Path planning method and system, vehicle and storage medium

Abstract

The invention discloses a path planning method and system, a vehicle and a storage medium, and the method comprises the steps: obtaining vehicle driving data; according to the vehicle driving data and a preset working condition standard, vehicle working conditions are determined, and the vehicle working conditions comprise a conventional working condition and a limit working condition; determining a path planning strategy according to the vehicle working condition; and according to the path planning strategy, planning a vehicle driving path to obtain a path planning result. The technical problem that according to an existing path planning method, when a vehicle encounters a complex working condition, the path planning result cannot guarantee the stability and safety of the vehicle is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of path planning, and in particular, relates to a path planning method, system, vehicle and storage medium. Background Art

[0002] The operation of vehicles under extreme conditions is the main cause of traffic accidents. Therefore, the study of active safety control technology for vehicles under extreme conditions has great economic significance and social value for reducing the incidence and mortality of traffic accidents and protecting people's lives and property. Judging from the current implementation status of many research plans, the discussion on the path planning method of autonomous driving vehicles under the specific application background of extreme conditions is still incomplete.

[0003] The design of traditional chassis stability control systems takes into account the average driving level of human drivers, and the design principle is to control the vehicle state to maintain within the range that ordinary drivers can control. However, when unmanned driving encounters complex working conditions and emergencies, such as slippery roads in rain and snow, the tire adhesion reaches saturation, and the control execution of conventional path planning results cannot guarantee the stability and safety of the vehicle. Summary of the invention

[0004] The embodiments of the present invention provide a path planning method, system, vehicle and storage medium to at least solve the technical problem that the path planning results of the existing path planning method cannot guarantee the stability and safety of the vehicle when the vehicle encounters complex working conditions.

[0005] According to a first aspect of an embodiment of the present invention, a path planning method is provided, comprising: acquiring vehicle driving data; determining a vehicle operating condition based on the vehicle driving data and in accordance with a preset operating condition standard, wherein the vehicle operating condition includes a normal operating condition and an extreme operating condition; determining a path planning strategy based on the vehicle operating condition; and planning a vehicle driving path based on the path planning strategy to obtain a path planning result.

[0006] Optionally, the vehicle driving data includes driving path data, vehicle current state data, and vehicle current driving environment data.

[0007] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being a normal operating condition, adopting a global path planning algorithm as the path planning strategy, wherein the global path planning algorithm is a heuristic search algorithm.

[0008] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being a normal operating condition, a local path planning algorithm is used as the path planning strategy, wherein the local path planning algorithm is a quintic polynomial algorithm.

[0009] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being an extreme condition, determining a model prediction contour control algorithm as the path planning strategy.

[0010] Optionally, construction of a model prediction contour control algorithm includes: obtaining perception information of the vehicle; determining a reference trajectory based on the perception information and parameterizing the reference trajectory to obtain a drivable space for the vehicle; determining a contour error based on a vehicle dynamics model; and constructing boundary constraints and optimization problems for planning control.

[0011] Optionally, the vehicle dynamics model is a two-degree-of-freedom single-track model of the vehicle with nonlinear tire forces.

[0012] Optionally, according to the path planning strategy, the vehicle driving path is planned to obtain a path planning result, including: according to the path planning strategy, the path planning result is determined based on the vehicle state, road data small center of mass sideslip angle stability constraint, road boundary constraint, control amount and change rate constraint.

[0013] According to a second aspect of an embodiment of the present invention, a path planning system is also provided, including: an acquisition module for acquiring vehicle driving data; a first determination module for determining the vehicle operating condition based on the vehicle driving data and a preset operating condition standard, wherein the vehicle operating condition includes a normal operating condition and an extreme operating condition; a second determination module for determining a path planning strategy based on the vehicle operating condition; and a planning module for planning the vehicle driving path according to the path planning strategy to obtain a path planning result.

[0014] Optionally, the vehicle driving data acquired by the acquisition module includes driving path data, vehicle current state data, and vehicle current driving environment data.

[0015] Optionally, the second determination module is further used to: in response to the vehicle operating condition being a normal operating condition, adopt a global path planning algorithm as a path planning strategy, wherein the global path planning algorithm is a heuristic search algorithm.

[0016] Optionally, the second determination module is further used to: in response to the vehicle operating condition being a normal operating condition, adopt a local path planning algorithm as a path planning strategy, wherein the local path planning algorithm is a quintic polynomial algorithm.

[0017] Optionally, the second determination module is further used to: in response to the vehicle operating condition being an extreme operating condition, determine a model prediction profile control algorithm as a path planning strategy.

[0018] Optionally, the second determination module is also used to: obtain the perception information of the vehicle; determine the reference trajectory based on the perception information and parameterize the reference trajectory to obtain the vehicle's drivable space; determine the contour error based on the vehicle dynamics model; and construct boundary constraints and optimization problems for planning control.

[0019] Optionally, the vehicle dynamics model in the second determination module is a vehicle two-degree-of-freedom single-track model with nonlinear tire forces.

[0020] Optionally, the planning module is also used to determine the path planning result according to the path planning strategy, based on the vehicle state, road data small center of mass sideslip angle stability constraint, road boundary constraint, control amount and change rate constraint.

[0021] According to a third aspect of an embodiment of the present invention, a vehicle is also provided, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the path planning method described in any embodiment of the first aspect above.

[0022] According to a fourth aspect of an embodiment of the present invention, a non-volatile storage medium is further provided, in which a computer program is stored, wherein the computer program is configured to execute the path planning method described in any embodiment of the first aspect when running on a computer or a processor.

[0023] According to a fifth aspect of an embodiment of the present invention, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the path planning method in any of the above items is implemented.

[0024] In an embodiment of the present invention, vehicle driving data is obtained; based on the vehicle driving data and according to preset working condition standards, the vehicle working condition is determined, wherein the vehicle working condition includes normal working condition and extreme working condition; based on the vehicle working condition, a path planning strategy is determined; based on the path planning strategy, the vehicle driving path is planned to obtain a path planning result. The present invention can judge the vehicle working condition, and when the vehicle is in different working conditions, different path planning strategies are adopted to perform path planning, and a driving path adapted to different vehicle working conditions can be generated, giving full play to the movement potential of unmanned vehicles, and on the basis of combining vehicle information and environmental information, the stability and safety of unmanned driving in complex working conditions and emergencies can be enhanced, thereby solving the technical problem that the existing path planning method cannot guarantee the stability and safety of the vehicle when the vehicle encounters complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 is a flow chart of a path planning method according to one embodiment of the present invention;

[0027] Figure 2 A single-track vehicle dynamics model provided according to one embodiment of the present invention;

[0028] Figure 3 is a schematic diagram of a vehicle profile error and its linear approximation according to one embodiment of the present invention;

[0029] Figure 4 is a schematic diagram of a first road boundary constraint according to one embodiment of the present invention;

[0030] Figure 5 is a schematic diagram of a second road boundary constraint according to one embodiment of the present invention;

[0031] Figure 6 is a schematic diagram of the dimensions of a simulation scene according to one embodiment of the present invention;

[0032] Figure 7 is a schematic diagram of a centroid trajectory at high weight according to one embodiment of the present invention;

[0033] Figure 8 is a schematic diagram of a centroid trajectory at low weight according to one embodiment of the present invention;

[0034] Fig. 9 Schematic diagram of the structure of a path planning system according to one embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] According to an embodiment of the present invention, an embodiment of a path planning method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system containing at least one set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] The method embodiment can also be executed in an electronic device including a memory and a processor, a similar control device or a cloud. Taking an electronic device as an example, the electronic device may include one or more processors and a memory for storing data. Optionally, the electronic device may also include a communication device and a display device for communication functions. It can be understood by those of ordinary skill in the art that the above structural description is only illustrative and does not limit the structure of the above electronic device. For example, the electronic device may also include more or fewer components than the above structural description, or have a configuration different from the above structural description.

[0039] The processor may include one or more processing units. For example, the processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a field-programmable gate array (FPGA), a neural network processor (NPU), a tensor processing unit (TPU), an artificial intelligence (AI) type processor, and other processing devices. Among them, different processing units may be independent components or integrated into one or more processors. In some instances, the electronic device may also include one or more processors.

[0040] The memory can be used to store computer programs, such as storing the computer program corresponding to the path planning method in the embodiment of the present invention, and the processor implements the above-mentioned path planning method by running the computer program stored in the memory. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0041] The communication device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the communication device includes a network adapter (network interface controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the communication device can be a radio frequency (radio frequency, RF) module, which is used to communicate with the Internet wirelessly. In some embodiments of the present solution, the communication device is used to connect to mobile devices such as mobile phones and tablets, and instructions can be sent to electronic devices through the mobile device.

[0042] The display device may be a touch screen type liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of the electronic device. In some embodiments, the electronic device has a graphical user interface (GUI), and a user may interact with the GUI by finger contacts and / or gestures on a touch sensitive surface, and executable instructions for executing the above human-computer interaction functions are configured / stored in a computer program product or a readable storage medium executable by one or more processors.

[0043] Figure 1 is a flow chart of a path planning method according to one embodiment of the present invention, the method is applied to a hybrid vehicle, such as Figure 1 As shown, the method comprises the following steps:

[0044] Step S101, obtaining vehicle driving data.

[0045] Step S102, determining the vehicle operating condition according to the vehicle driving data and the preset operating condition standard, wherein the vehicle operating condition includes a normal operating condition and an extreme operating condition.

[0046] Step S103, determining a path planning strategy according to the vehicle operating conditions.

[0047] Step S104, planning the vehicle driving path according to the path planning strategy to obtain a path planning result.

[0048] In an embodiment of the present invention, vehicle driving data is obtained; based on the vehicle driving data and according to preset working condition standards, the vehicle working condition is determined, wherein the vehicle working condition includes normal working condition and extreme working condition; based on the vehicle working condition, a path planning strategy is determined; based on the path planning strategy, the vehicle driving path is planned to obtain a path planning result. The present invention can judge the vehicle working condition, and when the vehicle is in different working conditions, different path planning strategies are adopted to perform path planning, and a driving path adapted to different vehicle working conditions can be generated, giving full play to the movement potential of unmanned vehicles, and on the basis of combining vehicle information and environmental information, the stability and safety of unmanned driving in complex working conditions and emergencies can be enhanced, thereby solving the technical problem that the existing path planning method cannot guarantee the stability and safety of the vehicle when the vehicle encounters complex working conditions.

[0049] Optionally, the vehicle driving data includes driving path data, vehicle current state data, and vehicle current driving environment data.

[0050] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being a normal operating condition, adopting a global path planning algorithm as the path planning strategy, wherein the global path planning algorithm is a heuristic search algorithm.

[0051] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being a normal operating condition, a local path planning algorithm is used as the path planning strategy, wherein the local path planning algorithm is a quintic polynomial algorithm.

[0052] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being an extreme condition, determining a model prediction contour control algorithm as the path planning strategy.

[0053] Optionally, construction of a model prediction contour control algorithm includes: obtaining perception information of the vehicle; determining a reference trajectory based on the perception information and parameterizing the reference trajectory to obtain a drivable space for the vehicle; determining a contour error based on a vehicle dynamics model; and constructing boundary constraints and optimization problems for planning control.

[0054] Optionally, the vehicle dynamics model is a two-degree-of-freedom single-track model of the vehicle with nonlinear tire forces.

[0055] Specifically, Figure 2 As shown, the vehicle model has the following dynamic equations:

[0056]

[0057] The state quantities X and Y of the kinematic part represent the position of the vehicle's center of mass (COG) in the inertial coordinate system. represents the yaw angle of the vehicle. The state quantity of the dynamic part is the longitudinal and lateral speed v of the vehicle x and v y , yaw angular velocity ω. Where m and I z Respectively represent the mass and moment of inertia of the vehicle; l r and l f Respectively represent the distance from the center of mass to the rear wheel and the front wheel; F ry and F rf Represent the lateral force of the rear wheel and the front wheel respectively; F rx represents the rear wheel driving force; δ represents the front wheel steering angle.

[0058] Among them, tire force F represents the interaction between the tire and the road surface, which is the most important part of dynamics. In the study of vehicle extreme dynamics, accurate tire modeling is particularly important. A detailed model can correctly reflect the driving state and control limit of the vehicle under extreme working conditions such as high speed and low adhesion. ry and F rf The simplified Pacejka tire model can better fit the lateral force curve actually measured by the vehicle tire.

[0059]

[0060] The parameters B, C, and D are the curve parameters measured and fitted by experiments, and α f ,α r Represent the sideslip angles of the front and rear wheels respectively.

[0061] Optionally, according to the path planning strategy, the vehicle driving path is planned to obtain a path planning result, including: according to the path planning strategy, the path planning result is determined based on the vehicle state, road data small center of mass sideslip angle stability constraint, road boundary constraint, control amount and change rate constraint.

[0062] Optionally, the specific implementation of the above embodiment of the present invention is as follows:

[0063] The path planning system under normal working conditions of the present invention includes global path planning and local path planning. The global path planning algorithm uses the A* algorithm, and the local path planning uses a quintic polynomial.

[0064] The A* algorithm calculates the priority of each node using formula (3):

[0065] f(n)=g(n)+h(n) (3)

[0066] Among them, f(n) is the comprehensive priority of node n. When choosing the next node to traverse, choose the node with the highest comprehensive priority (smallest value). g(n) is the cost of node n from the starting point. h(n) is the estimated cost of node n from the end point, which is the heuristic function of the A* algorithm.

[0067] During the operation of the A* algorithm, each time the node with the smallest f(n) value (highest priority) is selected from the priority queue as the next node to be traversed. In addition, the A* algorithm uses two sets to represent the nodes to be traversed and the nodes that have been traversed, which are called the open set and the close set.

[0068] Local path planning needs to comprehensively consider factors such as safety, comfort, and ease of tracking. This application uses a quintic polynomial model, and the trajectory is expressed as follows:

[0070] y d (x d )=a0x d 5 +a1x d 4 +a2x d 3 +a3x d 2 +a4x d +a5 (4)

[0071] At the lane change start point and lane change end point, the lateral acceleration and lateral velocity are both 0, the lateral displacement at the start point is 0, and the lateral displacement at the lane change end point is the lane width. From the above information, we can know that:

[0072]

[0073] In the above formula, D represents the lane change length, which is determined by the lane change time and lane change speed; W represents the lane width. In summary, the fifth-order polynomial lane change trajectory equation is as follows:

[0074]

[0075] In order to cope with the extreme working condition automatic driving and obtain the high-performance automatic driving controller, the present invention adopts the model prediction profile control algorithm, and the method of constructing the algorithm is specifically as follows:

[0076] 1) Obtain the vehicle status through the vehicle's sensors and collect perception information around the vehicle;

[0077] 2) Process the perception information, parameterize the reference trajectory, and divide the vehicle's drivable space;

[0078] 3) Calculate the contour error by combining the vehicle dynamics model (1)(2a)(2b);

[0079] 4) Construct boundary constraints for planning control under vehicle extreme conditions;

[0080] 5) Solve the optimization problem of planning control under vehicle extreme conditions;

[0081] 6) Apply the first term of the control sequence obtained by solving the optimization problem to the vehicle controller.

[0082] The parameterization of the reference trajectory mentioned in the above method flow refers to the lane centerline cubic polynomial curve fitted by the vehicle perception system for the left and right lanes and returned as the reference trajectory. The arc length of the reference trajectory θ∈[0,L], where L is the total length of the trajectory. By solving the cubic polynomial, we can obtain the value of any point X on the reference trajectory. ref (θ),Y ref The angle between the tangent line of a point on the reference trajectory and the X-axis of the geodetic coordinate system can be expressed by equation (7).

[0083]

[0084] The contour error calculation mentioned in the above method flow refers to defining the difference between the current position (X, Y) of the vehicle and its reference point (X ref (θ),Y ref The projection of the vehicle on the reference path is defined to determine the length of the reference path that the vehicle has traveled:

[0085]

[0086] For simplicity, define From this, the orthogonal distance from the vehicle position (X, Y) to the reference trajectory can be calculated, and the definition of the contour error is given:

[0087]

[0088] where Φ(·) is defined in equation (7), and the contour error e c exist Figure 3 is described in.

[0089] Among them, the vehicle planning boundary mentioned in the above method flow is as follows: Figure 4 and Figure 5 One of the main advantages of using a planning controller based on model predictive control is that constraints can be explicitly handled on the system. Boundary constraints are inherently non-convex. To make them convex, a linear approximation of the boundary can be obtained by projecting the position of the center of mass on the boundary. Figure 4The red straight line is the approximate constraint of the large red point on the planning range. Figure 5 The red line in the middle is the constraint of the points on the entire planning range. For each point in the planning range, there is a corresponding green point on the reference trajectory, which is the closest distance point. The points on the road boundary are the projections of the reference trajectory points.

[0090] Among them, the model prediction contour control problem mentioned in the above method flow is constructed as follows:

[0091] The vehicle dynamics model, reference path, and contour error are combined to build constraints to achieve a balance between path tracking accuracy (low contour error) and vehicle travel distance within a limited N sampling time range. Construct a nonlinear optimization problem in the form of optimal control:

[0092]

[0093] stθ0=θ,(10b)

[0094]

[0095] 0≤θ k ≤L,k=1,…,N(10d)

[0096]

[0097] In the formula and The first term of the formula is the contour error term of the vehicle state, the second term is the lag error term, the third and sixth terms are the weight terms for the vehicle speed, and the fourth and fifth terms are the cost terms for input changes added to avoid sudden changes, which helps to obtain smoother control inputs.

[0098] Where X k ,Y k is the position of the vehicle at time k, is the orthogonal projection point of the vehicle position on the reference path The arc length at u k is the input control quantity, θ k and v k is limited to its physical constraints to avoid erroneous solutions to nonlinear problems. s is the sampling time. and is the weight value. To ensure accurate approximation and strong coupling between the cost function and the vehicle model, the lag error Select a larger value.

[0099] To solve the nonlinear optimal control problem in real time, the nonlinear terms are linearized and the quadratic programming form is used at each sampling time:

[0100]

[0101] x k+1 =A k x k +B k u k +g k ,k=0,…,N-1,(11c)

[0102]

[0103] F k x k ≤f k +s k ,k=1,…,N,(11e)

[0104] s k ≥0,k=1,…,N,(11f)

[0105]

[0106] The first and second terms of the formula are the results of linearizing the vehicle profile error and hysteresis error cost terms, where represents the quadratic part of the linearized profile and hysteresis error loss function, The fourth term in the formula is to avoid the cost term of sudden change, and the fifth term in the formula is the cost term of the road boundary constraint.

[0107] To ensure that the linearity error is small enough, a linear time-varying estimate of the vehicle dynamics as well as the profile and hysteresis errors is used. The measured value x0 = x is the first simulation point. is the road boundary constraint. The control input u k Limit the state variable x to a reasonable range. k The limits are kept within reasonable limits to avoid convergence problems with the solver.

[0108] The above-mentioned extreme working condition planning control problem is simulated and tested. The vehicle model parameters use the proportionally reduced RC car parameters.

[0109]

[0110] Vehicle tire parameters are as follows:

[0111]

[0112] Use a circular field for testing, the field size is Figure 6 shown. Figure 7 and Figure 8The table shows the impact of different weights on the vehicle's driving trajectory. Different vehicle trajectories can be obtained by modifying the weight of the contour error term. When the contour error weight is large, the vehicle's driving trajectory is closer to the reference trajectory than when the weight is low. This proves that different behaviors of the vehicle can be controlled by modifying the weight term.

[0113] The vehicle's trajectory for a period of time in the future is predicted based on the vehicle's state and road information. The prediction process of this trajectory includes the traditional stability constraints for the vehicle's small center of mass sideslip angle, road boundary constraints, and control quantity and its rate of change constraints. If the predicted trajectory has a solution, the conventional path planning algorithm is used to complete the planning task. Otherwise, the path planning system under extreme working conditions is switched to this method, which is used as the switching judgment boundary between the conventional path planning and extreme path planning mentioned above.

[0114] The current state of the vehicle is x(0)=[Y(0),v y (0),v x (0),φ(0),r(0)] T , the desired final vehicle state vector after obstacle avoidance is x(t e )=[Y(t e ),v y (t e ),v x (t e ),φ(t e ),r(t e )] T , the objective function is the predicted yaw rate at the next moment, and the optimization variable is the control input sequence u(t) in the prediction time domain Hp = [F r (t),δ f (t)] T ,...,u(t+H p +1)=[F r (t+H p +1),δ f (t+H p +1)] T , control input u=[F r ,δ f ] T , where F r is the output torque of the rear axle, δ f is the front wheel turning angle.

[0115] The optimization problem is stated as follows:

[0116]

[0117] stx(t)=x(0) (a)

[0118] x(k+1)=f vm (x(k),u(k)) (b)

[0119] x(t+i)∈χ veh ,i=1,2,...,H p (c)

[0120] x(t+i)∈χ road ,i=1,2,...,H p (d)

[0121] u(t+i)∈U road ,i=1,2,...,H p -1(e)

[0122] x(t e )∈ζ e , (f)

[0123] Constraint (a) indicates that the optimization problem starts from the current state;

[0124] Constraint (b) represents the state space expression of the optimization problem, that is, the vehicle model;

[0125] Constraint (c) means that the vehicle always stays within the vehicle stability limit or the drift stability limit in the prediction time domain. The difference between the two lies in the limit on the center of mass slip angle. The center of mass slip angle of the vehicle stability limit is limited to less than 10°. For the drift stability limit, the center of mass slip angle limit can achieve the vehicle not losing the control ability of the front wheels when the rear wheels are saturated, that is, it stays within the range where the rear wheels are saturated but the front wheels are not saturated. It is a limit related to the current state of the vehicle;

[0126] Constraint (d) means that the vehicle always stays within the road safety boundary excluding the area where obstacles are located in the prediction time domain;

[0127] Constraint (e) is the constraint on the control input, which represents the physical limit of the control quantity;

[0128] Constraint (f) is the constraint of the stable domain of the vehicle state at the prediction end point, which means the stable state that the vehicle should reach at the end of the prediction time domain.

[0129] It is understandable that the path planning method provided by the present invention adopts a global planning and local planning hierarchical planning method in the path planning system under normal working conditions, processes the path planning tasks under normal working conditions, and outputs a planning result that is safe, collision-free, and can reach the target end point, meeting the tracking controller requirements under normal working conditions. When the vehicle is determined to be in an extreme working condition based on the driving path information, the current state of the vehicle, and the environmental obstacle information, the system automatically triggers and activates the model-based prediction contour planning control system under the extreme working condition, uses the center line of the road as a reference path, widens the dynamic control boundary of the unmanned vehicle, and generates a planning result similar to the drift trajectory of a professional racing driver. The advantage of this method is that path planning and path tracking are combined into a nonlinear control problem, and can be solved in real time. Give full play to the movement potential of unmanned vehicles, and enhance the stability and safety of unmanned driving in complex working conditions and emergencies on the basis of combining vehicle information and environmental information.

[0130] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0131] In this embodiment, a path planning system is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" is a combination of software and / or hardware that can implement a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0132] Fig. 9 is a structural block diagram of a path planning system 200 according to one embodiment of the present invention. Fig. 9 As shown, a path planning system 200 is used as an example, including: an acquisition module 201, used to acquire vehicle driving data; a first determination module 202, used to determine the vehicle operating condition according to the vehicle driving data and a preset operating condition standard, wherein the vehicle operating condition includes a normal operating condition and an extreme operating condition; a second determination module 203, used to determine a path planning strategy according to the vehicle operating condition; and a planning module 204, used to plan the vehicle driving path according to the path planning strategy to obtain a path planning result.

[0133] Optionally, the vehicle driving data acquired by the acquisition module 201 includes driving path data, vehicle current state data, and vehicle current driving environment data.

[0134] Optionally, the second determination module 203 is further used to: in response to the vehicle operating condition being a normal operating condition, adopt a global path planning algorithm as a path planning strategy, wherein the global path planning algorithm is a heuristic search algorithm.

[0135] Optionally, the second determination module 203 is further used to: in response to the vehicle operating condition being a normal operating condition, adopt a local path planning algorithm as a path planning strategy, wherein the local path planning algorithm is a quintic polynomial algorithm.

[0136] Optionally, the second determination module 203 is further used to: in response to the vehicle operating condition being an extreme operating condition, determine a model prediction profile control algorithm as a path planning strategy.

[0137] Optionally, the second determination module 203 is also used to: obtain the perception information of the vehicle; determine the reference trajectory based on the perception information and parameterize the reference trajectory to obtain the vehicle's drivable space; determine the contour error based on the vehicle dynamics model; and construct boundary constraints and optimization problems for planning control.

[0138] Optionally, the vehicle dynamics model in the second determination module 203 is a vehicle two-degree-of-freedom single-track model with nonlinear tire forces.

[0139] Optionally, the planning module 204 is also used to determine the path planning result according to the path planning strategy, based on the vehicle state, road data small center of mass sideslip angle stability constraint, road boundary constraint, control amount and change rate constraint.

[0140] An embodiment of the present invention further provides a vehicle, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the path planning method described in any of the above embodiments.

[0141] Optionally, in this embodiment, the processor in the vehicle may be configured to run a computer program to perform the following steps:

[0142] Step S101, obtaining vehicle driving data.

[0143] Step S102, determining the vehicle operating condition according to the vehicle driving data and the preset operating condition standard, wherein the vehicle operating condition includes a normal operating condition and an extreme operating condition.

[0144] Step S103, determining a path planning strategy according to the vehicle operating conditions.

[0145] Step S104, planning the vehicle driving path according to the path planning strategy to obtain a path planning result.

[0146] Optionally, the vehicle driving data includes driving path data, vehicle current state data, and vehicle current driving environment data.

[0147] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being a normal operating condition, adopting a global path planning algorithm as the path planning strategy, wherein the global path planning algorithm is a heuristic search algorithm.

[0148] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being a normal operating condition, a local path planning algorithm is used as the path planning strategy, wherein the local path planning algorithm is a quintic polynomial algorithm.

[0149] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being an extreme condition, determining a model prediction contour control algorithm as the path planning strategy.

[0150] Optionally, construction of a model prediction contour control algorithm includes: obtaining perception information of the vehicle; determining a reference trajectory based on the perception information and parameterizing the reference trajectory to obtain a drivable space for the vehicle; determining a contour error based on a vehicle dynamics model; and constructing boundary constraints and optimization problems for planning control.

[0151] Optionally, the vehicle dynamics model is a two-degree-of-freedom single-track model of the vehicle with nonlinear tire forces.

[0152] Optionally, according to the path planning strategy, the vehicle driving path is planned to obtain a path planning result, including: according to the path planning strategy, the path planning result is determined based on the vehicle state, road data small center of mass sideslip angle stability constraint, road boundary constraint, control amount and change rate constraint.

[0153] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0154] An embodiment of the present invention further provides a non-volatile storage medium, in which a computer program is stored, wherein the computer program is configured to execute the path planning method described in any of the above embodiments when running on a computer or a processor.

[0155] Optionally, in this embodiment, the above-mentioned computer program may be configured to store a computer program for performing the following steps:

[0156] Step S101, obtaining vehicle driving data.

[0157] Step S102, determining the vehicle operating condition according to the vehicle driving data and the preset operating condition standard, wherein the vehicle operating condition includes a normal operating condition and an extreme operating condition.

[0158] Step S103, determining a path planning strategy according to the vehicle operating conditions.

[0159] Step S104, planning the vehicle driving path according to the path planning strategy to obtain a path planning result.

[0160] Optionally, the vehicle driving data includes driving path data, vehicle current state data, and vehicle current driving environment data.

[0161] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being a normal operating condition, adopting a global path planning algorithm as the path planning strategy, wherein the global path planning algorithm is a heuristic search algorithm.

[0162] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being a normal operating condition, a local path planning algorithm is used as the path planning strategy, wherein the local path planning algorithm is a quintic polynomial algorithm.

[0163] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being an extreme condition, determining a model prediction contour control algorithm as the path planning strategy.

[0164] Optionally, construction of a model prediction contour control algorithm includes: obtaining perception information of the vehicle; determining a reference trajectory based on the perception information and parameterizing the reference trajectory to obtain a drivable space for the vehicle; determining a contour error based on a vehicle dynamics model; and constructing boundary constraints and optimization problems for planning control.

[0165] Optionally, the vehicle dynamics model is a two-degree-of-freedom single-track model of the vehicle with nonlinear tire forces.

[0166] Optionally, according to the path planning strategy, the vehicle driving path is planned to obtain a path planning result, including: according to the path planning strategy, the path planning result is determined based on the vehicle state, road data small center of mass sideslip angle stability constraint, road boundary constraint, control amount and change rate constraint.

[0167] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0168] An embodiment of the present invention further provides a computer program product, including a computer program, which implements the path planning method described in any of the above embodiments when executed by a processor.

[0169] Optionally, in this embodiment, the above computer program implements the following steps when executed by a processor:

[0170] Step S101, obtaining vehicle driving data.

[0171] Step S102, determining the vehicle operating condition according to the vehicle driving data and the preset operating condition standard, wherein the vehicle operating condition includes a normal operating condition and an extreme operating condition.

[0172] Step S103, determining a path planning strategy according to the vehicle operating conditions.

[0173] Step S104, planning the vehicle driving path according to the path planning strategy to obtain a path planning result.

[0174] Optionally, the vehicle driving data includes driving path data, vehicle current state data, and vehicle current driving environment data.

[0175] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being a normal operating condition, adopting a global path planning algorithm as the path planning strategy, wherein the global path planning algorithm is a heuristic search algorithm.

[0176] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being a normal operating condition, a local path planning algorithm is used as the path planning strategy, wherein the local path planning algorithm is a quintic polynomial algorithm.

[0177] Optionally, a path planning strategy is determined according to the vehicle operating condition, including: in response to the vehicle operating condition being an extreme condition, determining a model prediction contour control algorithm as the path planning strategy.

[0178] Optionally, construction of a model prediction contour control algorithm includes: obtaining perception information of the vehicle; determining a reference trajectory based on the perception information and parameterizing the reference trajectory to obtain a drivable space for the vehicle; determining a contour error based on a vehicle dynamics model; and constructing boundary constraints and optimization problems for planning control.

[0179] Optionally, the vehicle dynamics model is a two-degree-of-freedom single-track model of the vehicle with nonlinear tire forces.

[0180] Optionally, according to the path planning strategy, the vehicle driving path is planned to obtain a path planning result, including: according to the path planning strategy, the path planning result is determined based on the vehicle state, road data small center of mass sideslip angle stability constraint, road boundary constraint, control amount and change rate constraint.

[0181] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0182] In some embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the modules can be a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, modules or indirect coupling or communication connection of modules, which can be electrical or other forms.

[0183] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0184] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0185] If the integrated module 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. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0186] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A path planning method, characterized in that: include: Obtain vehicle driving data; Determining a vehicle operating condition according to the vehicle driving data and a preset operating condition standard, wherein the vehicle operating condition includes a normal operating condition and an extreme operating condition; Determining a path planning strategy according to the vehicle operating condition; According to the path planning strategy, the vehicle driving path is planned to obtain a path planning result.

2. The path planning method according to claim 1, characterized in that: The vehicle driving data includes driving path data, vehicle current state data, and vehicle current driving environment data.

3. The path planning method according to claim 1, characterized in that: Determining a path planning strategy according to the vehicle operating condition includes: In response to the vehicle operating condition being the normal operating condition, a global path planning algorithm is adopted as the path planning strategy, wherein the global path planning algorithm is a heuristic search algorithm.

4. The path planning method according to claim 1, characterized in that: Determining a path planning strategy according to the vehicle operating condition includes: In response to the vehicle operating condition being the normal operating condition, a local path planning algorithm is adopted as the path planning strategy, wherein the local path planning algorithm is a quintic polynomial algorithm.

5. The path planning method according to claim 1, characterized in that: Determining a path planning strategy according to the vehicle operating condition includes: In response to the vehicle operating condition being the extreme operating condition, a model prediction profile control algorithm is determined as the path planning strategy.

6. The path planning method according to claim 5, characterized in that: The construction of the model prediction contour control algorithm includes: Obtaining vehicle perception information; Determine a reference trajectory based on the perception information and parameterize the reference trajectory to obtain a drivable space for the vehicle; Determine the profile error based on the vehicle dynamics model; Construct boundary constraints and optimization problems for planning control.

7. The path planning method according to claim 6, characterized in that: The vehicle dynamics model is a two-degree-of-freedom single-track model of the vehicle with nonlinear tire forces.

8. The path planning method according to claim 1, characterized in that: The step of planning the vehicle driving path according to the path planning strategy to obtain a path planning result includes: According to the path planning strategy, the path planning result is determined based on the vehicle state, road data small center of mass sideslip angle stability constraint, road boundary constraint, control amount and change rate constraint.

9. A path planning system, characterized in that: include: An acquisition module, used for acquiring vehicle driving data; A first determination module is used to determine a vehicle operating condition according to the vehicle driving data and a preset operating condition standard, wherein the vehicle operating condition includes a normal operating condition and an extreme operating condition; A second determination module is used to determine a path planning strategy according to the vehicle operating condition; The planning module is used to plan the vehicle's driving path according to the path planning strategy to obtain a path planning result.

10. A vehicle comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to run the computer program to execute the path planning method described in any one of claims 1 to 8.

11. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a computer program, wherein the computer program is configured to execute the path planning method described in any one of claims 1 to 8 when running on a computer or a processor.

12. A computer program product, characterized in that The method comprises a computer program, which implements the path planning method according to any one of claims 1 to 8 when being executed by a processor.

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