Vehicle trajectory planning method and device and storage medium

Abstract

The invention relates to a vehicle trajectory planning method and device, and a storage medium, and the method comprises the steps: searching a reference line based on a three-lane model, predicting future trajectory points of a vehicle and obstacles around the vehicle based on the reference line, carrying out the time-space discretization of a current position point of the vehicle and a plurality of predicted trajectory points, and carrying out the prediction of the predicted trajectory points; each time layer is bound with a corresponding point, the binding points of any two adjacent time layers are connected to obtain a plurality of connecting edges, and the two adjacent time layers are connected to form a plurality of connecting edges, that is, the vehicle has a plurality of possible driving tracks in a certain time period, and further, the vehicle can be driven by a plurality of driving tracks in a certain time period. And calculating the cost of each connecting edge according to a preset cost function, and performing trajectory planning according to the cost of each connecting edge, a preset constraint condition and the predicted trajectory points of the obstacles bound with the plurality of time layers to obtain a vehicle planning trajectory which meets the lane boundary constraint and has the minimum cost. Therefore, vehicle trajectory planning in a dynamic obstacle avoidance scene is realized.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle trajectory planning, and in particular to a vehicle trajectory planning method and a device and storage medium thereof. Background Art

[0002] Vehicle trajectory planning refers to determining the vehicle's driving trajectory through a trajectory planning algorithm given the road network and environmental information so that it achieves a specific goal and meets various constraints. The design of the vehicle trajectory planning algorithm should take into account the requirements of road conditions, vehicle dynamics, safety, efficiency, etc. to provide a reliable, efficient and safe driving trajectory. With the development of autonomous driving technology, vehicle trajectory planning also faces higher challenges, including complex traffic environments, multi-vehicle collaboration, dynamic obstacle handling, etc. Therefore, different scenarios and needs may require different vehicle trajectory planning algorithms to achieve the best results. Summary of the invention

[0003] The purpose of this application is to propose a vehicle trajectory planning method and device, and a computer-readable storage medium to achieve vehicle trajectory planning in dynamic obstacle avoidance scenarios.

[0004] To achieve the above object, an embodiment of the present application provides a vehicle trajectory planning method, the method comprising:

[0005] Searching for reference lines based on a three-lane model; the reference lines include the center lines of the lane and the two lanes on the left, or the center lines of the lane and the two lanes on the right, or the center lines of the lane and the left and right adjacent lanes;

[0006] Generate multiple predicted trajectory points of the vehicle and obstacles around the vehicle within a preset distance range based on the reference line;

[0007] Binding the current position point of the vehicle and the plurality of predicted trajectory points to a plurality of preset time layers respectively, with a certain time interval between two adjacent time layers; wherein the first time layer is bound to the current position point of the vehicle, and the other time layers are bound to the predicted trajectory points of the vehicle and the obstacles;

[0008] The current position points or predicted trajectory points of the vehicle bound to any two adjacent time layers are connected to obtain multiple connection edges, and any two points are connected to form a connection edge;

[0009] A preset cost function calculates the cost of each connecting edge, and based on the cost of each connecting edge, preset constraints, and predicted trajectory points of obstacles bound to the multiple time layers, trajectory planning is performed to obtain a vehicle planning trajectory that satisfies the lane boundary constraints and has the lowest cost.

[0010] Preferably, the predicted trajectory points include horizontal predicted trajectory points and vertical predicted trajectory points;

[0011] The predicted lateral trajectory point of the vehicle is the predicted future trajectory point after the lateral movement of the vehicle, and the predicted longitudinal trajectory point of the vehicle is the predicted future trajectory point after the longitudinal movement of the vehicle;

[0012] The lateral predicted trajectory point of the obstacle is the predicted future trajectory point after the obstacle moves laterally, and the longitudinal predicted trajectory point of the obstacle is the predicted future trajectory point after the obstacle moves longitudinally.

[0013] Preferably, the cost of each connecting edge includes at least one of an acceleration cost, an acceleration change rate cost, a reference line distance cost, a collision cost, and a lateral boundary cost;

[0014] The acceleration cost is the cost corresponding to the acceleration required for the vehicle to move from one point on the connecting edge to another point;

[0015] The acceleration change rate cost is the cost corresponding to the acceleration change rate required for the vehicle to move from one point on the connecting edge to another point;

[0016] The reference line distance cost is the cost corresponding to the distance from one point on the connecting edge to another point on the lane where the vehicle is located;

[0017] The collision cost is the cost corresponding to whether the vehicle collides with an obstacle when moving from one point on the connecting edge to another point;

[0018] The lateral boundary cost is the cost corresponding to the distance between the vehicle and the left and right lane boundaries when the vehicle moves from one point on the connecting edge to another point.

[0019] Preferably, the constraint conditions include lane boundary constraints and obstacle linear boundary constraints;

[0020] The lane boundary constraint means that the vehicle can cross or press the dashed line, but cannot cross or press the solid line;

[0021] The constraint conditions include obstacle linear boundary constraints, which means that the obstacle boundary is represented by a straight line, one side of the straight line is considered a collision, and the other side of the straight line is considered safe and collision-free, and the vehicle needs to travel on the other side of the straight line.

[0022] Preferably, the multiple time layers include multiple time layers of a first time period and multiple time layers of a second time period, any two adjacent time layers of the multiple time layers of the first time period are separated by t1 time, and any two adjacent time layers of the multiple time layers of the second time period are separated by t2 time; wherein, both t2 time and t1 time are preset values, and t2 time is less than t1 time.

[0023] The present application also provides a vehicle trajectory planning device, the device comprising:

[0024] A reference line acquisition module, used to search for reference lines based on a three-lane model; the reference lines include the center lines of the lane and the two lanes on the left, or the center lines of the lane and the two lanes on the right, or the center lines of the lane and the left and right adjacent lanes;

[0025] A trajectory point prediction module, used to generate a plurality of predicted trajectory points of the vehicle and obstacles around the vehicle within a preset distance range based on the reference line;

[0026] A time-space discretization module is used to bind the current position point of the vehicle and the multiple predicted trajectory points to multiple preset time layers respectively, with a certain time interval between two adjacent time layers; wherein the first time layer is bound to the current position point of the vehicle, and the other time layers are bound to the predicted trajectory points of the vehicle and obstacles;

[0027] A connection edge generation module is used to connect the current position points or predicted trajectory points of the vehicle bound to any two adjacent time layers to obtain multiple connection edges, and any two points are connected to form a connection edge;

[0028] The optimization solution module is used to calculate the cost of each connecting edge according to a preset cost function, and perform trajectory planning based on the cost of each connecting edge, preset constraints, and predicted trajectory points of obstacles bound to the multiple time layers to obtain a vehicle planning trajectory that satisfies the lane boundary constraints and has the lowest cost.

[0029] Preferably, the predicted trajectory points include horizontal predicted trajectory points and vertical predicted trajectory points;

[0030] The predicted lateral trajectory point of the vehicle is the predicted future trajectory point after the lateral movement of the vehicle, and the predicted longitudinal trajectory point of the vehicle is the predicted future trajectory point after the longitudinal movement of the vehicle;

[0031] The lateral predicted trajectory point of the obstacle is the predicted future trajectory point after the obstacle moves laterally, and the longitudinal predicted trajectory point of the obstacle is the predicted future trajectory point after the obstacle moves longitudinally.

[0032] Preferably, the cost of each connecting edge includes at least one of an acceleration cost, an acceleration change rate cost, a reference line distance cost, a collision cost, and a lateral boundary cost;

[0033] The acceleration cost is the cost corresponding to the acceleration required for the vehicle to move from one point on the connecting edge to another point;

[0034] The acceleration change rate cost is the cost corresponding to the acceleration change rate required for the vehicle to move from one point on the connecting edge to another point;

[0035] The reference line distance cost is the cost corresponding to the distance from one point on the connecting edge to another point on the lane where the vehicle is located;

[0036] The collision cost is the cost corresponding to whether the vehicle collides with an obstacle when moving from one point on the connecting edge to another point;

[0037] The lateral boundary cost is the cost corresponding to the distance between the vehicle and the left and right lane boundaries when the vehicle moves from one point on the connecting edge to another point.

[0038] Preferably, the constraint conditions include lane boundary constraints and obstacle linear boundary constraints;

[0039] The lane boundary constraint means that the vehicle can cross or press the dashed line, but cannot cross or press the solid line;

[0040] The constraint conditions include obstacle linear boundary constraints, which means that the obstacle boundary is represented by a straight line, one side of the straight line is considered a collision, and the other side of the straight line is considered safe and collision-free, and the vehicle needs to travel on the other side of the straight line.

[0041] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the vehicle trajectory planning method as described above is implemented.

[0042] The vehicle trajectory planning method and device and computer-readable storage medium provided in the embodiments of the present application have the following beneficial effects:

[0043] The embodiment of the present application searches for a reference line based on a three-lane model, predicts the future trajectory points of the vehicle and obstacles around the vehicle based on the reference line, performs spatiotemporal discretization on the current position point of the vehicle and the multiple predicted trajectory points, binds corresponding points to each time layer, and connects the points bound to any two adjacent time layers to obtain multiple connecting edges. The connecting edges can be understood as trajectory segments that the vehicle may travel. Many connecting edges are formed between two adjacent time layers, which means that the vehicle has many possible driving trajectories in a certain time period. Furthermore, the cost of each connecting edge is calculated according to a preset cost function, and trajectory planning is performed based on the cost of each connecting edge, the preset constraints, and the predicted trajectory points of the obstacles bound to the multiple time layers to obtain a vehicle planning trajectory that meets the lane boundary constraints and has the lowest cost, thereby realizing vehicle trajectory planning for dynamic obstacle avoidance scenarios.

[0044] Details and advantages not described in detail in the embodiments of the present application are described in detail in the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1 The present invention is a flow chart of a vehicle trajectory planning method in an embodiment of the present application.

[0047] Figure 2 Schematic diagram of the connection of trajectory points between adjacent time layers in an embodiment of the present application.

[0048] Figure 3 This is a schematic diagram of a drivable area under lane boundary constraints in an embodiment of the present application.

[0049] Figure 4 This is a structural diagram of a vehicle trajectory planning device in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The detailed description of the drawings is intended as an illustration of the current embodiment of the present application, and is not intended to represent the only form in which the present application can be implemented. It should be understood that the same or equivalent functions can be accomplished by different embodiments intended to be included in the spirit and scope of the present application.

[0051] See also Figure 1 An embodiment of the present application provides a vehicle trajectory planning method, comprising the following steps:

[0052] Step S10, searching for reference lines based on the three-lane model; the reference lines include the center lines of the lane and the two lanes on the left, or the center lines of the lane and the two lanes on the right, or the center lines of the lane and the left and right adjacent lanes.

[0053] Specifically, in the three-lane model, the road is divided into three parallel lanes, each lane allows vehicles to travel in the same direction, vehicles can change lanes between different lanes, and choose the appropriate lane according to traffic conditions; when the vehicle is in the rightmost lane, it can search for the center line of its lane and the two lanes on the left; when the vehicle is in the leftmost lane, it can search for the center line of its lane and the two lanes on the right; when the vehicle is in the middle lane, it can search for the center line of its lane and the left and right adjacent lanes.

[0054] Step S20: generating a plurality of predicted trajectory points of the vehicle and obstacles around the vehicle within a preset distance range based on the reference line.

[0055] Specifically, obstacles around the vehicle can be detected by the vehicle's sensing unit, which includes but is not limited to vehicle cameras, ultrasonic radars, laser radars, etc. The preset distance range is the upper limit of the trajectory point prediction. If the range is exceeded, inaccurate predictions are likely to occur. Based on this, it can also be understood that the trajectory planning of this embodiment is carried out in sections, and the planned trajectory is continuously adjusted during the driving of the vehicle to achieve trajectory planning in a dynamic obstacle avoidance scenario. If the parking point is within the preset distance range, the end point of the output planned trajectory is the parking point, which can be the destination of the vehicle or a temporary parking location, such as the parking of the vehicle in front, or the parking at a red light at an intersection. It should be noted that multiple predicted trajectory points of the vehicle and multiple predicted trajectory points of the obstacle are evenly distributed, and the interval between two adjacent trajectory points is the same, for example, 3.5 meters. The corresponding preset distance range can be within 133 meters in the longitudinal direction in front of the vehicle. 133 meters is calculated based on a maximum speed of 80 km / h for 6 seconds and needs to be divisible by 3.5 as much as possible.

[0056] Step S30, the current position point of the vehicle and the multiple predicted trajectory points are respectively bound to multiple preset time layers, with a certain time interval between two adjacent time layers; among which, the first time layer is bound to the current position point of the vehicle, and the other time layers are bound to the predicted trajectory points of the vehicle and obstacles.

[0057] Specifically, this embodiment discretizes the current position point of the vehicle and the multiple predicted trajectory points in time and space, and binds each time layer to the corresponding point. For example, the time layer is set at a time interval of 0.2 seconds, the first time layer of 0 seconds is bound to the current position point of the vehicle, the second time layer of 0.2 seconds is bound to the trajectory point that the vehicle and the obstacle may reach after 0.2 seconds, and the third time layer of 0.4 seconds is bound to the trajectory point that the vehicle and the obstacle may reach after 0.4 seconds, and so on, until all time layers are bound to the corresponding trajectory points.

[0058] Step S40, connecting the current position points or predicted trajectory points of the vehicle bound to any two adjacent time layers to obtain a plurality of connection edges, and connecting any two points to form a connection edge.

[0059] Specifically, if Figure 2 As shown, this embodiment connects the points bound by any two adjacent time layers to obtain multiple connecting edges. The connecting edges can be understood as the trajectory segments that the vehicle may travel. The two adjacent time layers are connected to form many connecting edges, which means that the vehicle has many possible driving trajectories in a certain time period.

[0060] In step S50, a preset cost function is used to calculate the cost of each connecting edge, and trajectory planning is performed based on the cost of each connecting edge, preset constraints, and predicted trajectory points of obstacles bound to the multiple time layers to obtain a vehicle planning trajectory that satisfies the lane boundary constraints and has the lowest cost.

[0061] Specifically, the constraint conditions can be set according to actual conditions, such as constraints on road traffic rules (such as stop signs, traffic lights, lane regulations), and constraints on the dynamic performance of the vehicle (such as maximum acceleration, maximum speed, minimum turning radius, etc.). In step S50, the predicted trajectory points of the obstacles bound to the multiple time layers can be determined according to the predicted trajectory points of the obstacles in the two time layers corresponding to each connecting edge, that is, the possible positions of the obstacles in the two time layers can be determined, thereby calculating the collision risk, thereby helping to plan the driving trajectory of the vehicle, avoiding collisions, and improving safety. The purpose of step S50 is to address a vehicle planning trajectory that meets the lane boundary constraints and has the lowest cost. The vehicle planning trajectory is formed by connecting edges between different time layers. Based on the above embodiment method, vehicle trajectory planning for dynamic obstacle avoidance scenarios can be achieved.

[0062] In some embodiments, since the vehicle driving may include movement in both longitudinal and lateral directions, the predicted trajectory points of the vehicle and the obstacle in this embodiment include lateral predicted trajectory points and longitudinal predicted trajectory points.

[0063] The predicted lateral trajectory points of the vehicle are the predicted future trajectory points after the lateral movement of the vehicle, and the predicted longitudinal trajectory points of the vehicle are the predicted future trajectory points after the longitudinal movement of the vehicle; specifically, the lateral trajectory point prediction can be obtained by evenly scattering points from the center line of the lane where the vehicle is located to the center line of the adjacent lane, or from the center line of the lane where the vehicle is located to the boundary of the adjacent lane.

[0064] The lateral predicted trajectory points of the obstacle are the predicted future trajectory points after the obstacle moves laterally, and the longitudinal predicted trajectory points of the obstacle are the predicted future trajectory points after the obstacle moves longitudinally; specifically, the longitudinal trajectory point prediction can be obtained by evenly scattering points in the longitudinal direction of the reference line.

[0065] In some embodiments, the cost of each connecting edge includes at least one of an acceleration cost, an acceleration rate cost, a reference line distance cost, a collision cost, and a lateral boundary cost.

[0066] Among them, the acceleration cost is the cost corresponding to the acceleration required for the vehicle to move from one point on the connecting edge to another point; the calculation method of the acceleration cost can be defined by oneself, for example, the greater the acceleration required for the vehicle to move from one point on the connecting edge to another point, the greater the corresponding acceleration cost, and the smaller the acceleration required for the vehicle to move from one point on the connecting edge to another point, the smaller the corresponding acceleration cost, because if an emergency occurs in front during the acceleration process, it may be difficult to avoid it in time.

[0067] For another example, it can be set to the following function:

[0068]

[0069] Among them, cost(a) represents the acceleration cost, and a represents the acceleration.

[0070] The acceleration change rate cost is the cost corresponding to the acceleration change rate (jerk) required for the vehicle to move from one point on the connecting edge to another point.

[0071] The calculation method of the acceleration rate cost can be defined by yourself. For example, the greater the acceleration required for the vehicle to move from one point on the connecting edge to another, the greater the corresponding acceleration rate cost. The smaller the acceleration required for the vehicle to move from one point on the connecting edge to another, the smaller the corresponding acceleration rate cost. This is because if an emergency occurs in front during the acceleration change, it may be difficult to avoid it in time.

[0072] For another example, it can be set to the following function:

[0073]

[0074] Among them, cost(j) represents the acceleration change rate cost, and j represents the acceleration change rate.

[0075] The reference line distance cost is the cost corresponding to the distance from one point on the connecting edge to another point on the lane where the vehicle is located, as shown in the following function:

[0076] cost((x,y),(x ref ,y ref ))=(xx ref ) 2 +(yy ref ) 2

[0077] Among them, cost((x,y),(x ref ,y ref )) represents the reference line distance cost, x, y are the longitudinal and lateral coordinates of the other point respectively, x ref ,y ref are respectively the longitudinal coordinate and the lateral coordinate of the point on the lane centerline closest to the other point.

[0078] The collision cost is the cost corresponding to whether the vehicle collides with an obstacle when moving from one point on the connecting edge to another point, for example:

[0079] If the distance d between the ego vehicle and the obstacle is greater than the preset safety distance dis_safe, the collision cost cost(ego,obs) = 0, where ego is the ego vehicle and obs is the obstacle;

[0080] If the distance d between the vehicle and the obstacle is less than or equal to the preset safety distance dis_safe, then further calculate whether a collision occurs. If a collision occurs, the collision cost cost(ego,obs)=∞. If no collision occurs, the collision cost cost(ego,obs) is calculated based on the distance d between the vehicle and the obstacle, for example, 1 / d-1 / dis_safe.

[0081] The lateral boundary cost is the cost corresponding to the distance between the vehicle and the left and right lane boundaries when the vehicle moves from one point on the connecting edge to another point, such as shown in the following function:

[0082]

[0083] l safe_buffer It is the preset lateral safety distance, which can be set to 0.2 times ego_width. l_min refers to the right lateral boundary, l_max refers to the left lateral boundary, and ego_width is the width of the vehicle.

[0084] In some embodiments, the constraints include lane boundary constraints and obstacle linear boundary constraints;

[0085] The lane boundary constraint means that the vehicle can cross or press the dashed line, but cannot cross or press the solid line. Specifically, according to the road traffic rules, the vehicle can cross the dashed line, but cannot cross the solid line. Based on this, the vehicle's drivable range is formed. The drivable range is, for example, Figure 3 As shown, Figure 3 The drivable range in is the drivable position range of the center of the vehicle. When the center of the vehicle is within this range, there is no compaction line.

[0086] The constraint condition includes an obstacle linear boundary constraint, which means that the obstacle boundary is represented by a straight line, one side of the straight line is regarded as a collision, and the other side of the straight line is regarded as safe and collision-free, and the vehicle needs to travel on the other side of the straight line; specifically, as mentioned above, obstacle information can be obtained through perception units such as vehicle cameras, ultrasonic radars, and lidars, and the obstacle information includes the surface structure of the obstacle. In this embodiment, obstacle boundary information is extracted from the obstacle information output by the perception unit, and the obstacle boundary is described by a straight line. The obstacle linear boundary constraint can be understood as a linear inequality, one side of the inequality is regarded as a collision, and the other side is regarded as safe and collision-free.

[0087] In some embodiments, the multiple time layers include multiple time layers of a first time period and multiple time layers of a second time period, and any two adjacent time layers of the multiple time layers of the first time period are separated by t1 time, and any two adjacent time layers of the multiple time layers of the second time period are separated by t2 time; wherein, both t2 time and t1 time are preset values, and t2 time is less than t1 time.

[0088] Specifically, the t2 time is, for example, 0.2 seconds, the t1 time is, for example, 1 second, the first time period is, for example, 0 to 5 seconds, and the second time period is, for example, 5 to 6 seconds. The purpose of this is that if it is too dense, too many search lines will lead to excessive computing consumption, and if it is too sparse, the effective sampling range is too small and the flexibility is poor. Therefore, in the spatiotemporal union of this embodiment, each layer corresponds to a time, the 5.2 second layer and the 5.4 second layer are different layers, and the original connection is sampled at 1 second, and can only be connected from the 5 second layer to the 6 second layer. However, in order to ensure that the last section can pass quickly or stop as soon as possible, it is necessary to allow a connection line from 5 seconds to 5.2 seconds to avoid excessive computing consumption while also improving flexibility.

[0089] Corresponding to the vehicle trajectory planning method of the above embodiment, another embodiment of the present application further provides a vehicle trajectory planning device, the device comprising:

[0090] A reference line acquisition module, used to search for reference lines based on a three-lane model; the reference lines include the center lines of the lane and the two lanes on the left, or the center lines of the lane and the two lanes on the right, or the center lines of the lane and the left and right adjacent lanes;

[0091] A trajectory point prediction module, used to generate a plurality of predicted trajectory points of the vehicle and obstacles around the vehicle within a preset distance range based on the reference line;

[0092] A time-space discretization module is used to bind the current position point of the vehicle and the multiple predicted trajectory points to multiple preset time layers respectively, with a certain time interval between two adjacent time layers; wherein the first time layer is bound to the current position point of the vehicle, and the other time layers are bound to the predicted trajectory points of the vehicle and obstacles;

[0093] A connection edge generation module is used to connect the current position points or predicted trajectory points of the vehicle bound to any two adjacent time layers to obtain multiple connection edges, and any two points are connected to form a connection edge;

[0094] The optimization solution module is used to calculate the cost of each connecting edge according to a preset cost function, and perform trajectory planning based on the cost of each connecting edge, preset constraints, and predicted trajectory points of obstacles bound to the multiple time layers to obtain a vehicle planning trajectory that satisfies the lane boundary constraints and has the lowest cost.

[0095] In some embodiments, the predicted trajectory points include horizontal predicted trajectory points and vertical predicted trajectory points;

[0096] The predicted lateral trajectory point of the vehicle is the predicted future trajectory point after the lateral movement of the vehicle, and the predicted longitudinal trajectory point of the vehicle is the predicted future trajectory point after the longitudinal movement of the vehicle;

[0097] The lateral predicted trajectory point of the obstacle is the predicted future trajectory point after the obstacle moves laterally, and the longitudinal predicted trajectory point of the obstacle is the predicted future trajectory point after the obstacle moves longitudinally.

[0098] In some embodiments, the cost of each connecting edge includes at least one of an acceleration cost, an acceleration rate cost, a reference line distance cost, a collision cost, and a lateral boundary cost;

[0099] The acceleration cost is the cost corresponding to the acceleration required for the vehicle to move from one point on the connecting edge to another point;

[0100] The acceleration change rate cost is the cost corresponding to the acceleration change rate required for the vehicle to move from one point on the connecting edge to another point;

[0101] The reference line distance cost is the cost corresponding to the distance from one point on the connecting edge to another point on the lane where the vehicle is located;

[0102] The collision cost is the cost corresponding to whether the vehicle collides with an obstacle when moving from one point on the connecting edge to another point;

[0103] The lateral boundary cost is the cost corresponding to the distance between the vehicle and the left and right lane boundaries when the vehicle moves from one point on the connecting edge to another point.

[0104] In some embodiments, the constraints include lane boundary constraints and obstacle linear boundary constraints;

[0105] The lane boundary constraint means that the vehicle can cross or press the dashed line, but cannot cross or press the solid line;

[0106] The constraint conditions include obstacle linear boundary constraints, which means that the obstacle boundary is represented by a straight line, one side of the straight line is considered a collision, and the other side of the straight line is considered safe and collision-free, and the vehicle needs to travel on the other side of the straight line.

[0107] In some embodiments, the multiple time layers include multiple time layers of a first time period and multiple time layers of a second time period, any two adjacent time layers of the multiple time layers of the first time period are separated by t1 time, and any two adjacent time layers of the multiple time layers of the second time period are separated by t2 time, and t2 time is less than t1 time.

[0108] It should be noted that the vehicle trajectory planning device of this embodiment corresponds to the vehicle trajectory planning method of the above embodiment. Therefore, the undetailed parts of the vehicle trajectory planning device of this embodiment can be obtained by referring to the contents of the vehicle trajectory planning method of the above embodiment, and will not be repeated here.

[0109] Furthermore, if the vehicle trajectory planning device of the above embodiment is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0110] Another embodiment of the present application further proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the vehicle trajectory planning method as described in the above embodiment is implemented.

[0111] Specifically, the computer-readable storage medium may include: any entity or recording medium that can carry the computer program instructions, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0112] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A vehicle trajectory planning method, characterized in that: The method comprises: Searching for reference lines based on a three-lane model; the reference lines include the center lines of the lane and the two lanes on the left, or the center lines of the lane and the two lanes on the right, or the center lines of the lane and the left and right adjacent lanes; Generate multiple predicted trajectory points of the vehicle and obstacles around the vehicle within a preset distance range based on the reference line; Binding the current position point of the vehicle and the plurality of predicted trajectory points to a plurality of preset time layers respectively, with a certain time interval between two adjacent time layers; wherein the first time layer is bound to the current position point of the vehicle, and the other time layers are bound to the predicted trajectory points of the vehicle and the obstacles; The current position points or predicted trajectory points of the vehicle bound to any two adjacent time layers are connected to obtain multiple connection edges, and any two points are connected to form a connection edge; A preset cost function calculates the cost of each connecting edge, and based on the cost of each connecting edge, preset constraints, and predicted trajectory points of obstacles bound to the multiple time layers, trajectory planning is performed to obtain a vehicle planning trajectory that satisfies the lane boundary constraints and has the lowest cost.

2. The method according to claim 1, characterized in that The predicted trajectory points include horizontal predicted trajectory points and vertical predicted trajectory points; The predicted lateral trajectory point of the vehicle is the predicted future trajectory point after the lateral movement of the vehicle, and the predicted longitudinal trajectory point of the vehicle is the predicted future trajectory point after the longitudinal movement of the vehicle; The lateral predicted trajectory point of the obstacle is the predicted future trajectory point after the obstacle moves lateraly, and the longitudinal predicted trajectory point of the obstacle is the predicted future trajectory point after the obstacle moves longitudinally.

3. The method according to claim 1, characterized in that The cost of each connecting edge includes at least one of an acceleration cost, an acceleration change rate cost, a reference line distance cost, a collision cost, and a lateral boundary cost; The acceleration cost is the cost corresponding to the acceleration required for the vehicle to move from one point on the connecting edge to another point; The acceleration change rate cost is the cost corresponding to the acceleration change rate required for the vehicle to move from one point on the connecting edge to another point; The reference line distance cost is the cost corresponding to the distance from one point on the connecting edge to another point on the lane where the vehicle is located; The collision cost is the cost corresponding to whether the vehicle collides with an obstacle when moving from one point on the connecting edge to another point; The lateral boundary cost is the cost corresponding to the distance between the vehicle and the left and right lane boundaries when the vehicle moves from one point on the connecting edge to another point.

4. The method according to claim 1, characterized in that The constraints include lane boundary constraints and obstacle linear boundary constraints; The lane boundary constraint means that the vehicle can cross or press the dashed line, but cannot cross or press the solid line; The constraint conditions include obstacle linear boundary constraints, which means that the obstacle boundary is represented by a straight line, one side of the straight line is considered a collision, and the other side of the straight line is considered safe and collision-free, and the vehicle needs to travel on the other side of the straight line.

5. The method according to claim 1, characterized in that The multiple time layers include multiple time layers of a first time period and multiple time layers of a second time period, any two adjacent time layers of the multiple time layers of the first time period are separated by t1 time, and any two adjacent time layers of the multiple time layers of the second time period are separated by t2 time; wherein, both t2 time and t1 time are preset values, and t2 time is less than t1 time.

6. A vehicle trajectory planning device, characterized in that: The device comprises: A reference line acquisition module, used to search for reference lines based on a three-lane model; the reference lines include the center lines of the lane and the two lanes on the left, or the center lines of the lane and the two lanes on the right, or the center lines of the lane and the left and right adjacent lanes; A trajectory point prediction module, used to generate a plurality of predicted trajectory points of the vehicle and obstacles around the vehicle within a preset distance range based on the reference line; A time-space discretization module is used to bind the current position point of the vehicle and the multiple predicted trajectory points to multiple preset time layers respectively, with a certain time interval between two adjacent time layers; wherein the first time layer is bound to the current position point of the vehicle, and the other time layers are bound to the predicted trajectory points of the vehicle and obstacles; A connection edge generation module is used to connect the current position points or predicted trajectory points of the vehicle bound to any two adjacent time layers to obtain multiple connection edges, and any two points are connected to form a connection edge; The optimization solution module is used to calculate the cost of each connecting edge according to a preset cost function, and perform trajectory planning based on the cost of each connecting edge, preset constraints, and predicted trajectory points of obstacles bound to the multiple time layers to obtain a vehicle planning trajectory that satisfies the lane boundary constraints and has the lowest cost.

7. The device according to claim 6, characterized in that The predicted trajectory points include horizontal predicted trajectory points and vertical predicted trajectory points; The predicted lateral trajectory point of the vehicle is the predicted future trajectory point after the lateral movement of the vehicle, and the predicted longitudinal trajectory point of the vehicle is the predicted future trajectory point after the longitudinal movement of the vehicle; The lateral predicted trajectory point of the obstacle is the predicted future trajectory point after the obstacle moves lateraly, and the longitudinal predicted trajectory point of the obstacle is the predicted future trajectory point after the obstacle moves longitudinally.

8. The device according to claim 6, characterized in that The cost of each connecting edge includes at least one of an acceleration cost, an acceleration change rate cost, a reference line distance cost, a collision cost, and a lateral boundary cost; The acceleration cost is the cost corresponding to the acceleration required for the vehicle to move from one point on the connecting edge to another point; The acceleration change rate cost is the cost corresponding to the acceleration change rate required for the vehicle to move from one point on the connecting edge to another point; The reference line distance cost is the cost corresponding to the distance from one point on the connecting edge to another point on the lane where the vehicle is located; The collision cost is the cost corresponding to whether the vehicle collides with an obstacle when moving from one point on the connecting edge to another point; The lateral boundary cost is the cost corresponding to the distance between the vehicle and the left and right lane boundaries when the vehicle moves from one point on the connecting edge to another point.

9. The device according to claim 6, characterized in that The constraints include lane boundary constraints and obstacle linear boundary constraints; The lane boundary constraint means that the vehicle can cross or press the dashed line, but cannot cross or press the solid line; The constraint conditions include obstacle linear boundary constraints, which means that the obstacle boundary is represented by a straight line, one side of the straight line is considered a collision, and the other side of the straight line is considered safe and collision-free, and the vehicle needs to travel on the other side of the straight line.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the vehicle trajectory planning method according to any one of claims 1 to 5 is implemented.

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