设时间T_e内是否会进入本车危险区。
S330、基于车道决策语义信息，生成可行驶区域。
针对每个车道，本公开实施例可同时基于通过时间代价和安全性代价，生成可行驶区域，由此可选出兼顾通过性和安全性的车道。
在一些实施例中，可对车道决策语义信息中的各代价进行加权求和；基于加权求和结果，生成可行驶区域。本实施例中，对通过时间代价和安全性代价进行加权求和，得到加权求和结果。例如：
f＝w 1f pass+w 2f safe；
其中，f为加权求和结果(或加权求和值)，f pass为通过时间代价，f safe为安全性代价，w 1为通过时间代价的权重，w 2为安全性代价的权重。w 1和w 2可根据仿真或实际车辆测试实验得到。基于该技术方案，本公开实施例可将加权求和值最大的车道确定为可行驶区域。
在一些实施例中，为了便于规划器接收这个可行驶区域，将可行驶区域的边界离散化，形成可行驶区域边界点，包括左边界点和右边界点。示例性的，可基于弗雷纳坐标系，以固定的分辨率来离散化可行驶区域。如图11所示，根据车道决策信息，以及车道中心构建的曲线弗雷纳坐标系，以固定分辨率，来生成可行驶区域的左边界和右边界，左边界表示弗雷纳坐标系中L值的上届，右边界表示弗雷纳坐标系中L值的下界。其中，相邻两个左边界点或相邻两个右边界点的纵向距离为上述固定分辨率。图11表示的是车辆在右车道，并且车道决策的结果也是右车道，所以可行驶区域的左边界和右边界如图11所示。如果车道决策的结果是换道，那么此时的可行驶区域就包含两个车道。
本公开实施例提供的车辆可行驶区域生成方法，根据环境感知信息确定每个车道的车道决策语义信息，将车道决策语义信息转换成可行驶区域的约束边界，兼顾通过性和安全性，能够快速生成通过性和安全性高的可行驶区域，加速行驶轨迹的生成，实现对障碍物的快速避让；同时，通过时间代价和安全性代价均能够表征对动态障碍物的通行代价，且通过时间代价还能表征对静态障碍物的通行代价，因此，本公开技术方案基于通过时间代价和安全性代价生成可行驶区域，可以同时实现对动态障碍物和静态障碍物的通行规划，可适用动态环境中障碍物的处理。
基于上述技术方案，当基于车道决策语义信息确定的可行驶区域包括至少两个车道，且至少两个车道均存在静态障碍物时，可进一步根据通行宽度代价选出一条最优车道。在一些实施例中，车道决策语义信息还包括通行宽度代价，基于环境感知信息，确定每个车道的车道决策语义信息，包括：基于车道信息和静态障碍物信息，确定车道的最小通行宽度；将最小通行宽度确定为通行宽度代价。其中，通行宽度代价用于表征本车前方的静态障碍物对车道的堵塞情况。在一些实施例中，基于车道信息和静态障碍物信息，确定车道的最小通行宽度，包括：基于车道 信息和静态障碍物信息，确定车道上各静态障碍物的最大通行宽度；将各静态障碍物的最大通行宽度中的最小值确定为车道的最小通行宽度。
具体的，建立弗雷纳坐标系，将各静态障碍物投影到弗雷纳坐标系中，生成各障碍物的SL包围盒。对于每个车道，计算出每个静态障碍物的左侧通行宽度和右侧通行宽度，从左侧通行宽度和右侧通行宽度中确定每个静态障碍物的最大通行宽度，从所有静态障碍物的最大通行宽度选出一个最小的最大通行宽度作为车道的最小通行宽度，将该最小通行宽度确定为通行宽度代价。该通行宽度代价越大，静态障碍物对车道的堵塞程度越小。示例性的，如图12所示，本车车道上包括障碍物1和障碍物2，相邻车道上包括障碍物3(障碍物1、障碍物2和障碍物3均为静态障碍物)，障碍物1的最大通行宽度为d1，障碍物2的最大通行宽度为d2，障碍物3的最大通行宽度为d3，其中，d1小于d2且d2小于d3，因此，本车车道的最小通行宽度为d1，即本车车道的通行宽度代价为d1，相邻车道的最小通行宽度为d3，即相邻车道的通行宽度代价为d3。此时，相邻车道的障碍物堵塞程度小于本车车道的障碍物堵塞程度。因此，可进一步选取相邻车道生成可行驶区域。
基于上述技术方案，当基于车道决策语义信息中各代价仍无法确定出最优的可行驶区域时，为了保证车辆行驶轨迹的稳定性，通过在车道决策语义信息中增加稳定性代价来优选选择本车车道生成可行驶区域。相应的，在一些实施例中，车道决策语义信息还包括稳定性代价，基于环境感知信息，确定每个车道的车道决策语义信息，包括：基于车道信息和本车信息，确定本车车道和其他车道；对于本车车道，将第一预设稳定性代价确定为稳定性代价；对于其他车道，将第二预设稳定性代价确定为稳定性代价，其中，第二预设稳定性代价与第一预设稳定性代价不同。该技术方案中，第一预设稳定性代价可大于第二预设稳定性代价，其中第一预设稳定性代价可以为100，第二预设稳定性代价可以为0。
基于上述各实施例，对车道决策语义信息中的各代价进行加权求和，得到加权求和结果，可采用如下公式计算：
f＝w 1f pass+w 2f safe+w 3f narrow+w 4f stable；
其中，f narrow为通行宽度代价，f stable为稳定性代价，w 3为通行宽度代价的权重，w 4为稳定性代价的权重。
基于上述技术方案，在一些实施例中，在基于车道决策语义信息，生成可行驶区域之后，方法还包括如下至少一项：
基于预设交通规则，更新可行驶区域；
基于车辆的运动学和动力学约束，更新可行驶区域；
基于障碍物语义信息和预设安全区，更新可行驶区域，预设安全区与可行驶区域相连；
基于静态障碍物信息、障碍物决策语义信息和光线追踪算法，更新可行驶区域，障碍物决策语义信息包括障碍物左侧通过或障碍物右侧通过。
具体的，在一些实施例中，可基于预设交通规则判断可行驶区域是否违反了交通规则，进而对违反交通规则的可行驶区域进行修剪，以更新可行驶区域。本公开实施例中，预设交通规则可以包括虚实黄线、虚实白线以及车道指示线等常见交通规则。示例性的，如图13所示，在车道决策后，车辆会选择换道，所以首先使用换道过程的双车道作为可行驶区域，即原始可行驶区域。但是又由于车道的末端是实线，所以基于预设交通规则会修剪可行驶区域，得到图13中黑圆点为边界点限定的可行驶区域，即更新后的可行驶区域。
在一些实施例中，本公开技术方案也可基于车辆的运动学和动力学约束，更新可行驶区域。示例性的，基于车辆的运动学和动力学约束，在车辆临时借道时，通过增加额外可行驶区域来更新可行驶区域。如图14所示，本车的航向角与路网的航向角之间的夹角为Δθ，本车在本车车道位置点的曲率为k r，本车在弗雷纳坐标系中的L坐标值为d，所以根据弗雷纳运动学方程，本车相对于弗雷纳坐标系的横向速度为d′＝(1-k rd)tan(Δθ)，横向加速度为
Figure PCTCN2022088532-appb-000003
其中，k ADV为使用无人车自行车模型的车轮转角δ而计算出的本车轨迹曲率，本车的轴距为B，那么
Figure PCTCN2022088532-appb-000004
k′ r为路网的曲率变化率，为了近似计算，令k′ r＝0，为了计算额外可行驶区域d extra，假设本车最后的弗雷纳横向速度为0,所以基于弗雷纳坐标系运动学有
Figure PCTCN2022088532-appb-000005
如此，通过计算得到的额外可行驶区域向外扩展可行驶区域，以对可行驶区域进行更新。
在一些实施例中，考虑到相邻车道上可能存在动态障碍物对可行驶区域的影响，导致并不能保证可行驶区域绝对的安全，因此，可将可行驶区域可能会受到动态障碍物影响的区域修剪掉，来保证剩余可行驶区域的安全性。
具体的，基于障碍物语义信息，确定需横向避让的障碍物；如果需横向避让的障碍物的移动轨迹占用了预设安全区，则修剪占用预设安全区对应位置处的部分可行驶区域。本实施例中，障碍物语义信息可以包括障碍物并线、障碍物横穿、障碍物平行行驶和逆向行驶等用于表征障碍物运动状态的信息。本车基于障碍物语义信息，会自动判断是否需横向避让障碍物。例如，如果基于障碍物语义信息确定障碍物并线，则本车不需要横向避让该障碍物，如果基于障碍物语义信息确定障碍物太靠近本车车道，则本车需要横向避让该障碍物。示例性的，如图15所示，在可行驶区域两侧增加预设安全区(如果可行驶区域邻接道路边界，可仅在可行驶区域内侧增肌预设安全区)，判断障碍物预测模块(在前模块，本公开未涉及)输出的障碍物的移动轨迹是否占用了预设安全区，如果移动轨迹占用了预设安全区，则修剪占用预设安全区对应位置处的部分可行驶区域，如图15中的修剪区域，以此更新可行驶区域。
在一些实施例中，由于上述各实施例得到的可行驶区域仍包含静态障碍物所在的区域，并不满足避障约束，因此需要进一步将静态障碍物所在的区域从可行驶区域中修剪掉，以更新可行驶区域。本公开技术方案为了避免已有方案利用弗雷纳包围盒来生成近似的可行驶区域而导致车辆通过性降低的问题，结合障碍物决策语义信息和光线追踪算法来精确地确定静态障碍物所在的区域。
具体的，基于静态障碍物信息、障碍物决策语义信息和光线追踪算法，更新可行驶区域，包括：基于静态障碍物信息、障碍物决策语义信息和光线追踪算法，确定光线与障碍物的碰撞点，碰撞点位于可行驶区域内；基于碰撞点，更新可行驶区域。在一些实施例中，基于静态障碍物信息、障碍物决策语义信息和光线追踪算法，确定光线与障碍物的碰撞点，包括：基于障碍物决策语义信息，确定光源点和光线投射方向；基于静态障碍物信息，确定光线投射范围；基于光源点、光线投射方向和光线投射范围，对障碍物进行光线扫描；确定光线与障碍物的碰撞点。在一些实施例中，光线追踪算法可采用基于Gilbert–Johnson–Keerthi算法的光线投射算法，以提高求解精度。
示例性的，障碍物决策语义信息包括从障碍物左侧通过或从障碍物右侧通过。当障碍物决策语义信息为从障碍物左侧通过时，确定光源点位于静态障碍物的左侧，且光线投射方向垂直于车道通行方向且朝向静态障碍物；当障碍物决策语义信息为从障碍物右侧通过时，确定光源点位于静态障碍物的右侧，且光线投射方向垂直于车道通行方向且朝向静态障碍物。基于静态障碍物信息，如静态障碍物的位置信息和尺寸信息，可确定静态障碍物所在区域，从而确定光线投射范围。在确定光线与障碍物的碰撞点之后，将各碰撞点限定的可行驶区域修剪掉。本公开实施例对光源点的具***置不作限定，在一些实施例中，光源点可位于可行驶区域边界点上。
在一具体实施例中，如图16所示，可先确定静态障碍物的frenet包围盒box_sl＝{S_min,S_max,L_min,L_max},基于frenet包围盒，确定静态障碍物在可行驶区域纵向上的ID范围。在可行驶区域边界点的分辨率为Δs时，上述ID范围为(id_start，id_end)，即光线投射范围，其中，id_start＝floor(s_min/Δs),id_end＝ceil(s_max/Δs)，floor表示对浮点数向下取整数操作，ceil表示对浮点数向上取整数操作。需要说明的是，本公开实施例仅以frenet包围盒来确定可以包含整个静态障碍物的光线投射范围，以保证光线对障碍物的完整扫描，后续确定的碰撞点位于静态障碍物上，而不是位于frenet包围盒边界上。如图17所示，可行驶区域中包含两个静态障碍物，即障碍物1和障碍物2。根据障碍物1的障碍物决策语义信息可确定本车从障碍物1右侧通过，根据障碍物2的障碍物决策语义信息可确定本车从障碍物2左侧通过。以障碍物1为例，基于障碍物1的障碍物决策语义信息，确定点光源位于可行驶区域右侧边界点，基于障碍物1的静态障碍物信息，确定光源对障碍物1的光线投射范围，即上述ID范围，从而点光源可根据ID范围按顺序对障碍物1进行扫描。具体的，当光线与障碍物1相撞时，如果碰撞点位于可行驶区域内，则修剪碰撞点远离点光源一侧的可行驶区域，如此，直至完成对光线投射范围的扫描。采用上述技术方案，在使可行驶区域满足避障约束的情况下，可以提高静态障碍物在可行驶区域中所占区域的求解精度，提高车辆的通过性。
本公开实施例还提供了一种车辆决策规划装置。参考图2，该车辆决策规划装置包括基坐标系生成器111、引导线生成器112、障碍物决策器113和行驶空间生成器114。
其中，基坐标系生成器111，用于生成基坐标系；引导线生成器112，用于在所述基坐标系下生成引导线，以决策车辆未来的一个大致行驶轨迹；障碍物决策器113，用于在所述引导线的约束下，进行障碍物决策；行驶空间生成器114，用于根据障碍物决策生成可行驶区域。
图18为本公开实施例提供的车辆决策规划装置中的障碍物决策器的功能模块框图。如图18所示，障碍物决策器(或者避让障碍物的决策装置)包括信息获取模块401、预处理模块402、类型转换模块403和避让决策模块404。
其中，信息获取模块401，用于获取道路信息、第一栅格障碍物的第一栅格障碍物信息和第一凸包障碍物的第一凸包障碍物信息；
预处理模块402，用于基于道路信息和第一栅格障碍物信息，对第一栅格障碍物进行预处理，得到第二栅格障 碍物，其中，第二栅格障碍物的数量小于第一栅格障碍物的数量；
类型转换模块403，用于将第二栅格障碍物转换成第二凸包障碍物；
避让决策模块404，用于基于目标凸包障碍物的目标凸包障碍物信息，对目标凸包障碍物做出避让决策，其中，目标凸包障碍物包括第一凸包障碍物和/或第二凸包障碍物。
在一些实施例中，预处理模块402包括：
障碍物过滤单元，用于基于道路信息和第一栅格障碍物信息，过滤掉位于道路之外的第一栅格障碍物；将剩余第一栅格障碍物作为第二栅格障碍物；和/或，
障碍物聚合单元，用于基于道路信息和第一栅格障碍物信息，确定位于道路内的第一栅格障碍物；对位于道路内的第一栅格障碍物进行聚合处理；将聚合处理后的第一栅格障碍物作为第二栅格障碍物。
在一些实施例中，障碍物过滤单元包括：
道路包围盒生成子单元，用于基于道路信息，生成沿道路通行方向的道路包围盒；
栅格障碍物包围盒生成子单元，用于基于第一栅格障碍物信息，生成第一栅格障碍物的栅格障碍物包围盒；
第一栅格障碍物子单元，用于基于栅格障碍物包围盒和道路包围盒，确定位于道路之外的第一栅格障碍物；
第一栅格障碍物过滤子单元，用于过滤掉位于道路之外的第一栅格障碍物。
在一些实施例中，道路包围盒生成子单元具体用于：
基于道路信息，将道路边界离散成边界点；
基于边界点，生成道路包围盒。
在一些实施例中，道路包围盒生成子单元具体用于：
基于道路信息，确定道路边界和道路边界曲率；
基于道路边界曲率，对道路边界进行离散化，得到沿道路通行方向间隔排布的边界点组，其中，每组边界点组包括在横向上对应的左边界点和右边界点，横向垂直于道路通行方向。
在一些实施例中，道路包围盒生成子单元具体用于：
基于任意相邻两组边界点组，生成经过任意相邻两组边界点组中各边界点的矩形框，并将矩形框作为道路包围盒。
在一些实施例中，道路包围盒生成子单元具体用于：
以本车当前位置作为起始路点；
获取起始路点在横向上对应的一组边界点组；
基于道路边界曲率，沿道路通行方向选择下一路点，其中，相邻两个路点之间的距离与道路边界曲率负相关；
将下一路点作为起始路点，返回执行获取起始路点在横向上对应的一组边界点组，直至在道路通行方向上下一路点到本车当前位置的距离大于预设距离阈值，将当前获得的所有边界点组确定为边界点组。
在一些实施例中，栅格障碍物包围盒生成子单元具体用于：
基于第一栅格障碍物信息，生成第一栅格障碍物的栅格障碍物轮廓；
基于栅格障碍物轮廓生成栅格障碍物包围盒。
在一些实施例中，第一栅格障碍物子单元具体用于：
对于每个栅格障碍物包围盒，基于栅格障碍物包围盒和道路包围盒，从道路包围盒中确定到栅格障碍物包围盒欧氏距离最小的目标道路包围盒；
将栅格障碍物包围盒与对应的目标道路包围盒进行碰撞检测；
如果栅格障碍物包围盒与对应的目标道路包围盒没有碰撞，则确定栅格障碍物包围盒对应的第一栅格障碍物位于道路之外。
在一些实施例中，装置还包括：
第一栅格障碍物位置判断模块，用于如果栅格障碍物包围盒与对应的目标道路包围盒发生碰撞，判断栅格障碍物包围盒对应的第一栅格障碍物是否位于道路之外。
在一些实施例中，第一栅格障碍物位置判断模块具体用于：
基于目标道路包围盒的边界点和栅格障碍物包围盒通过向量叉积进行碰撞检测，判断栅格障碍物包围盒对应的第一栅格障碍物是否位于道路之外。
在一些实施例中，第一栅格障碍物位置判断模块具体用于：
确定边界点向量；
确定栅格障碍物包围盒的顶点向量；
当栅格障碍物包围盒的顶点向量与边界点向量的叉积均大于0时，确定栅格障碍物包围盒对应的第一栅格障碍物位于道路之外。
在一些实施例中，第一栅格障碍物位置判断模块具体用于：
确定边界点向量；
确定栅格障碍物包围盒的顶点向量；
当栅格障碍物包围盒的顶点向量与边界点向量的叉积小于或等于0时，确定栅格障碍物包围盒对应的第一栅格障碍物位于道路内。
在一些实施例中，障碍物聚合单元包括：
第一障碍物包围盒生成子单元，用于基于位于道路内的第一栅格障碍物的第一栅格障碍物信息，生成其中第一栅格障碍物的第一障碍物包围盒；
第二障碍物包围盒生成子单元，用于当相邻两个第一障碍物包围盒之间的欧氏距离小于本车宽度时，将相邻两个第一障碍物包围盒进行合并，生成第二障碍物包围盒；
返回执行单元，用于将第二障碍物包围盒作为第一障碍物包围盒，返回执行当相邻两个第一障碍物包围盒之间的欧氏距离小于本车宽度时，将相邻两个第一障碍物包围盒进行合并，生成第二障碍物包围盒，直至第二障碍物包围盒与相邻的第一障碍物包围盒之间的欧氏距离大于或等于本车宽度，或者没有与第二障碍物包围盒相邻的第一障碍物包围盒。
在一些实施例中，避让决策模块404包括：
第一决策单元，用于基于目标凸包障碍物信息，对满足预设过滤条件的目标凸包障碍物打上无需避让标签或无需横向避让标签；和/或，
第二决策单元，用于基于目标凸包障碍物信息和本车引导线，对目标凸包障碍物打上避让标签，其中，避让标签包括左侧通行标签、右侧通行标签或跟随标签。
在一些实施例中，预设过滤条件包括以下至少一种：
目标凸包障碍物位于道路之外；
目标凸包障碍物的运动状态满足无需横向避让条件；
目标凸包障碍物位于本车引导线上。
在一些实施例中，第一决策单元具体用于：
当目标凸包障碍物位于道路之外时，为目标凸包障碍物打上无需避让标签；
当目标凸包障碍物的运动状态满足无需横向避让条件或目标凸包障碍物位于本车引导线上时，为目标凸包障碍物打上无需横向避让标签。
在一些实施例中，无需横向避让条件包括以下任一种：
目标凸包障碍物横穿道路；
目标凸包障碍物向本车车道变道；
目标凸包障碍物的纵向速度大于本车速度。
在一些实施例中，第二决策单元具体用于：
如果目标凸包障碍物位于本车引导线上，则对目标凸包障碍物打上跟随标签；
如果目标凸包障碍物没有位于本车引导线上，则当目标凸包障碍物的质心位于本车引导线的左侧时，对目标凸包障碍物打上右侧通行标签，当目标凸包障碍物的质心位于本车引导线的右侧时，对目标凸包障碍物打上左侧通行标签。
图19为本公开实施例提供的车辆决策规划装置中的行驶空间生成器的功能模块框图。如图19所示，行驶空间生成器(或者车辆可行驶区域生成装置)包括感知信息获取模块501、车道决策语义信息确定模块502和可行驶区域生成模块503。
其中，感知信息获取模块501，用于获取环境感知信息，其中，环境感知信息包括车道信息、障碍物信息和本车信息中的至少两个，障碍物信息包括静态障碍物信息和/或动态障碍物信息；
车道决策语义信息确定模块502，用于基于环境感知信息，确定每个车道的车道决策语义信息，其中，车道决策语义信息包括通过时间代价和安全性代价；
可行驶区域生成模块503，用于基于车道决策语义信息，生成可行驶区域。
在一些实施例中，当车道决策语义信息包括通过时间代价时，车道决策语义信息确定模块502具体用于：
对于每个车道，基于环境感知信息，确定本车与本车前方第一个障碍物的碰撞时间；
将碰撞时间确定为通过时间代价。
在一些实施例中，车道决策语义信息确定模块502还用于：
如果基于环境感知信息，确定本车前方无障碍物或者本车前方第一个障碍物的纵向速度大于或等于本车纵向速度，则将预设时长确定为通过时间代价。
在一些实施例中，当车道决策语义信息包括安全性代价时，车道决策语义信息确定模块502具体用于：
基于车道信息和本车信息，确定本车车道和其他车道；
对于本车车道，将第一预设安全性代价确定为安全性代价；
对于其他车道，如果基于环境感知信息，确定障碍物在预设时间内进入本车危险区，则将第二预设安全性代价确定为安全性代价，如果基于环境感知信息，确定障碍物在预设时间内未进入本车危险区，则将第一预设安全性代价确定为安全性代价，其中，第二预设安全性代价与第一预设安全性代价不同。
在一些实施例中，车道决策语义信息确定模块502具体用于：
基于环境感知信息，确定本车ST图曲线和障碍物ST图曲线；
基于本车ST图曲线，确定本车危险区；
判断预设时间内障碍物ST图曲线是否与本车危险区存在交叠；
如果预设时间内障碍物ST图曲线与本车危险区存在交叠，则确定障碍物在预设时间内进入本车危险区；否则，确定障碍物在预设时间内未进入本车危险区。
在一些实施例中，车道决策语义信息还包括通行宽度代价，车道决策语义信息确定模块502具体用于：
基于车道信息和静态障碍物信息，确定车道的最小通行宽度；
将最小通行宽度确定为通行宽度代价。
在一些实施例中，车道决策语义信息确定模块502具体用于：
基于车道信息和静态障碍物信息，确定车道上各静态障碍物的最大通行宽度；
将各静态障碍物的最大通行宽度中的最小值确定为车道的最小通行宽度。
在一些实施例中，车道决策语义信息还包括稳定性代价，车道决策语义信息确定模块502具体用于：
基于车道信息和本车信息，确定本车车道和其他车道；
对于本车车道，将第一预设稳定性代价确定为稳定性代价；
对于其他车道，将第二预设稳定性代价确定为稳定性代价，其中，第二预设稳定性代价与第一预设稳定性代价不同。
在一些实施例中，可行驶区域生成模块503具体用于：
对车道决策语义信息中的各代价进行加权求和；
基于加权求和结果，生成可行驶区域。
在一些实施例中，上述装置还包括：
离散模块，用于在基于车道决策语义信息，生成可行驶区域之后，将可行驶区域的边界离散化。
在一些实施例中，上述装置还包括可行驶区域更新模块，该可行驶区域更新模块在基于车道决策语义信息，生成可行驶区域之后，具体用于以下至少一项更新操作：
基于预设交通规则，更新可行驶区域；
基于车辆的运动学和动力学约束，更新可行驶区域；
基于障碍物语义信息和预设安全区，更新可行驶区域，预设安全区与可行驶区域相连；
基于静态障碍物信息、障碍物决策语义信息和光线追踪算法，更新可行驶区域，障碍物决策语义信息包括从障碍物左侧通过或从障碍物右侧通过。
在一些实施例中，可行驶区域更新模块具体用于：
基于障碍物语义信息，确定需横向避让的障碍物；
如果需横向避让的障碍物的移动轨迹占用了预设安全区，则修剪占用预设安全区对应位置处的部分可行驶区域。
在一些实施例中，可行驶区域更新模块具体用于：
基于静态障碍物信息、障碍物决策语义信息和光线追踪算法，确定光线与障碍物的碰撞点，碰撞点位于可行驶区域内；
基于碰撞点，更新可行驶区域。
在一些实施例中，可行驶区域更新模块具体用于：
基于障碍物决策语义信息，确定光源点和光线投射方向；
基于静态障碍物信息，确定光线投射范围；
基于光源点、光线投射方向和光线投射范围，对障碍物进行光线扫描；
确定光线与障碍物的碰撞点。
以上实施例公开的车辆决策规划装置能够执行以上各实施例公开的车辆决策规划方法，具有相同或相应的有益效果，为避免重复，在此不再赘述。
本公开实施例还提供了一种电子设备，包括：存储器以及一个或多个处理器；其中，存储器与一个或多个处理器通信连接，存储器中存储有可被一个或多个处理器执行的指令，指令被一个或多个处理器执行时，电子设备用于实现本公开任一实施例描述的方法。
图20是适于用来实现本公开实施方式的电子设备的结构示意图。如图20所示，电子设备600包括中央处理单元(CPU)601，其可以根据存储在只读存储器(ROM)602中的程序或者从存储部分608加载到随机访问存储器(RAM)603中的程序而执行前述的实施方式中的各种处理。在RAM603中，还存储有电子设备600操作所需的各种程序和数据。CPU601、ROM602以及RAM603通过总线604彼此相连。输入/输出(I/O)接口605也连接至总线604。
以下部件连接至I/O接口605：包括键盘、鼠标等的输入部分606；包括诸如阴极射线管(CRT)、液晶显示器(LCD)等以及扬声器等的输出部分607；包括硬盘等的存储部分608；以及包括诸如LAN卡、调制解调器等的网络接口卡的通信部分609。通信部分609经由诸如因特网的网络执行通信处理。驱动器610也根据需要连接至I/O接口605。可拆卸介质611，诸如磁盘、光盘、磁光盘、半导体存储器等等，根据需要安装在驱动器610上，以便于从其上读出的计算机程序根据需要被安装入存储部分608。
特别地，根据本公开的实施方式，上文描述的方法可以被实现为计算机软件程序。例如，本公开的实施方式包括一种计算机程序产品，其包括有形地包含在及其可读介质上的计算机程序，计算机程序包含用于执行前述障碍物避让方法的程序代码。在这样的实施方式中，该计算机程序可以通过通信部分609从网络上被下载和安装，和/或从可拆卸介质611被安装。
附图中的流程图和框图，图示了按照本公开各种实施方式的装置、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，路程图或框图中的每个方框可以代表一个模块、程序段或代码的一部分，模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的***来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开实施方式中所涉及到的单元或模块可以通过软件的方式实现，也可以通过硬件的方式来实现。所描述的单元或模块也可以设置在处理器中，这些单元或模块的名称在某种情况下并不构成对该单元或模块本身的限定。
另外，本公开实施例还提供了一种计算机可读存储介质，该计算机可读存储介质可以是上述实施方式中所述装置中所包含的计算机可读存储介质；也可以是单独存在，未装配入设备中的计算机可读存储介质。计算机可读存储介质存储有计算机可执行指令，当计算机可执行指令被计算装置执行时，可用来实现本公开任一实施例描述的方法。
基于所述道路信息，生成沿道路通行方向的道路包围盒，包括：
基于所述道路信息，将道路边界离散成边界点；
基于所述边界点，生成所述道路包围盒。
基于所述道路信息，将道路边界离散成边界点，包括：
基于所述道路信息，确定道路边界和道路边界曲率；
基于道路边界曲率，对所述道路边界进行离散化，得到沿道路通行方向间隔排布的边界点组，其中，每组边界点组包括在横向上对应的左边界点和右边界点，所述横向垂直于道路通行方向。
基于所述边界点，生成所述道路包围盒，包括：
基于任意相邻两组边界点组，生成经过所述任意相邻两组边界点组中各边界点的矩形框，并将所述矩形框作为所述道路包围盒。
基于道路边界曲率，对所述道路边界进行离散化，得到沿道路通行方向间隔排布的边界点组，包括：
以本车当前位置作为起始路点；
获取所述起始路点在横向上对应的一组边界点组；
基于道路边界曲率，沿道路通行方向选择下一路点，其中，相邻两个路点之间的距离与道路边界曲率负相关；
将所述下一路点作为所述起始路点，返回执行获取所述起始路点在横向上对应的一组边界点组，直至在所述道路通行方向上所述下一路点到所述本车当前位置的距离大于预设距离阈值，将当前获得的所有边界点组确定为所述边界点组。
基于所述第一栅格障碍物信息，生成所述第一栅格障碍物的栅格障碍物包围盒，包括：
基于所述第一栅格障碍物信息，生成所述第一栅格障碍物的栅格障碍物轮廓；
基于所述栅格障碍物轮廓生成所述栅格障碍物包围盒。
基于所述栅格障碍物包围盒和所述道路包围盒，确定位于道路之外的所述第一栅格障碍物，包括：
对于每个所述栅格障碍物包围盒，基于所述栅格障碍物包围盒和所述道路包围盒，从所述道路包围盒中确定到所述栅格障碍物包围盒欧氏距离最小的目标道路包围盒；
将所述栅格障碍物包围盒与对应的所述目标道路包围盒进行碰撞检测；
如果所述栅格障碍物包围盒与对应的所述目标道路包围盒没有碰撞，则确定所述栅格障碍物包围盒对应的第一栅格障碍物位于道路之外。
所述方法还包括：
如果所述栅格障碍物包围盒与对应的所述目标道路包围盒发生碰撞，判断所述栅格障碍物包围盒对应的第一栅格障碍物是否位于道路之外。
判断所述栅格障碍物包围盒对应的第一栅格障碍物是否位于道路之外，包括：
基于目标道路包围盒的边界点和所述栅格障碍物包围盒通过向量叉积进行碰撞检测，判断所述栅格障碍物包围盒对应的第一栅格障碍物是否位于道路之外。
所述基于目标道路包围盒的边界点和所述栅格障碍物包围盒通过向量叉积进行碰撞检测，判断所述栅格障碍物包围盒对应的第一栅格障碍物是否位于道路之外，包括：
确定边界点向量；
确定栅格障碍物包围盒的顶点向量；
当栅格障碍物包围盒的顶点向量与所述边界点向量的叉积均大于0时，确定所述栅格障碍物包围盒对应的第一栅格障碍物位于道路之外。
所述方法还包括：
确定边界点向量；
确定栅格障碍物包围盒的顶点向量；
当栅格障碍物包围盒的顶点向量与所述边界点向量的叉积小于或等于0时，确定所述栅格障碍物包围盒对应的第一栅格障碍物位于道路内。
对位于所述道路内的所述第一栅格障碍物进行聚合处理，包括：
基于位于所述道路内的所述第一栅格障碍物的所述第一栅格障碍物信息，生成其中所述第一栅格障碍物的第一障碍物包围盒；
当相邻两个第一障碍物包围盒之间的欧氏距离小于本车宽度时，将所述相邻两个第一障碍物包围盒进行合并，生成第二障碍物包围盒；
将所述第二障碍物包围盒作为所述第一障碍物包围盒，返回执行当相邻两个第一障碍物包围盒之间的欧氏距离小于本车宽度时，将所述相邻两个第一障碍物包围盒进行合并，生成第二障碍物包围盒，直至第二障碍物包围盒与相邻的第一障碍物包围盒之间的欧氏距离大于或等于本车宽度，或者没有与第二障碍物包围盒相邻的第一障碍物包围盒。
基于目标凸包障碍物的目标凸包障碍物信息，对所述目标凸包障碍物做出避让决策，包括：
基于所述目标凸包障碍物信息，对满足预设过滤条件的所述目标凸包障碍物打上无需避让标签或无需横向避让标签；和/或，
基于所述目标凸包障碍物信息和本车引导线，对所述目标凸包障碍物打上避让标签，其中，所述避让标签包括左侧通行标签、右侧通行标签或跟随标签。
所述预设过滤条件包括以下至少一种：
所述目标凸包障碍物位于道路之外；
所述目标凸包障碍物的运动状态满足无需横向避让条件；
所述目标凸包障碍物位于本车引导线上。
基于所述目标凸包障碍物信息，对满足预设过滤条件的所述目标凸包障碍物打上无需避让标签或无需横向避让标签，包括：
当所述目标凸包障碍物位于道路之外时，为所述目标凸包障碍物打上所述无需避让标签；
当所述目标凸包障碍物的运动状态满足无需横向避让条件或所述目标凸包障碍物位于本车引导线上时，为所述目标凸包障碍物打上所述无需横向避让标签。
所述无需横向避让条件包括以下任一种：
所述目标凸包障碍物横穿道路；
所述目标凸包障碍物向本车车道变道；
所述目标凸包障碍物的纵向速度大于本车速度。
基于所述目标凸包障碍物信息和本车引导线，对所述目标凸包障碍物打上避让标签，包括：
如果所述目标凸包障碍物位于所述本车引导线上，则对所述目标凸包障碍物打上所述跟随标签；
如果所述目标凸包障碍物没有位于所述本车引导线上，则当所述目标凸包障碍物的质心位于所述本车引导线的左侧时，对所述目标凸包障碍物打上所述右侧通行标签，当所述目标凸包障碍物的质心位于所述本车引导线的右侧时，对所述目标凸包障碍物打上所述左侧通行标签。
所述方法还包括：
如果基于所述环境感知信息，确定本车前方无障碍物或者本车前方第一个障碍物的纵向速度大于或等于本车纵向速度，则将预设时长确定为所述通过时间代价。
所述方法还包括：
基于所述环境感知信息，确定本车ST图曲线和障碍物ST图曲线；
基于所述本车ST图曲线，确定本车危险区；
判断所述预设时间内所述障碍物ST图曲线是否与所述本车危险区存在交叠；
如果所述预设时间内所述障碍物ST图曲线与所述本车危险区存在交叠，则确定所述障碍物在预设时间内进入本车危险区；否则，确定所述障碍物在预设时间内未进入本车危险区。
所述车道决策语义信息还包括通行宽度代价，基于所述环境感知信息，确定每个车道的车道决策语义信息，包括：
基于所述车道信息和所述静态障碍物信息，确定车道的最小通行宽度；
将所述最小通行宽度确定为所述通行宽度代价。
基于所述车道信息和所述静态障碍物信息，确定车道的最小通行宽度，包括：
基于所述车道信息和所述静态障碍物信息，确定所述车道上各静态障碍物的最大通行宽度；
将各静态障碍物的最大通行宽度中的最小值确定为所述车道的最小通行宽度。
所述车道决策语义信息还包括稳定性代价，基于所述环境感知信息，确定每个车道的车道决策语义信息，包括：
基于所述车道信息和所述本车信息，确定本车车道和其他车道；
对于所述本车车道，将第一预设稳定性代价确定为所述稳定性代价；
对于所述其他车道，将第二预设稳定性代价确定为所述稳定性代价，其中，所述第二预设稳定性代价与所述第一预设稳定性代价不同。
基于所述车道决策语义信息，生成可行驶区域，包括：
对所述车道决策语义信息中的各代价进行加权求和；
基于加权求和结果，生成可行驶区域。
在基于所述车道决策语义信息，生成可行驶区域之后，所述方法还包括：
将所述可行驶区域的边界离散化。
在基于所述车道决策语义信息，生成可行驶区域之后，所述方法还包括如下至少一项：
基于预设交通规则，更新所述可行驶区域；
基于车辆的运动学和动力学约束，更新所述可行驶区域；
基于障碍物语义信息和预设安全区，更新所述可行驶区域，所述预设安全区与所述可行驶区域相连；
基于所述静态障碍物信息、障碍物决策语义信息和光线追踪算法，更新所述可行驶区域，所述障碍物决策语义信息包括从障碍物左侧通过或从障碍物右侧通过。
基于障碍物语义信息和预设安全区，更新所述可行驶区域，包括：
基于障碍物语义信息，确定需横向避让的障碍物；
如果所述需横向避让的障碍物的移动轨迹占用了所述预设安全区，则修剪所述占用所述预设安全区对应位置处 的部分可行驶区域。
基于所述静态障碍物信息、障碍物决策语义信息和光线追踪算法，更新所述可行驶区域，包括：
基于所述静态障碍物信息、障碍物决策语义信息和光线追踪算法，确定光线与障碍物的碰撞点，所述碰撞点位于所述可行驶区域内；
基于所述碰撞点，更新所述可行驶区域。
基于所述静态障碍物信息、障碍物决策语义信息和光线追踪算法，确定光线与障碍物的碰撞点，包括：
基于所述障碍物决策语义信息，确定光源点和光线投射方向；
基于所述静态障碍物信息，确定光线投射范围；
基于所述光源点、所述光线投射方向和所述光线投射范围，对障碍物进行光线扫描；
确定光线与所述障碍物的碰撞点。
一种避让障碍物的决策方法，包括：
获取道路信息、第一栅格障碍物的第一栅格障碍物信息和第一凸包障碍物的第一凸包障碍物信息；
基于所述道路信息和所述第一栅格障碍物信息，对所述第一栅格障碍物进行预处理，得到第二栅格障碍物；
将所述第二栅格障碍物转换成第二凸包障碍物；
基于目标凸包障碍物的目标凸包障碍物信息，对所述目标凸包障碍物做出避让决策，其中，所述目标凸包障碍物包括所述第一凸包障碍物和/或所述第二凸包障碍物。
一种车辆可行驶区域生成方法，包括：
获取环境感知信息，其中，所述环境感知信息包括车道信息、障碍物信息和本车信息中的至少两个，所述障碍物信息包括静态障碍物信息和/或动态障碍物信息；
基于所述环境感知信息，确定每个车道的车道决策语义信息，其中，所述车道决策语义信息包括通过时间代价和安全性代价；
基于所述车道决策语义信息，生成可行驶区域。
以上所述仅是本公开的具体实施方式，使本领域技术人员能够理解或实现本公开。对这些实施例的多种修改对本领域的技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本公开的精神或范围的情况下，在其它实施例中实现。因此，本公开将不会被限制于本文所述的这些实施例，而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。
工业实用性
本公开将栅格类型的障碍物转换成凸包类型的障碍物，实现对栅格类型和凸包类型两种类型障碍物的统一决策，从而能够简化混合类型障碍物的障碍物决策流程，加速障碍物决策过程，使得决策规划模块能够方便、快速地进行障碍物决策，具有很强的工业实用性。

Claims (19)

  1. 一种车辆决策规划方法，其特征在于，包括：
    生成基坐标系；
    在所述基坐标系下生成引导线，以决策车辆未来的一个大致行驶轨迹；
    在所述引导线的约束下，进行障碍物决策；
    根据障碍物决策生成可行驶区域。
  2. 根据权利要求1所述的方法，其特征在于，进行障碍物决策，包括：
    获取道路信息、第一栅格障碍物的第一栅格障碍物信息和第一凸包障碍物的第一凸包障碍物信息；
    基于所述道路信息和所述第一栅格障碍物信息，对所述第一栅格障碍物进行预处理，得到第二栅格障碍物；
    将所述第二栅格障碍物转换成第二凸包障碍物；
    基于目标凸包障碍物的目标凸包障碍物信息，对所述目标凸包障碍物做出避让决策，其中，所述目标凸包障碍物包括所述第一凸包障碍物和/或所述第二凸包障碍物。
  3. 根据权利要求2所述的方法，其特征在于，基于所述道路信息和所述第一栅格障碍物信息，对所述第一栅格障碍物进行预处理，得到第二栅格障碍物，包括：
    基于所述道路信息和所述第一栅格障碍物信息，过滤掉位于道路之外的所述第一栅格障碍物；将剩余所述第一栅格障碍物作为所述第二栅格障碍物；和/或，
    基于所述道路信息和所述第一栅格障碍物信息，确定位于所述道路内的所述第一栅格障碍物；对位于所述道路内的所述第一栅格障碍物进行聚合处理；将聚合处理后的所述第一栅格障碍物作为所述第二栅格障碍物。
  4. 根据权利要求3所述的方法，其特征在于，基于所述道路信息和所述第一栅格障碍物信息，过滤掉位于道路之外的所述第一栅格障碍物，包括：
    基于所述道路信息，生成沿道路通行方向的道路包围盒；
    基于所述第一栅格障碍物信息，生成所述第一栅格障碍物的栅格障碍物包围盒；
    基于所述栅格障碍物包围盒和所述道路包围盒，确定位于道路之外的所述第一栅格障碍物；
    过滤掉位于道路之外的所述第一栅格障碍物。
  5. 根据权利要求1-4任一项所述的方法，其特征在于，根据障碍物决策生成可行驶区域，包括：
    获取环境感知信息，其中，所述环境感知信息包括车道信息、障碍物信息和本车信息中的至少两个，所述障碍物信息包括静态障碍物信息和/或动态障碍物信息；
    基于所述环境感知信息，确定每个车道的车道决策语义信息，其中，所述车道决策语义信息包括通过时间代价和安全性代价；
    基于所述车道决策语义信息，生成可行驶区域。
  6. 根据权利要求5所述的方法，其特征在于，当所述车道决策语义信息包括通过时间代价时，基于所述环境感知信息，确定每个车道的车道决策语义信息，包括：
    对于每个车道，基于所述环境感知信息，确定本车与本车前方第一个障碍物的碰撞时间；
    将所述碰撞时间确定为所述通过时间代价。
  7. 根据权利要求5所述的方法，其特征在于，当所述车道决策语义信息包括安全性代价时，基于所述环境感知信息，确定每个车道的车道决策语义信息，包括：
    基于所述车道信息和所述本车信息，确定本车车道和其他车道；
    对于所述本车车道，将第一预设安全性代价确定为所述安全性代价；
    对于所述其他车道，如果基于所述环境感知信息，确定障碍物在预设时间内进入本车危险区，则将第二预设安全性代价确定为所述安全性代价，如果基于所述环境感知信息，确定所述障碍物在所述预设时间内未进入本车危险区，则将所述第一预设安全性代价确定为所述安全性代价，其中，所述第二预设安全性代价与所述第一预设安全性代价不同。
  8. 一种避让障碍物的决策方法，其特征在于，包括：
    获取道路信息、第一栅格障碍物的第一栅格障碍物信息和第一凸包障碍物的第一凸包障碍物信息；
    基于所述道路信息和所述第一栅格障碍物信息，对所述第一栅格障碍物进行预处理，得到第二栅格障碍物；
    将所述第二栅格障碍物转换成第二凸包障碍物；
    基于目标凸包障碍物的目标凸包障碍物信息，对所述目标凸包障碍物做出避让决策，其中，所述目标凸包障碍物包括所述第一凸包障碍物和/或所述第二凸包障碍物。
  9. 根据权利要求8所述的方法，其特征在于，基于所述道路信息和所述第一栅格障碍物信息，对所述第一栅格障碍物进行预处理，得到第二栅格障碍物，包括：
    基于所述道路信息和所述第一栅格障碍物信息，过滤掉位于道路之外的所述第一栅格障碍物；将剩余所述第一栅格障碍物作为所述第二栅格障碍物；和/或，
    基于所述道路信息和所述第一栅格障碍物信息，确定位于所述道路内的所述第一栅格障碍物；对位于所述道路内的所述第一栅格障碍物进行聚合处理；将聚合处理后的所述第一栅格障碍物作为所述第二栅格障碍物。
  10. 一种车辆可行驶区域生成方法，其特征在于，包括：
    获取环境感知信息，其中，所述环境感知信息包括车道信息、障碍物信息和本车信息中的至少两个，所述障碍物信息包括静态障碍物信息和/或动态障碍物信息；
    基于所述环境感知信息，确定每个车道的车道决策语义信息，其中，所述车道决策语义信息包括通过时间代价和安全性代价；
    基于所述车道决策语义信息，生成可行驶区域。
  11. 根据权利要求10所述的方法，其特征在于，当所述车道决策语义信息包括通过时间代价时，基于所述环境感知信息，确定每个车道的车道决策语义信息，包括：
    对于每个车道，基于所述环境感知信息，确定本车与本车前方第一个障碍物的碰撞时间；
    将所述碰撞时间确定为所述通过时间代价。
  12. 根据权利要求10所述的方法，其特征在于，当所述车道决策语义信息包括安全性代价时，基于所述环境感知信息，确定每个车道的车道决策语义信息，包括：
    基于所述车道信息和所述本车信息，确定本车车道和其他车道；
    对于所述本车车道，将第一预设安全性代价确定为所述安全性代价；
    对于所述其他车道，如果基于所述环境感知信息，确定障碍物在预设时间内进入本车危险区，则将第二预设安全性代价确定为所述安全性代价，如果基于所述环境感知信息，确定所述障碍物在所述预设时间内未进入本车危险区，则将所述第一预设安全性代价确定为所述安全性代价，其中，所述第二预设安全性代价与所述第一预设安全性代价不同。
  13. 一种车辆决策规划装置，其特征在于，包括：
    基坐标系生成器，用于生成基坐标系；
    引导线生成器，用于在所述基坐标系下生成引导线，以决策车辆未来的一个大致行驶轨迹；
    障碍物决策器，用于在所述引导线的约束下，进行障碍物决策；
    行驶空间生成器，用于根据障碍物决策生成可行驶区域。
  14. 根据权利要求13所述的装置，其特征在于，障碍物决策器包括：
    信息获取模块，用于获取道路信息、第一栅格障碍物的第一栅格障碍物信息和第一凸包障碍物的第一凸包障碍物信息；
    预处理模块，用于基于所述道路信息和所述第一栅格障碍物信息，对所述第一栅格障碍物进行预处理，得到第二栅格障碍物，其中，所述第二栅格障碍物的数量小于所述第一栅格障碍物的数量；
    类型转换模块，用于将所述第二栅格障碍物转换成第二凸包障碍物；
    避让决策模块，用于基于目标凸包障碍物的目标凸包障碍物信息，对所述目标凸包障碍物做出避让决策，其中，所述目标凸包障碍物包括所述第一凸包障碍物和/或所述第二凸包障碍物。
  15. 根据权利要求13所述的装置，其特征在于，行驶空间生成器包括：
    感知信息获取模块，用于获取环境感知信息，其中，所述环境感知信息包括车道信息、障碍物信息和本车信息中的至少两个，所述障碍物信息包括静态障碍物信息和/或动态障碍物信息；
    车道决策语义信息确定模块，用于基于所述环境感知信息，确定每个车道的车道决策语义信息，其中，所述车道决策语义信息包括通过时间代价和安全性代价；
    可行驶区域生成模块，用于基于所述车道决策语义信息，生成可行驶区域。
  16. 一种避让障碍物的决策装置，其特征在于，包括：
    信息获取模块，用于获取道路信息、第一栅格障碍物的第一栅格障碍物信息和第一凸包障碍物的第一凸包障碍物信息；
    预处理模块，用于基于所述道路信息和所述第一栅格障碍物信息，对所述第一栅格障碍物进行预处理，得到第二栅格障碍物，其中，所述第二栅格障碍物的数量小于所述第一栅格障碍物的数量；
    类型转换模块，用于将所述第二栅格障碍物转换成第二凸包障碍物；
    避让决策模块，用于基于目标凸包障碍物的目标凸包障碍物信息，对所述目标凸包障碍物做出避让决策，其中，所述目标凸包障碍物包括所述第一凸包障碍物和/或所述第二凸包障碍物。
  17. 一种车辆可行驶区域生成装置，其特征在于，包括：
    感知信息获取模块，用于获取环境感知信息，其中，所述环境感知信息包括车道信息、障碍物信息和本车信息中的至少两个，所述障碍物信息包括静态障碍物信息和/或动态障碍物信息；
    车道决策语义信息确定模块，用于基于所述环境感知信息，确定每个车道的车道决策语义信息，其中，所述车道决策语义信息包括通过时间代价和安全性代价；
    可行驶区域生成模块，用于基于所述车道决策语义信息，生成可行驶区域。
  18. 一种电子设备，其特征在于，包括：
    存储器以及一个或多个处理器；
    其中，所述存储器与所述一个或多个处理器通信连接，所述存储器中存储有可被所述一个或多个处理器执行的指令，所述指令被所述一个或多个处理器执行时，所述电子设备用于实现如权利要求1-12中任一项所述的方法。
  19. 一种计算机可读存储介质，其上存储有计算机可执行指令，其特征在于，当所述计算机可执行指令被计算装置执行时，可用来实现如权利要求1-12中任一项所述的方法。
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