CN113671941A

CN113671941A - Trajectory planning method, device, equipment and storage medium

Info

Publication number: CN113671941A
Application number: CN202010414249.2A
Authority: CN
Inventors: 李柏; 边学鹏
Original assignee: Beijing Jingdong Qianshi Technology Co Ltd
Current assignee: Beijing Jingdong Qianshi Technology Co Ltd
Priority date: 2020-05-15
Filing date: 2020-05-15
Publication date: 2021-11-19

Abstract

The embodiment of the invention discloses a track planning method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring initial track information obtained by performing track decision on a target vehicle; constructing a target trajectory planning nonlinear proposition corresponding to a target vehicle, wherein the target trajectory planning nonlinear proposition comprises the following steps: the method comprises the following steps of (1) a track cost function and driving constraint conditions, wherein the driving constraint conditions comprise: determining a collision avoidance constraint condition based on a tunneling modeling mode; and solving the nonlinear proposition of the target trajectory planning according to the initial trajectory information to determine the target planning trajectory corresponding to the target vehicle. By the technical scheme of the embodiment of the invention, the track planning efficiency can be improved.

Description

Trajectory planning method, device, equipment and storage medium

Technical Field

The embodiment of the invention relates to computer technology, in particular to a trajectory planning method, a trajectory planning device, trajectory planning equipment and a storage medium.

Background

With the rapid development of computer technology, vehicles can realize unmanned automatic driving in a structured road. In unmanned autonomous driving scenarios, it is often necessary to make trajectory decisions (i.e., running decisions) for the vehicle to decide which side the vehicle is to bypass an obstacle, to decide whether to preempt or yield, etc. After the vehicle is subjected to the trajectory decision, trajectory planning is needed to be carried out so as to carry out operations such as smoothing processing and the like on the decided rough driving trajectory to obtain a more accurate planned trajectory.

At present, the existing trajectory planning method plans according to the information of all obstacles in the driving scene to ensure that the vehicle does not collide with the obstacles when driving along the planned trajectory.

However, in the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art:

due to the fact that the length of the vehicle is limited, the vehicle and all obstacles are unlikely to collide at every moment, information of all the obstacles is considered at every moment in the existing track planning mode, a large amount of redundant calculation exists, and track planning efficiency is greatly reduced.

Disclosure of Invention

The embodiment of the invention provides a track planning method, a device, equipment and a storage medium, which are used for improving the track planning efficiency.

In a first aspect, an embodiment of the present invention provides a trajectory planning method, including:

acquiring initial track information obtained by performing track decision on a target vehicle;

constructing a target trajectory planning nonlinear proposition corresponding to the target vehicle, wherein the target trajectory planning nonlinear proposition comprises the following steps: a trajectory cost function and driving constraints, the driving constraints comprising: determining a collision avoidance constraint condition based on a tunneling modeling mode;

and solving the nonlinear proposition of the target trajectory planning according to the initial trajectory information to determine the target planning trajectory corresponding to the target vehicle.

In a second aspect, an embodiment of the present invention further provides a trajectory planning apparatus, including:

the initial track information acquisition module is used for acquiring initial track information obtained by carrying out track decision on a target vehicle;

the trajectory planning nonlinear proposition construction module is used for constructing a target trajectory planning nonlinear proposition corresponding to the target vehicle, wherein the target trajectory planning nonlinear proposition comprises the following steps: a trajectory cost function and driving constraints, the driving constraints comprising: determining a collision avoidance constraint condition based on a tunneling modeling mode;

and the target planning track determining module is used for solving the nonlinear proposition of the target track planning according to the initial track information to determine the target planning track corresponding to the target vehicle.

In a third aspect, an embodiment of the present invention further provides an apparatus, where the apparatus includes:

one or more processors;

a memory for storing one or more programs;

when executed by the one or more processors, cause the one or more processors to implement a trajectory planning method according to any embodiment of the invention.

In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the trajectory planning method according to any embodiment of the present invention.

The embodiment of the invention has the following advantages or beneficial effects:

the collision avoidance constraint condition is determined based on a tunneling modeling mode, namely, the drivable tunnel region of the target vehicle at each moment is obtained, so that obstacles which cannot influence the driving of the target vehicle do not need to be considered in the track planning process, the complexity of the collision avoidance constraint condition is reduced, simpler nonlinear propositions of the target track planning are constructed, the nonlinear propositions of the target track planning can be quickly solved in a mode of minimizing a track cost function, the target planning track corresponding to the target vehicle is obtained, the redundant calculation is avoided, and the track planning efficiency is improved.

Drawings

Fig. 1 is a flowchart of a trajectory planning method according to an embodiment of the present invention;

FIG. 2 is an example of a driving scenario in accordance with an embodiment of the present invention;

FIG. 3 is an example of a two-degree-of-freedom vehicle kinematics model in accordance with an embodiment of the present invention;

fig. 4 is an example of a local tunnel region in accordance with an embodiment of the present invention;

fig. 5 is a flowchart of a trajectory planning method according to a second embodiment of the present invention;

fig. 6 is an example of a process for constructing a local tunnel region according to a second embodiment of the present invention;

fig. 7 is a flowchart of a trajectory planning method according to a third embodiment of the present invention;

fig. 8 is a schematic structural diagram of a trajectory planning apparatus according to a fourth embodiment of the present invention;

fig. 9 is a schematic structural diagram of an apparatus according to a fifth embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

Example one

Fig. 1 is a flowchart of a trajectory planning method according to an embodiment of the present invention, which is applicable to a situation in which an autonomous vehicle performs trajectory planning, and is particularly applicable to a situation in which an autonomous vehicle in a structured road performs trajectory planning. The method may be performed by a trajectory planning device, which may be implemented in software and/or hardware, integrated in a device with autonomous driving functions, such as any type of vehicle. As shown in fig. 1, the method specifically includes the following steps:

and S110, obtaining initial track information obtained by making track decision on the target vehicle.

The target vehicle may refer to any type of autonomous vehicle in a road that requires trajectory planning. The link may refer to a structured link. The structured road may refer to a road having a driving rule on which the target vehicle is to be driven. For example, the structured road may be a road having a guide line to indicate a driving direction, such as a city road or the like. The trajectory decision may refer to a decision trajectory for deciding on which side the target vehicle travels on the road, that is, deciding on which side the target vehicle bypasses an obstacle when encountering the obstacle, deciding on preemption or yielding, and the like. The initial trajectory information may refer to trajectory information corresponding to a decision trajectory obtained after the trajectory decision. For example, the initial trajectory information may include, but is not limited to: and the determined running path information and running speed information of the target vehicle.

Specifically, the track decision of the target vehicle can be performed based on the information of each static obstacle and each dynamic obstacle in the structured road and the self information of the target vehicle, a reasonable and feasible decision track of the target vehicle running on the road is determined, and the track information of the decision track is used as the initial track information of the target vehicle in the track planning process.

S120, constructing a target track planning nonlinear proposition corresponding to the target vehicle, wherein the target track planning nonlinear proposition comprises the following steps: the method comprises the following steps of (1) a track cost function and driving constraint conditions, wherein the driving constraint conditions comprise: and determining a collision avoidance constraint condition based on a tunneling modeling mode.

The target trajectory planning NonLinear proposition may be a NonLinear planning (NLP) proposition facing the trajectory planning task. The nonlinear proposition of the target trajectory planning can be used as an Optimal Control proposition, so that the proposition solving can be carried out by utilizing a calculation Optimal Control (Computational Optimal Control) mode or a Numerical Optimal Control (Numerical Optimal Control) mode. The trajectory planning task may be to plan a driving trajectory meeting driving constraints between a starting time motion state and an ending time motion state of the target vehicle. The collision avoidance constraint condition may refer to a constraint condition for avoiding collision with an obstacle in a driving scene. Fig. 2 shows an example of a driving scenario. As shown in fig. 2, the driving scene may include a stationary obstacle and a moving obstacle and a scatter point for describing a road edge, and the collision avoidance constraint condition may be used to describe a condition that the target vehicle (i.e., vehicle i) does not overlap with all the obstacles and the road edge. The tunneling modeling method may be a method of laying a local tunnel in a time space based on the initial trajectory information to separate an actual travelable area of the target vehicle from an obstacle in the environment. The track cost function can be an optimized objective function of a target track planning nonlinear proposition and is used for screening an index formula of a high-quality track. Generally, there are multiple tracks that satisfy the driving constraint condition, so that an optimal track needs to be screened out as a final result based on a track cost function. Due to the existence of the driving constraint condition and the index formula for optimizing in the track planning task, the track planning proposition of the automatic driving vehicle can be described in a form of an optimal control problem.

Specifically, a drivable tunnel region of the target vehicle at each moment can be determined based on a tunneling modeling mode, so that a complex collision avoidance constraint condition directly established based on all obstacles in a road is converted into a simple boundary constraint form of collision avoidance constraint conditions, only the obstacles around the target vehicle need to be considered, the obstacles with longer distance do not need to be considered, the driving obstacles of the target vehicle cannot be influenced, and the complexity of the collision avoidance constraint condition is greatly reduced. The corresponding trajectory cost function can be determined based on the expectation that the trajectory is as smooth as possible and as close to the decision trajectory as possible. Based on a track cost function and a collision avoidance constraint condition, a simpler target track planning nonlinear proposition described by an optimal control problem mode can be constructed.

And S130, solving the nonlinear proposition of the target trajectory planning according to the initial trajectory information, and determining the target planning trajectory corresponding to the target vehicle.

Specifically, the initial trajectory information can be used as an initial solution to solve a target trajectory planning nonlinear proposition, that is, a trajectory cost function is minimized under the condition that a collision avoidance constraint condition determined based on a tunneling modeling mode is satisfied, so that an optimal solution can be obtained, that is, a target planning trajectory corresponding to a target vehicle is determined. The present embodiment may use an Interior Point algorithm (IPM) to solve the nonlinear proposition of target trajectory planning. According to the embodiment, the simpler nonlinear proposition of the target trajectory planning is solved, so that redundant calculation can be avoided, and the trajectory planning efficiency is improved.

According to the technical scheme, the collision avoidance constraint condition is determined based on a tunneling modeling mode, namely, the drivable tunnel region of the target vehicle at each moment is obtained, so that obstacles which cannot influence the driving of the target vehicle do not need to be considered in the track planning process, the complexity of the collision avoidance constraint condition is reduced, simpler nonlinear propositions of the target track planning are constructed, the nonlinear propositions of the target track planning can be rapidly solved in a mode of minimizing a track cost function, the target planning track corresponding to the target vehicle is obtained, redundant calculation is avoided, and the track planning efficiency is improved.

On the basis of the above technical solution, the driving constraint condition may further include: kinematic constraints of the target vehicle, boundary constraints, and vehicle internal mechanical constraints.

The kinematic constraint condition of the target vehicle may refer to a limitation of a motion capability of the target vehicle during driving. The boundary constraint may refer to a motion state specified by the start time and the end time of the target vehicle. The vehicle internal mechanical constraints may refer to limitations of motion capabilities inherent in the target vehicle.

Illustratively, a two degree of freedom model may be employed to describe vehicle motion. Fig. 3 shows an example of a two-degree-of-freedom vehicle kinematics model. As shown in fig. 3, the two-degree-of-freedom model may combine two front wheels and two rear wheels of the vehicle into a virtual single wheel respectively in the longitudinal axis direction of the vehicle body, and may indirectly determine the rotation angle, the running speed, and the like of the front wheels of the vehicle by determining the rotation angular velocity of the virtual front wheels and the linear acceleration variable of the virtual rear wheels, thereby implementing the vehicle motion.

Based on fig. 3, the kinematic constraints of the target vehicle may be expressed as:

wherein the driving time t belongs to [0, t ]_f]，t_fRepresents a termination time, which may be constant or variable; (x)_i(t),y_i(t)) represents the rear axle midpoint coordinates of vehicle i in a cartesian coordinate system XOY (i.e. a rectangular coordinate system); v. of_i(t) and a_i(t) represents the speed and acceleration in the longitudinal axis direction of the vehicle body, respectively, such that the direction in which the vehicle is advanced is the positive direction; phi is a_i(t) represents a vehicle front wheel deflection angle, with a left turn direction as a positive direction; omega_i(t) represents the front wheel yaw angular velocity, with the direction perpendicular to the XOY coordinate system outward as the positive direction; theta_i(t) represents the attitude angle of the vehicle in the XOY coordinate system, i.e., the rotation angle from the positive X-axis direction of the coordinate system to the positive longitudinal axis direction of the vehicle body, with the counterclockwise turning being the positive direction. Wherein x is_i(t)、y_i(t)、φ_i(t)、θ_i(t) and v_i(t) belonging to the state variables x (t), a_i(t) and ω_i(t) belongs to the control variable u (t). If the motion states x (0) and [0, t) of the vehicle i at the initial moment are obtained_f]And u (t) in the time domain, determining the motion states x (t) in the time domain one by one through integration, and obtaining a certain specific motion track of the vehicle i.

Since t is t_fThe time is not macroscopically the end point of the whole driving process, so that t can be adjusted_fSet as the trajectory decision time-domain length t_decisionIs a predetermined ratio of, i.e. t_f＝t_decision·γ_rateWherein, 0 < gamma_rate< 1 represents a preset scaling factor.

Based on fig. 3, the starting point constraint in the edge value constraint can be expressed as:

[v_i(0),φ_i(0),x_i(0),y_i(0),θ_i(0)]＝[v_0i,p_0i,x_0i,y_0i,θ_0i] (2)

wherein, [ v ]_0i,φ_0i,x_0i,y_0i,θ_0i]May be motion state information collected by onboard sensors. End time t_fNot macroscopically onThe end point of the process is determined, so that the hard limit on the end of the local planning time domain can be avoided.

Based on fig. 3, the vehicle interior mechanical constraints may be expressed as:

|φ_i(t)|≤Φ_max,|a_i(t)|≤a_max,|v_i(t)|≤v_max,|ω_i(t)|≤Ω_max,t∈[0,t_f]. (3)

wherein phi_max、a_max、v_maxAnd Ω_maxThe maximum amplitude of each state variable and control variable respectively. Phi_maxRepresenting the front wheel angle phi of the vehicle_i(t) a maximum allowable deflection angle value; v. of_maxIs the upper limit of safe driving speed of the vehicle in a low-speed scene; to ensure passenger comfort, a_maxAnd omega_maxThe maximum magnitudes of linear acceleration and front wheel turning angular velocity, respectively. For example, if it is desired that the acceleration variable change is gradual, the differential variable jerk of the acceleration may be supplemented_i(t)＝da_i(t)/dt and clipping it.

Based on fig. 3, the trajectory cost function of the target vehicle i can be represented as:

wherein (x)_decision(t),y_decision(t),θ_decision(t)) represents the decision trajectory of the target vehicle i.

Specifically, the collision avoidance constraint condition, the kinematic constraint condition of the target vehicle, the side value constraint condition and the mechanical constraint condition in the vehicle, which are determined based on the tunneling modeling mode, are all used as driving constraint conditions, and the track cost function is minimized, so that a more accurate target planning track can be obtained, and the track planning accuracy is further improved.

It should be noted that the present embodiment is a trajectory planning performed in a cartesian coordinate system XOY that does not depend on the guiding line, so that the trajectory planning task on the road may be completely independent of the guiding line.

On the basis of the technical scheme, determining a collision avoidance constraint condition based on a tunneling modeling mode can include: covering the vehicle body area of the target vehicle with two circular areas with preset radiuses, and determining the motion tracks of two circle centers; determining a local tunnel region corresponding to each circle center at each sampling moment in a track planning time domain according to the motion tracks of the two circle centers and a preset radius; and determining collision avoidance constraint conditions according to the local tunnel region.

Wherein the preset radius may be set based on a size of the target vehicle. For example, the radius of two circumscribed circles of the target vehicle may be set as the preset radius. As shown in fig. 3, the wheel base L may be based on the front and rear wheel base of the vehicle_wFront overhang distance L of vehicle_fRear overhang distance L of vehicle_rAnd a vehicle width L_bAnd determining a preset radius. For example, the preset radius corresponding to the target vehicle i is:

the local tunnel region corresponding to each circle center may refer to a travelable region of the target vehicle.

Specifically, the centers of two circumscribed circles of the target vehicle can be used for representing the target vehicle, and a corresponding local tunnel region is constructed for each center of the circle, so that tunneling modeling of the target vehicle can be realized. For example, two preset radii R may be utilized_iUniformly covers the rectangular body area of the target vehicle. Initial track information (x) corresponding to the decision track_decision(t),y_decision(t),θ_decision(t)) and the corresponding relation between the coordinates of the circle center position and the coordinates of the midpoint of the rear wheel shaft of the vehicle position, the motion tracks of two circle centers can be determined and respectively recorded as: p_r(xr (t), yr (t)) and P_f(xf (t), yf (t)). The corresponding relation between the center position coordinate and the midpoint coordinate of the rear wheel axle of the vehicle position can be expressed as follows:

it can be seen that the constraint that the vehicle does not collide with an obstacle can be translated into: p_rAnd P_fAre all at least at a distance R from the obstacle_iSo that P can be restricted_rAnd P_fSo that P is in a range of values_rAnd P_fAre all at least at a distance R from the obstacle_iAnd further simpler collision avoidance constraint conditions are obtained. For example, the accuracy parameter N can be based on discretization in numerical optimization_fePlanning the time domain [0, t ] for the trajectory_f]Carry out uniform sampling (N)_fe+1) moments, among them_kAt a time t_k＝t_f·(k-1)/N_fe. For each sampling moment, a local tunnel region corresponding to each circle center can be determined, and then P can be obtained based on the local tunnel regions_rAnd P_fThe value range of (1) is the collision avoidance constraint condition. According to the embodiment, the target vehicle is represented by using the two circle centers as mass points, and the corresponding local tunnel region is constructed for each circle center, so that the tunneling modeling process of the target vehicle can be realized more quickly, simpler collision avoidance constraint conditions are obtained, and the track planning efficiency is further improved.

Exemplarily, determining a collision avoidance constraint condition according to the local tunnel region may include: determining position coordinates of four region vertexes of a local tunnel region in a Cartesian coordinate system; and determining the constraint range of the position coordinate of each circle center at each sampling time according to the position coordinates of the vertexes of the four regions, and obtaining collision avoidance constraint conditions.

The local tunnel region may be a rectangular region. The region vertices may refer to the four vertices of a rectangular region. For example, fig. 4 shows an example of a local tunnel region. FIG. 4 shows the center P_rMotion locus P of_r(t) and at sampling time t_kCenter of a circle P of time_rPosition P of_r(t_k). In fig. 4, the small rectangular area represents the sampling time t_kTime center P_rCorresponding local tunnel regions. Large rectangular areaThe boundary represents the criticality of the target vehicle colliding with the obstacle. The distance between the two rectangular areas in fig. 4 may be a preset radius R_iTo ensure P_rAt least a distance R from the obstacle_i。

Specifically, the position coordinates of four region vertices of the local tunnel region may be obtained in the cartesian coordinate system XOY in fig. 3, and the position coordinates of the corresponding circle center may be limited based on the position coordinates of the four region vertices, so as to ensure that the position coordinates of the corresponding circle center are located in the local tunnel region, thereby obtaining the collision avoidance constraint condition. For example, as in FIG. 4, the center P of the circle_rAt sampling time t, (xr (t), yr (t)), (r) (t)_kThe constraint range of the center position coordinates is as follows: x is the number of_{r_lb_k}≤xr(t_k)≤x_{r_ub_k}，y_{r_lb_k}≤yr(t_k)≤y_{r_ub_k}. Similarly, it can also be based on the circle center P_fCorresponding local tunnel region, determining center of circle P_fThe position coordinates of (2). The collision avoidance constraint condition constructed using simple boundaries in the present embodiment can be expressed as:

it should be noted that, no matter how many obstacles exist in the driving scene, only 2 (N) exists_fe+1) set of constraints in the form of (6), so that the stability of the propositional solution can be set.

Illustratively, the target trajectory planning non-linear proposition P_NLP0Can be expressed in its entirety as:

and (3) minimizing: trajectory cost function (4)

s.t. kinematic constraint (1)

Boundary value constraint condition (2)

Vehicle interior mechanical constraints (3)

Relation of circle center (5)

Collision avoidance constraint condition (6)

In particular, the non-linear life is planned by solving the above-mentioned target trajectoryQuestion P_NLP0On the premise of meeting all constraint conditions, the track cost function value is minimum, so that a more accurate target planning track can be obtained, and the track planning efficiency and the track planning accuracy are improved.

Example two

Fig. 5 is a track planning method according to a second embodiment of the present invention, and in this embodiment, based on the foregoing embodiments, optimization is performed on "determining a local tunnel region corresponding to each circle center at each sampling time in a track planning time domain according to a motion track of two circle centers and a preset radius". Wherein explanations of the same or corresponding terms as those of the above embodiments are omitted.

Referring to fig. 5, the trajectory planning method provided in this embodiment specifically includes the following steps:

s210, obtaining initial track information obtained by making track decision on the target vehicle.

And S220, covering the two circular areas with preset radiuses on the body area of the target vehicle, and determining the motion tracks of the two circle centers.

In particular, it may be based on initial trajectory information (x)_decision(t),y_decision(t),θ_decision(t)) and the corresponding relation between the coordinates of the circle center position and the coordinates of the midpoint of the rear wheel shaft of the vehicle position, the motion tracks of two circle centers can be determined and respectively recorded as: p_r(xr (t), yr (t)) and P_f＝(xf(t),yf(t))。

And S230, determining the circle center positions of the two circle centers at each sampling time in the planning time domain according to the motion tracks of the two circle centers.

Specifically, for each circle center, the position of the circle center of the circle at each sampling moment can be determined based on the motion track of the circle center. E.g. based on the centre of a circle P_rMotion locus P of_r(t), at the sampling time t can be determined_kCenter of a circle P of time_rHas a center position of P_r(t_k)。

And S240, based on the preset radius, performing incremental expansion by taking the circle center position of each circle center as a center, and determining a local tunnel region corresponding to each circle center at each sampling moment.

Specifically, a region range with a preset radius can be obtained by taking the position of the center of a circle at each sampling moment as a center, and a local tunnel region of each center of a circle at each sampling moment can be determined by performing incremental expansion around the region range. According to the embodiment, each circle center position can be incrementally expanded, an accurate local tunnel region can be obtained, the accuracy of determining collision avoidance constraint conditions is improved, and the accuracy of trajectory planning is further improved.

Exemplarily, S240 may include: respectively expanding the distance with the length being the preset radius along the positive and negative directions of an X axis and a Y axis by taking the circle center position of the current circle center at the current sampling moment as the center, and constructing a quadrangle taking the circle center position as the center; gradually expanding outwards along the four expansion edges of the quadrangle for a preset distance to obtain an expansion area expanded each time; if the fact that the expansion area does not have an overlapping portion with the obstacle area in the driving scene is detected, updating the quadrangle based on the expansion area, and continuing expansion operation on the updated quadrangle; if the expanded area is detected to be overlapped with the obstacle area in the driving scene, stopping the expansion operation of the expanded side corresponding to the expanded area, and continuing the expansion operation of other expanded sides; and when the expansion operations of the four expansion edges are all stopped, determining a local tunnel region corresponding to the current circle center at the current sampling moment according to the current quadrangle.

Any sampling moment can be used as the current sampling moment, and any circle center can be used as the current circle center, so that the local tunnel region of each circle center at each sampling moment can be determined by utilizing the determination mode of the local tunnel region.

Specifically, fig. 6 shows an example of a process for constructing a local tunnel region. As shown in a) of fig. 6, the current sampling time t may be_kLower current circle center P_rCenter position P of_r(t_k) As the center, respectively extending the length to a preset radius R along the positive and negative directions of the X axis and the Y axis in the XOY coordinate system_iIs constructed as a circle center position P_r(t_k) A quadrilateral with a center, i.e., a rectangle. As shown in b) of fig. 6, the expanded area of each expansion, such as the area identified by the serial number in b), can be obtained by expanding the four expanded edges of the quadrangle outward by a preset distance Δ step in a clockwise direction or a counterclockwise direction, respectively. The order of the sequence numbers can be used to characterize the expansion order. After each expansion, it may be detected whether the expanded area overlaps with an obstacle area in the driving scene, for example, a moving obstacle in the driving scene may be set at t_kThe position of the moment, the position of the static obstacle and the scattered point position of the road edge are stored in the set V, so that whether the overlapped part exists can be determined by detecting whether the element position contained in the expansion area exists in the set V. If not, the target vehicle does not collide with the obstacle when running in the expansion area, namely the expansion area is an effective area, at this time, the expansion area can be combined into the quadrangle to update the four-side rows, and the expansion operation is continued on the updated quadrangle. If the expansion area and the obstacle area in the driving scene have an overlapping part, it indicates that the target vehicle may collide with the obstacle when driving in the expansion area, that is, the expansion area is an invalid area, and at this time, the expansion operation of the expansion side corresponding to the expansion area is stopped and the expansion operation of the other expansion sides is continued without merging the expansion area. If the expansion operation of the four expansion edges is detected to stop, the current quadrangle obtained at the current moment is the current circle center P_rThe maximum area that can be moved, as shown in fig. c). In order to ensure that the target vehicle does not collide with the obstacle, the current quadrilateral area corresponding to the current circle center can be reduced to obtain a local tunnel area corresponding to the current circle center at the current sampling moment.

Exemplarily, determining a local tunnel region corresponding to a current circle center at a current sampling time according to a current quadrangle may include: shrinking four sides of the current quadrangle to the center by a preset radius, and determining the shrunk quadrangle area as a local tunnel corresponding to the current circle center at the current sampling momentA street region. As shown in d) of fig. 6, the length is reduced to the preset radius R by shrinking all four sides of the current quadrangle to the center_iThe distance of the distance between the center of each circle and the barrier is at least R, namely a smaller quadrangle is obtained, at the moment, the area of the contracted quadrangle can be determined as a local tunnel area corresponding to the current center of the circle at the current sampling moment, and therefore the distance between each center of the circle and the barrier can be ensured to be at least R_iThe collision of the target vehicle with the obstacle is avoided.

And S250, determining collision avoidance constraint conditions according to the local tunnel region.

S260, constructing a target track planning nonlinear proposition corresponding to the target vehicle, wherein the target track planning nonlinear proposition comprises the following steps: the method comprises the following steps of (1) a track cost function and driving constraint conditions, wherein the driving constraint conditions comprise: and determining a collision avoidance constraint condition based on a tunneling modeling mode.

And S270, solving the nonlinear proposition of the target trajectory planning according to the initial trajectory information, and determining the target planning trajectory corresponding to the target vehicle.

According to the technical scheme, the position of each circle center at each sampling moment is incrementally expanded, so that a local tunnel region can be accurately obtained, the accuracy of determining the collision avoidance constraint condition can be improved, and the accuracy of trajectory planning is improved.

EXAMPLE III

Fig. 7 is a track planning method provided in the third embodiment of the present invention, and in this embodiment, based on the above embodiments, optimization is performed on "constructing a target track planning nonlinear proposition corresponding to a target vehicle", and based on this, optimization is performed on "solving the target track planning nonlinear proposition according to initial track information, and determining a target planning track corresponding to the target vehicle". Wherein explanations of the same or corresponding terms as those of the above embodiments are omitted.

Referring to fig. 7, the trajectory planning method provided in this embodiment specifically includes the following steps:

s310, obtaining initial track information obtained by making track decision on the target vehicle.

S320, constructing a first track planning nonlinear proposition corresponding to the target vehicle, wherein the first track planning nonlinear proposition comprises the following steps: the target function is determined based on a track cost function, a kinematic constraint condition of a target vehicle and a circle center incidence relation of two circle centers corresponding to a collision avoidance constraint condition determined based on a tunneling modeling mode.

The boundary constraint condition may refer to a condition that is limited by a boundary form. For example, the boundary constraints may include collision avoidance constraints, side value constraints, and vehicle interior mechanical constraints determined based on a tunneling modeling approach. The objective function may refer to a first trajectory planning non-linear proposition P_NLP1The optimization objective of (1).

In particular, nonlinear proposition P due to target trajectory planning_NLP0Two circle centers P in_rAnd P_fAnd an optimization variable x_i、y_iAnd theta_iThe nonlinear relationship (see expression (5)) is formed so that the collision avoidance constraint condition (see expression (6)) becomes a non-convex constraint. It can be seen that the target trajectory planning non-linear proposition P_NLP0The non-convex non-linear constraint condition in (1) may include a kinematic constraint condition of the target vehicle (see equation (1)) and a circle center association relationship of two circle centers (see equation (5)), thereby causing the target trajectory planning non-linear proposition P_NLP0The solution efficiency of (2) is low. In this regard, xr (t), yr (t), xf (t), and yf (t) may also be optimized as optimization variables, i.e. the first trajectory planning nonlinear proposition P is constructed_NLP1The optimization variables of the objective function in (1) include: xr (t), yr (t), xf (t), yf (t), x_i、y_iAnd theta_iThereby enabling the first trajectory plan to have a non-linear proposition P_NLP1The constraints in (2) are all linear boundary constraints.

Exemplarily, determining the target function based on the track cost function, the kinematic constraint condition of the target vehicle, and the circle center incidence relation between two circle centers corresponding to the collision avoidance constraint condition determined based on the tunneling modeling manner may include: performing polynomial characterization on the kinematic constraint condition of the target vehicle and the circle center incidence relation of two circle centers corresponding to the collision avoidance constraint condition determined based on the tunneling modeling mode, and determining each punishment item; and determining an objective function according to each penalty term and the track cost function.

For example, the kinematic constraint condition (see formula (1)) of the target vehicle and the circle center association relationship (see formula (5)) of two circle centers may be transformed into a penalty polynomial to obtain corresponding penalty terms, and the track cost function and the penalty terms are added to obtain the target function. For example, the objective function may be expressed as follows:

illustratively, the first trajectory plan nonlinear proposition P_NLP1Can be expressed in its entirety as:

and (3) minimizing: objective function (7)

s.t. boundary value constraint condition (2)

Vehicle interior mechanical constraints (3)

Collision avoidance constraint condition (6)

And S330, solving the nonlinear proposition of the first trajectory planning according to the initial trajectory information, and determining a first planning trajectory corresponding to the target vehicle.

Specifically, a nonlinear proposition P is planned for the first trajectory by taking the initial trajectory information as an initial solution_NLP1And solving to obtain a first planned track corresponding to the target vehicle. At P_NLP1And temporarily neglecting the track cost function (4), and focusing on obtaining a first planning track which is more consistent with kinematics on the premise of ensuring no collision. Due to the nonlinear proposition P of the first trajectory plan_NLP1The constraints are all simple boundary constraints, so that P_NLP1Certain solutions exist, so that IPM is utilized to solve P_NLP1Has a solving speed higher than P_NLP0The solution speed of (2).

It should be noted that the execution sequence of S330 is not limited in this embodiment, and step S330 may be executed after step S320, or after step S340.

S340, constructing a second trajectory planning nonlinear proposition corresponding to the target vehicle, wherein the second trajectory planning nonlinear proposition comprises the following steps: trajectory cost function and driving constraints.

Specifically, the second trajectory plan nonlinear proposition P_NLP2Non-linear proposition P with target trajectory planning_NLP0The optimization targets are consistent and are all track cost functions. The driving constraint condition may include a collision avoidance constraint condition determined based on a tunneling modeling manner, and may further include: kinematic constraints of the target vehicle, boundary constraints, and vehicle internal mechanical constraints.

Illustratively, the second trajectory plan non-linear proposition P_NLP2Can be expressed in its entirety as:

and (3) minimizing: trajectory cost function (4)

s.t. kinematic constraint (1)

Boundary value constraint condition (2)

Vehicle interior mechanical constraints (3)

Relation of circle center (5)

Collision avoidance constraint condition (6)

Exemplarily, the nonlinear proposition P of the second trajectory planning can be redetermined according to the trajectory information of the first planning trajectory and the association relationship between the circle centers of the two circle centers (see equation (5)) based on the tunneling modeling method provided by the above embodiments_NLP2And (5) avoiding the constraint condition (6) so as to further improve the accuracy of path planning.

And S350, solving the nonlinear proposition of the second trajectory planning according to the first planning trajectory, and determining a target planning trajectory corresponding to the target vehicle.

Specifically, a nonlinear proposition P is planned for the second track by taking the first track information as an initial solution_NLP2And solving to obtain a target planning track corresponding to the target vehicle. P_NLP2On the premise of ensuring the kinematic feasibility, the method focuses on minimizing the track cost function (5), so that the target planning track can be solved more quickly. Due to the objectThe track planning nonlinear proposition has three tasks which need to be completed simultaneously, namely rigid vehicle kinematics constraint, rigid collision avoidance constraint and track cost function optimization, so that the solving difficulty of the target track planning nonlinear proposition is higher. Through disassembling the target trajectory planning nonlinear proposition into a first trajectory planning nonlinear proposition and a second trajectory planning nonlinear proposition, the solving difficulty can be reduced, and the trajectory planning efficiency is further improved.

According to the technical scheme, the first track planning nonlinear proposition and the second track planning nonlinear proposition are solved step by step, so that the solving difficulty of a track planning task can be reduced, and the track planning efficiency is further improved.

The following is an embodiment of the trajectory planning apparatus provided in the embodiments of the present invention, and the apparatus and the trajectory planning method in the embodiments belong to the same inventive concept, and details that are not described in detail in the embodiment of the trajectory planning apparatus may refer to the embodiment of the trajectory planning method.

Example four

Fig. 8 is a schematic structural diagram of a trajectory planning device according to a fourth embodiment of the present invention, which is applicable to a situation of performing trajectory planning on an autonomous vehicle, and is particularly applicable to a situation of performing trajectory planning on an autonomous vehicle in a structured road. As shown in fig. 8, the apparatus specifically includes: an initial trajectory information acquisition module 410, a trajectory planning nonlinear proposition construction module 420 and a target planning trajectory determination module 430.

The initial track information obtaining module 410 is configured to obtain initial track information obtained by performing track decision on a target vehicle; the trajectory planning nonlinear proposition construction module 420 is configured to construct a target trajectory planning nonlinear proposition corresponding to the target vehicle, where the target trajectory planning nonlinear proposition includes: the method comprises the following steps of (1) a track cost function and driving constraint conditions, wherein the driving constraint conditions comprise: determining a collision avoidance constraint condition based on a tunneling modeling mode; and the target planning track determining module 430 is configured to solve the nonlinear proposition of the target track planning according to the initial track information, and determine a target planning track corresponding to the target vehicle.

Optionally, the apparatus further comprises: a collision avoidance constraint determining module comprising:

the motion track determining submodule is used for covering the vehicle body area of the target vehicle with two circular areas with preset radiuses and determining the motion tracks of two circle centers;

the local tunnel region determining submodule is used for determining a local tunnel region corresponding to each circle center at each sampling moment in the track planning time domain according to the motion tracks of the two circle centers and the preset radius;

and the collision avoidance constraint condition determining submodule is used for determining the collision avoidance constraint condition according to the local tunnel region.

Optionally, the local tunnel region determining sub-module includes:

the circle center position determining unit is used for determining the circle center positions of the two circle centers at each sampling time in the planning time domain according to the motion tracks of the two circle centers;

and the local tunnel region determining unit is used for performing incremental expansion by taking the circle center position of each circle center as a center based on a preset radius, and determining the local tunnel region corresponding to each circle center at each sampling moment.

Optionally, the local tunnel region determining unit is specifically configured to:

respectively expanding the distance with the length being the preset radius along the positive and negative directions of an X axis and a Y axis by taking the circle center position of the current circle center at the current sampling moment as the center, and constructing a quadrangle taking the circle center position as the center; gradually expanding outwards along the four expansion edges of the quadrangle for a preset distance to obtain an expansion area expanded each time; if the fact that the expansion area does not have an overlapping portion with the obstacle area in the driving scene is detected, updating the quadrangle based on the expansion area, and continuing expansion operation on the updated quadrangle; if the expanded area is detected to be overlapped with the obstacle area in the driving scene, stopping the expansion operation of the expanded side corresponding to the expanded area, and continuing the expansion operation of other expanded sides; and when the expansion operations of the four expansion edges are all stopped, determining a local tunnel region corresponding to the current circle center at the current sampling moment according to the current quadrangle.

Optionally, the local tunnel region determining unit is further specifically configured to: and shrinking the four sides of the current quadrangle to the center by a distance of a preset radius, and determining the area of the shrunk quadrangle as a local tunnel area corresponding to the current circle center at the current sampling moment.

Optionally, the collision avoidance constraint determining submodule is specifically configured to:

determining position coordinates of four region vertexes of a local tunnel region in a Cartesian coordinate system; and determining the constraint range of the position coordinate of each circle center at each sampling time according to the position coordinates of the vertexes of the four regions, and obtaining collision avoidance constraint conditions.

Optionally, the driving constraints further comprise: kinematic constraints of the target vehicle, boundary constraints, and vehicle internal mechanical constraints.

Optionally, the trajectory planning nonlinear proposition construction module 420 is specifically configured to: constructing a first trajectory planning nonlinear proposition corresponding to the target vehicle, wherein the first trajectory planning nonlinear proposition comprises the following steps: the target function is determined based on a track cost function, a kinematic constraint condition of a target vehicle and a circle center incidence relation of two circle centers corresponding to a collision avoidance constraint condition determined based on a tunneling modeling mode; constructing a second trajectory planning nonlinear proposition corresponding to the target vehicle, wherein the second trajectory planning nonlinear proposition comprises the following steps: a trajectory cost function and a driving constraint condition;

accordingly, the target planning trajectory determination module 430 is specifically configured to: solving the nonlinear proposition of the first trajectory planning according to the initial trajectory information to determine a first planning trajectory corresponding to the target vehicle; and solving the nonlinear proposition of the second trajectory planning according to the first planning trajectory to determine a target planning trajectory corresponding to the target vehicle.

Optionally, the apparatus further comprises an objective function determining module, configured to: performing polynomial characterization on the kinematic constraint condition of the target vehicle and the circle center incidence relation of two circle centers corresponding to the collision avoidance constraint condition determined based on the tunneling modeling mode, and determining each punishment item; and determining an objective function according to each penalty term and the track cost function.

The trajectory planning device provided by the embodiment of the invention can execute the trajectory planning method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the trajectory planning method.

It should be noted that, in the embodiment of the trajectory planning apparatus, the included units and modules are only divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.

EXAMPLE five

Fig. 9 is a schematic structural diagram of an apparatus according to a fifth embodiment of the present invention. FIG. 9 illustrates a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 9 is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present invention.

As shown in FIG. 9, device 12 is in the form of a general purpose computing device. The components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 9, and commonly referred to as a "hard drive"). Although not shown in FIG. 9, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.

Device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with device 12, and/or with any devices (e.g., network card, modem, etc.) that enable device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 20. As shown, the network adapter 20 communicates with the other modules of the device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement a trajectory planning method provided by the embodiment of the present invention, the method includes:

constructing a target trajectory planning nonlinear proposition corresponding to a target vehicle, wherein the target trajectory planning nonlinear proposition comprises the following steps: the method comprises the following steps of (1) a track cost function and driving constraint conditions, wherein the driving constraint conditions comprise: determining a collision avoidance constraint condition based on a tunneling modeling mode;

Of course, those skilled in the art can understand that the processor can also implement the technical solution of the trajectory planning method provided by any embodiment of the present invention.

EXAMPLE five

The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a trajectory planning method according to any of the embodiments of the present invention, the method comprising:

Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer-readable storage medium may be, for example but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It will be understood by those skilled in the art that the modules or steps of the invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and optionally they may be implemented by program code executable by a computing device, such that it may be stored in a memory device and executed by a computing device, or it may be separately fabricated into various integrated circuit modules, or it may be fabricated by fabricating a plurality of modules or steps thereof into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A trajectory planning method, comprising:

2. The method of claim 1, wherein determining collision avoidance constraints based on a tunneling modeling approach comprises:

covering two circular areas with preset radiuses on a vehicle body area of the target vehicle, and determining the motion tracks of two circle centers;

determining a local tunnel region corresponding to each circle center at each sampling moment in a track planning time domain according to the motion tracks of the two circle centers and the preset radius;

and determining collision avoidance constraint conditions according to the local tunnel region.

3. The method of claim 2, wherein determining the local tunnel region corresponding to each circle center at each sampling time in the trajectory planning time domain according to the motion trajectories of the two circle centers and the preset radius comprises:

determining the circle center positions of the two circle centers at each sampling time in a planning time domain according to the motion tracks of the two circle centers;

based on the preset radius, incremental expansion is carried out by taking the circle center position of each circle center as a center, and a local tunnel region corresponding to each circle center at each sampling moment is determined.

4. The method according to claim 3, wherein based on the preset radius, incremental expansion is performed with the center position of each circle center as a center, and the determining of the local tunnel region corresponding to each circle center at each sampling time includes:

respectively expanding distances with lengths of preset radiuses in positive and negative directions of an X axis and a Y axis by taking the circle center position of the current circle center at the current sampling moment as the center, and constructing a quadrangle with the circle center position as the center;

gradually expanding outwards along the four expansion edges of the quadrangle for a preset distance to obtain an expansion area expanded each time;

if the fact that the expansion area does not have an overlapping portion with the obstacle area in the driving scene is detected, updating the quadrangle based on the expansion area, and continuing expansion operation on the updated quadrangle;

if the expanded area is detected to be overlapped with the obstacle area in the driving scene, stopping the expansion operation of the expanded side corresponding to the expanded area, and continuing the expansion operation of other expanded sides;

and when the expansion operations of the four expansion edges are all stopped, determining a local tunnel region corresponding to the current circle center at the current sampling moment according to the current quadrangle.

5. The method of claim 4, wherein determining the local tunnel region corresponding to the current center at the current sampling time according to the current quadrilateral comprises:

and shrinking the four sides of the current quadrangle to the center by a distance of a preset radius, and determining the area of the shrunk quadrangle as a local tunnel area corresponding to the current circle center at the current sampling moment.

6. The method of claim 2, wherein determining collision avoidance constraints from the local tunnel regions comprises:

determining position coordinates of four region vertexes of the local tunnel region in a Cartesian coordinate system;

and determining the constraint range of the position coordinates of each circle center at each sampling time according to the position coordinates of the vertexes of the four regions, and obtaining collision avoidance constraint conditions.

7. The method of claim 1, wherein the driving constraints further comprise: kinematic constraints of the target vehicle, boundary constraints, and vehicle internal mechanical constraints.

8. The method according to any one of claims 1-7, wherein constructing a target trajectory planning nonlinear proposition corresponding to the target vehicle comprises:

constructing a first trajectory planning nonlinear proposition corresponding to the target vehicle, wherein the first trajectory planning nonlinear proposition comprises: the target function is determined based on a track cost function, a kinematic constraint condition of a target vehicle and a circle center incidence relation of two circle centers corresponding to a collision avoidance constraint condition determined based on a tunneling modeling mode;

constructing a second trajectory planning nonlinear proposition corresponding to the target vehicle, wherein the second trajectory planning nonlinear proposition comprises: a trajectory cost function and a driving constraint condition;

correspondingly, solving the nonlinear proposition of the target trajectory planning according to the initial trajectory information to determine the target planning trajectory corresponding to the target vehicle, and the method comprises the following steps:

solving the first track planning nonlinear proposition according to the initial track information to determine a first planning track corresponding to the target vehicle;

and solving the nonlinear proposition of the second trajectory planning according to the first planning trajectory to determine a target planning trajectory corresponding to the target vehicle.

9. The method according to claim 8, wherein determining the target function based on the track cost function, the kinematic constraint condition of the target vehicle and the circle center incidence relation between two circle centers corresponding to the collision avoidance constraint condition determined based on the tunneling modeling manner comprises:

performing polynomial characterization on the kinematic constraint condition of the target vehicle and the circle center incidence relation of two circle centers corresponding to the collision avoidance constraint condition determined based on the tunneling modeling mode, and determining each punishment item;

and determining an objective function according to each penalty term and the track cost function.

10. A trajectory planning apparatus, comprising:

11. An apparatus, characterized in that the apparatus comprises:

one or more processors;

a memory for storing one or more programs;

when executed by the one or more processors, cause the one or more processors to implement the trajectory planning method of any one of claims 1-9.

12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a trajectory planning method according to any one of claims 1-9.