Vehicle speed decision-making method and system, vehicle and equipment

Abstract

The invention discloses a vehicle speed decision-making method and system, a vehicle and equipment. The vehicle speed decision-making method comprises the following steps: obtaining a dynamically planned speed curve; according to the speed curve, longitudinal decisions are made for all obstacles, and the longitudinal decisions comprise a following strategy, an overtaking strategy and an ignoring strategy; target obstacles are searched, and the target obstacles comprise obstacles selected from the obstacles making the following strategy; according to the target obstacle, forward longitudinal simulation in a lane is carried out on the vehicle, and a final speed curve is obtained. By the adoption of the method and device, lane forward simulation can be conducted on the speed curve (rough solution) obtained through dynamic planning, so that the pressure of a downstream speed planning module can be relieved, the solving speed of speed planning can be increased, in addition, in lane forward simulation, the end point constraint can be followed, and the speed planning accuracy is improved. The speed planning solving speed can be effectively improved, and solving of emergency braking, cut-in and other scenes can be guaranteed.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a vehicle speed decision method, system, vehicle and equipment. Background Art

[0002] Computer technology, information technology, automatic control technology, artificial intelligence technology are gradually applied in the field of autonomous driving technology. Vehicle speed decision technology is one of the key technologies for autonomous driving, and it is also a basic problem and necessary condition for studying intelligent vehicles. Vehicle speed decision technology is to calculate the speed curve that meets safety, comfort, and feasibility based on the state and environmental information of the vehicle. At present, the commonly used speed decision method is a method based on dynamic programming, which samples on the ST graph, forward calculates all feasible solutions for the entire time, reversely searches to obtain the minimum cost result, and finally outputs the speed curve, that is, the rough solution of speed planning.

[0003] The following technical defects exist: (1) The speed curve obtained based on dynamic programming is relatively rough. The speed curve is not smooth and has broken lines. The smoothness of the speed curve depends entirely on the downstream module of the speed decision (speed planning). The objective function of the speed planning needs to be solved between smoothness and constraints, and it must be kept consistent with the rough speed solution as much as possible. The requirements for speed planning are too high and difficult to achieve in practice. (2) It is difficult to impose constraints on the terminal state when making speed decisions based on dynamic programming, which can easily lead to solution failure in some scenarios such as emergency braking and cutting in. Summary of the invention

[0004] Based on this, it is necessary to provide a vehicle speed decision method, system, vehicle and equipment to address the above technical problems, which can perform lane forward simulation on the speed curve (rough solution) obtained by dynamic planning, thereby reducing the pressure on the downstream speed planning module and improving the speed of speed planning solution. In addition, in the lane forward simulation, the end point constraint can be followed, which can effectively improve the speed of speed planning solution and ensure the solution of scenarios such as emergency braking and cutting in.

[0005] In a first aspect, a vehicle speed decision method is provided, comprising:

[0006] Obtain the speed curve of vehicle dynamic planning;

[0007] According to the speed curve, a longitudinal decision is made for each obstacle, wherein the longitudinal decision includes a following strategy, a surpassing strategy and an ignoring strategy;

[0008] Finding a target obstacle, wherein the target obstacle includes an obstacle selected from obstacles for which a following strategy is made;

[0009] According to the target obstacle, a longitudinal simulation is performed on the vehicle in the forward direction within the lane to obtain a final speed curve.

[0010] In some examples, making longitudinal decisions for each obstacle based on the speed curve includes:

[0011] Obtain the minimum and maximum values ​​of the obstacle in the s direction at the minimum time point on the ST graph;

[0012] The minimum and maximum values ​​of the obstacle in the s direction at the minimum time point on the ST graph are compared with the position of the vehicle in the s direction at the minimum time point, where:

[0013] When the position of the vehicle in the s direction at the minimum time point is greater than the maximum value, making an overtaking decision on the obstacle;

[0014] When the position of the ego vehicle in the s direction at the minimum time point is less than the minimum value, or the sum of the position of the ego vehicle in the s direction at the minimum time point and the length of the ego vehicle is less than the maximum value, a following decision is made for the obstacle, otherwise an ignoring strategy is adopted for the obstacle.

[0015] In some examples, searching for a target obstacle includes:

[0016] Traverse each obstacle for which a following decision is made to find the obstacle closest to the ego vehicle as the target obstacle.

[0017] In some examples, this also includes:

[0018] If there is no obstacle for which a following decision is made, a virtual obstacle is constructed and used as the target obstacle, wherein the distance between the position of the virtual obstacle and the ego vehicle is greater than a preset distance and the speed of the virtual obstacle is a reference speed of the ego vehicle.

[0019] In some examples, the performing of a forward longitudinal simulation of the ego vehicle in a lane according to the target obstacle to obtain a final speed curve includes:

[0020] At each moment, calculate the expected distance between the ego vehicle and the target obstacle;

[0021] Based on the expected distance, calculating the acceleration of the improved intelligent driver model and calculating the acceleration of the isoacceleration inspired model;

[0022] Comparing the acceleration of the improved intelligent driver model with the acceleration of the equal acceleration inspiration model to obtain the acceleration at time t;

[0023] According to the acceleration at time t, the vehicle position and the target obstacle position at the next time t+Δt are updated, so as to obtain the final speed curve according to the update result.

[0024] In some examples, obtaining a speed curve for vehicle dynamic planning includes:

[0025] By sampling on the ST graph, multiple feasible solutions for the entire time are forward calculated, and the minimum cost value of each feasible solution among the multiple feasible solutions is obtained by reverse search, and the speed curve corresponding to the minimum cost value is used as the speed curve for vehicle dynamic planning.

[0026] In a second aspect, a vehicle speed decision system is provided, comprising:

[0027] An acquisition module is used to obtain a speed curve of a vehicle dynamic planning;

[0028] A longitudinal decision module, used for making longitudinal decisions on each obstacle according to the speed curve, wherein the longitudinal decision includes a following strategy, a surpassing strategy and an ignoring strategy;

[0029] A search module, configured to search for a target obstacle, wherein the target obstacle comprises an obstacle selected from obstacles for which a following strategy is made;

[0030] The speed decision module is used to perform a forward longitudinal simulation of the vehicle in the lane according to the target obstacle to obtain a final speed curve.

[0031] In a third aspect, a vehicle is provided, comprising: a vehicle speed decision system according to the second aspect described above.

[0032] In a fourth aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the vehicle speed decision method of the first aspect and any possible implementation of the first aspect are implemented.

[0033] In a fifth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the steps of the vehicle speed decision method of the above-mentioned first aspect and any possible implementation method of the first aspect are implemented.

[0034] In a sixth aspect, a computer program product is provided, on which a computer program is stored, and when the program is executed by a processor, the steps of the vehicle speed decision method of the above-mentioned first aspect and any possible implementation method of the first aspect are implemented.

[0035] By using the embodiments of the present application, longitudinal decisions can be made for each obstacle based on the speed curve of dynamic programming. After the decision is made, the target obstacle is found based on the decision result. Finally, the vehicle is simulated longitudinally in the lane forward based on the target obstacle to obtain the final speed curve. The speed curve (rough solution) obtained by dynamic programming can be simulated forward in the lane, thereby reducing the pressure on the downstream speed planning module and improving the speed of speed planning. In addition, in the forward simulation of the lane, the end point constraint can be followed, which can effectively improve the speed of speed planning and ensure the solution of scenes such as emergency braking and cutting in. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0037] Figure 1 A flow chart of a vehicle speed decision method provided in an embodiment of the present application;

[0038] Figure 2 This is a schematic diagram of the state after the ST diagram is initialized;

[0039] Figure 3 It is a schematic diagram of the feasible downward movement area for each sampling point in the ST diagram;

[0040] Figure 4 Schematic diagram of reverse search;

[0041] Figure 5 A structural block diagram of a vehicle speed decision system provided in an embodiment of the present application;

[0042] Figure 6 A structural block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The present application is further described in detail below in conjunction with the embodiments and drawings. It is to be understood that the specific embodiments described herein are only used to explain the relevant application, rather than to limit the application. It is also necessary to explain that, for ease of description, only the parts related to the application are shown in the drawings.

[0044] It should be noted that, in the absence of conflict, the embodiments of the present application, that is, the features of the embodiments, can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0045] The following describes in detail the vehicle speed decision method, system, device and medium according to the embodiments of the present application in conjunction with the accompanying drawings.

[0046] Figure 1FIG. 1 is a flow chart of a vehicle speed decision method according to an embodiment of the present application. Figure 1 As shown, the vehicle speed decision method according to an embodiment of the present application includes the following steps:

[0047] S101: Obtaining a speed curve of vehicle dynamic planning.

[0048] In one embodiment of the present application, the speed curve of the vehicle can be obtained by a dynamic programming method, for example: obtaining the speed curve of the vehicle dynamic programming includes: sampling on the ST graph to forward calculate multiple feasible solutions for the entire time, and reversely searching to obtain the minimum cost value of each feasible solution in the multiple feasible solutions, and using the speed curve corresponding to the minimum cost value as the speed curve of the vehicle dynamic programming.

[0049] Specifically, the ST graph is first sampled, and each point is represented as [t, s].

[0050] In the S direction, dense sampling is used for near objects and sparse sampling is used for far objects. The distinction between near objects and far objects is achieved by setting an S threshold, which is usually between 10m and 40m. The sampling length of S is s end Usually the path length is taken.

[0051] In the T direction, equal-space sampling is adopted, and the sampling interval is unit time (usually 1s). The sampling length of T is t end Usually the set planning time is taken. Among them, the state of the ST diagram after initialization is as follows Figure 2 shown.

[0052] The cost of each sampling point [t, s] in the ST graph is set to infinity, the speed of the initial point t = 0, s = 0 is set as the initial speed, and the speed of each sampling point [t, s] in the ST graph is set to limit the speed.

[0053] Forward recursion

[0054] Start the forward recursion from the initial point t=0, s=0, set the cost of the initial point to 0, and traverse every feasible sampling point [t, s].

[0055] like Figure 3 As shown, the next feasible region for each point is calculated as follows:

[0056] Δs min =s+vΔt+0.5a min Δt 2

[0057] Δs min =s+vΔt+0.5a max Δt 2

[0058] Among them, Δt is the time error between the current point and the next point, Δs min and Δs max is the minimum and maximum value that can be reached at the sampling point [t, s] after Δt, v ​​is the velocity at the current point, a min and a max is the minimum acceleration and maximum acceleration of the vehicle at the current point.

[0059] The state transfer equation is as follows:

[0060] C(t+Δt,s+Δs)=C(t,s)+C obs (t+Δt,s+Δs)+C edge (t,s,t+Δt,s+Δs)

[0061] Among them, C(t,s) is the cost of the point [t,s], C(t+Δt,s+Δs) is the cost of the point [t+Δt,s+Δs], and C obs (t+Δt,s+Δs) is the obstacle cost at point [t+Δt,s+Δs], C edge (t,s,t+Δt,s+Δs) is the cost of moving from point [t,s] to point [t+Δt,s+Δs].

[0062] The calculation expression of obstacle cost is as follows:

[0063]

[0064] s follow =v·T follow +s follow-safe

[0065] s overtake =v obs ·T overtake +s overtake-safe

[0066] Among them, C obs (t,s) is the obstacle cost at point [t,s], s obs-lower and obs-upper is the minimum and maximum value of the obstacle in the s direction, ω obs is the weight coefficient of obstacle cost, s follow is the safe distance for following vehicles, s overtake is the safe distance for overtaking the vehicle, v is the speed of the vehicle, and v obs is the obstacle speed, T follow is the time moment of following the vehicle, T overtake is the moment of overtaking vehicle travel, s follow-safeis the minimum safe distance for following vehicles, s overtake-safe The minimum safe distance for overtaking vehicles.

[0067] Moving from point [t,s] to point [t+Δt,s+Δs], we can get the velocity, acceleration, and jerk of point [t+Δt,s+Δs]. The calculation expression of the moving cost is as follows:

[0068] C edge (t,s,t+Δt,s+Δs)=C speed +C acc +C jerk

[0069] C acc =ω acc a 2

[0070] C jerk =ω jerk j 2

[0071] Among them, C speed is the speed cost, C acc is the acceleration cost, C jerk is the acceleration cost.

[0072] The calculation expression of speed cost is as follows:

[0073]

[0074] Where Δv is the velocity change from point [t,s] to point [t+Δt,s+Δs], v ref is the reference speed, ω v-up and ω v-lower is the speed change weight coefficient under different conditions, ω re f is the error weight coefficient with reference speed, v limit For speed limit.

[0075] The calculation expression of acceleration cost is as follows:

[0076]

[0077] Where a is the acceleration from point [t,s] to point [t+Δt,s+Δs], a min and a max is the minimum and maximum value of acceleration, ω acc is the weight coefficient when the acceleration is positive, ω dec is the weight coefficient when the acceleration is negative.

[0078] The calculation expression of acceleration cost is as follows:

[0079]

[0080] Where, j is the acceleration from point [t,s] to point [t+Δt,s+Δs], j min and j max is the minimum and maximum value of the acceleration, ω j-positive is the weight coefficient when the acceleration is positive, ω j-negative is the weight coefficient when the acceleration is negative.

[0081] Thus, we can recursively proceed from the initial point to the end point [t end ,s end ], and get the total cost of the feasible speed curve.

[0082] Reverse Search

[0083] like Figure 4 As shown, the last position sampled from s, that is, s = s end , start reverse search, the process is as follows:

[0084] Traverse all s=s end The sampling point is selected to find the location with the minimum total cost [t best ,s end ], where t best is the time t corresponding to the sampling point with the minimum total cost;

[0085] The position with the minimum total cost [t best ,s end ] Start reverse search and find the point with the smallest cost [t best -Δt,s end -Δs];

[0086] Repeat the previous step until the initial point t=0, s=0, and end.

[0087] Since it is a reverse search, after obtaining the above ts curve, it is necessary to reverse the order to obtain the ts curve in the positive order.

[0088] Conversion results

[0089] Since the speed curve needs to know t, s, v, and a at each point, the ts curve of the reverse search needs to be converted. Knowing the s and v of the initial point, the t, s, v, and a of the next point can be calculated forward using the following formula:

[0090]

[0091] S102: Making longitudinal decisions for each obstacle according to the speed curve, wherein the longitudinal decisions include a following strategy, a surpassing strategy, and an ignoring strategy.

[0092] In one embodiment of the present application, a longitudinal decision is made for each obstacle based on the speed curve, including: obtaining the minimum and maximum values ​​of the obstacle in the s direction at the minimum time point on the ST diagram; comparing the minimum and maximum values ​​of the obstacle in the s direction at the minimum time point on the ST diagram with the position of the ego vehicle in the s direction at the minimum time point, wherein: when the position of the ego vehicle in the s direction at the minimum time point is greater than the maximum value, an overtaking decision is made for the obstacle; when the position of the ego vehicle in the s direction at the minimum time point is less than the minimum value, or the sum of the position of the ego vehicle in the s direction at the minimum time point and the length of the ego vehicle is less than the maximum value, a following decision is made for the obstacle, otherwise an ignoring strategy is adopted for the obstacle.

[0093] Specifically, after obtaining the velocity profile of the ego vehicle, a longitudinal decision (follow, overtake, ignore) needs to be made for each obstacle.

[0094] Take the minimum time t of each obstacle ST graph obs-min , we can get the obstacle at t obs-min The minimum value s in the s direction at the moment obs-min and the maximum value s ego >s obs-max , and the vehicle at t obs-min The position in the s direction at the time s ego For comparison, the length of the vehicle is l ego ,as follows:

[0095] When ego >s obs-max When the vehicle reaches the obstacle, it makes a longitudinal decision to overtake the obstacle, that is, it performs a driving operation to overtake the obstacle;

[0096] When ego obs-min or s+l ego obs-max When the obstacle is reached, a longitudinal decision is made to follow the obstacle, that is, a driving operation to follow the obstacle is performed;

[0097] In other cases, a longitudinal decision is made to ignore the obstacle, that is, the driving operation of the obstacle is not considered.

[0098] After making longitudinal decisions for all obstacles, the next step can be carried out.

[0099] S103: searching for a target obstacle, wherein the target obstacle includes an obstacle selected from obstacles for which a following strategy is made.​​

[0100] In one embodiment of the present application, searching for a target obstacle includes: traversing each obstacle for which a following decision is made to find an obstacle closest to the ego vehicle as the target obstacle.

[0101] This example also includes: if there is no obstacle for which a following decision is made, a virtual obstacle is constructed and the virtual obstacle is used as the target obstacle, wherein the distance between the position of the virtual obstacle and the vehicle is greater than a preset distance and the speed is the reference speed of the vehicle.

[0102] For example: traverse and search all obstacles that make a follow decision, and find the obstacle closest to the ego vehicle, which is the target obstacle. If there is no obstacle that makes a follow decision, construct a virtual obstacle, the position of which is very far from the ego vehicle (usually set to 300m), and the speed is the reference speed v of the ego vehicle ref , and the virtual obstacle is taken as the target obstacle.

[0103] S104: According to the target obstacle, a longitudinal simulation is performed on the vehicle in the forward direction within the lane to obtain a final speed curve.

[0104] In one embodiment of the present application, according to the target obstacle, a forward longitudinal simulation of the ego vehicle in the lane is performed to obtain a final speed curve, including: at each moment, calculating the expected distance between the ego vehicle and the target obstacle; based on the expected distance, calculating the acceleration of the improved intelligent driver model, and calculating the acceleration of the equal acceleration inspiration model; comparing the acceleration of the improved intelligent driver model with the acceleration of the equal acceleration inspiration model to obtain the acceleration at time t; according to the acceleration at time t, updating the ego vehicle position and the target obstacle position at the next moment t+Δt, so as to obtain the final speed curve according to the update result.

[0105] Specifically, starting from t = 0, the interval Δt, until t = t end , the longitudinal simulation of the vehicle forward in the lane is performed, and finally the speed curve is obtained.

[0106] At each moment, calculate the expected distance s to the target obstacle ref ,as follows:

[0107]

[0108] Among them, s follow-safe is the minimum safe distance for following vehicles, v is the speed of the vehicle, T follow is the time to follow the target obstacle, v obs is the speed of the target obstacle, a is the comfortable acceleration of the vehicle, and b is the comfortable deceleration of the vehicle (a positive number).

[0109] Calculate the acceleration a of the improved intelligent driver model (IIDM) IIDM ,as follows:

[0110]

[0111] Among them, v ref is the reference speed of the ego vehicle, δ is the speed coefficient (usually 4), and s is the distance between the ego vehicle and the target obstacle.

[0112] Calculate the acceleration a of the constant acceleration heuristic model (CAH) CAH ,as follows:

[0113]

[0114] Where ε is a step function,

[0115] Improve the acceleration of IDM model IIDM The acceleration a of the isoacceleration-inspired model CAH By comparison, we can get the acceleration at time t:

[0116]

[0117] Among them, k acc is the acceleration proportionality coefficient.

[0118] According to the acceleration at time t, the position of the vehicle and the target obstacle at the next time t+Δt are updated as follows:

[0119] v(t+Δt)=v(t)+aΔt

[0120] s(t+Δt)=s(t)+vΔt+0.5aΔt 2

[0121] v obs (t+Δt)=v obs (t)+a obs Δt

[0122] s obs (t+Δt)=s obs (t)+v obs Δt+0.5a obs Δt 2

[0123] From the above formula, we can know that t, s, v, and a at each moment can be obtained, thus forming the final result ts curve.

[0124] According to the vehicle speed decision method of the embodiment of the present application, longitudinal decisions can be made for each obstacle based on the speed curve of dynamic programming. After the decision is made, the target obstacle is found based on the decision result. Finally, according to the target obstacle, the vehicle is simulated longitudinally in the lane to obtain the final speed curve. The speed curve (rough solution) obtained by dynamic programming can be simulated forward in the lane, thereby reducing the pressure on the downstream speed planning module and improving the speed of speed planning. In addition, in the forward simulation of the lane, the end point constraint can be followed, which can effectively improve the speed of speed planning and ensure the solution of scenes such as emergency braking and cutting in.

[0125] Figure 5 FIG. 1 is a structural block diagram of a vehicle speed decision system according to an embodiment of the present application. Figure 5 As shown, a vehicle speed decision system according to an embodiment of the present application includes: an acquisition module 510, a longitudinal decision module 520, a search module 530 and a speed decision module 540, wherein:

[0126] An acquisition module 510 is used to obtain a speed curve of a vehicle dynamic planning;

[0127] A longitudinal decision module 520, configured to make longitudinal decisions on each obstacle according to the speed curve, wherein the longitudinal decisions include a following strategy, a surpassing strategy, and an ignoring strategy;

[0128] A search module 530, configured to search for a target obstacle, wherein the target obstacle comprises an obstacle selected from obstacles for which a following strategy is made;

[0129] The speed decision module 540 is used to perform a forward longitudinal simulation of the ego vehicle in the lane according to the target obstacle to obtain a final speed curve.

[0130] According to the vehicle speed decision system of the embodiment of the present application, longitudinal decisions can be made for each obstacle based on the speed curve of dynamic programming. After the decision is made, the target obstacle is found according to the decision result. Finally, according to the target obstacle, the vehicle is simulated longitudinally in the lane to obtain the final speed curve. The speed curve (rough solution) obtained by dynamic programming can be simulated forward in the lane, thereby reducing the pressure on the downstream speed planning module and improving the speed of speed planning. In addition, in the forward simulation of the lane, the end point constraint can be followed, which can effectively improve the speed of speed planning and ensure the solution of scenes such as emergency braking and cutting in.

[0131] The specific definition of the vehicle speed decision system can be found in the definition of the vehicle speed decision method mentioned above, which will not be repeated here. The various modules of the above-mentioned vehicle speed decision system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned various modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned various modules.

[0132] In one embodiment, a vehicle is provided, comprising: a speed decision system for a vehicle according to any one of the above embodiments. The vehicle can make longitudinal decisions on each obstacle based on the speed curve of dynamic programming, and after making the decision, find the target obstacle based on the decision result, and finally perform a forward longitudinal simulation of the vehicle in the lane based on the target obstacle to obtain the final speed curve. The speed curve (rough solution) obtained by dynamic programming can be subjected to lane forward simulation, thereby reducing the pressure on the downstream speed planning module and improving the speed of speed planning solution. In addition, in the lane forward simulation, the end point constraint can be followed, which can effectively improve the speed of speed planning solution and ensure the solution of emergency braking, cutting in and other scenarios.

[0133] In addition, other structures and functions of the vehicle according to the embodiment of the present application are known to ordinary technicians in the field and will not be elaborated here.

[0134] In one embodiment, a computer device is provided. Figure 6 This is a block diagram of the computer device provided in the embodiment of the present application, refer to Figure 6 The computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the above-mentioned vehicle speed decision method embodiment is implemented. For example, the following steps are performed: obtaining a speed curve of a vehicle dynamic planning;

[0135] According to the speed curve, a longitudinal decision is made for each obstacle, wherein the longitudinal decision includes a following strategy, a surpassing strategy and an ignoring strategy;

[0136] Finding a target obstacle, wherein the target obstacle includes an obstacle selected from obstacles for which a following strategy is made;

[0137] According to the target obstacle, a longitudinal simulation is performed on the vehicle in the forward direction within the lane to obtain a final speed curve.

[0138] The embodiment of the present application further provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and the processor implements the above-mentioned vehicle speed decision method embodiment when executing the computer program. For example, the following steps are performed: obtaining a speed curve of a vehicle dynamic planning;

[0139] According to the speed curve, a longitudinal decision is made for each obstacle, wherein the longitudinal decision includes a following strategy, a surpassing strategy and an ignoring strategy;

[0140] Finding a target obstacle, wherein the target obstacle includes an obstacle selected from obstacles for which a following strategy is made;

[0141] According to the target obstacle, a longitudinal simulation is performed on the vehicle in the forward direction within the lane to obtain a final speed curve.

[0142] The present application embodiment provides a computer program product, which includes instructions. When the instructions are executed, the method described in the embodiment of the present application is executed. For example, it can be executed Figure 1 The various steps of the vehicle speed decision method shown, for example, are performed: obtaining a speed curve of the vehicle dynamic planning;

[0143] According to the speed curve, a longitudinal decision is made for each obstacle, wherein the longitudinal decision includes a following strategy, a surpassing strategy and an ignoring strategy;

[0144] Finding a target obstacle, wherein the target obstacle includes an obstacle selected from obstacles for which a following strategy is made;

[0145] According to the target obstacle, a longitudinal simulation is performed on the vehicle in the forward direction within the lane to obtain a final speed curve.

[0146] Those of ordinary skill in the art can understand that all or part of the processes in the methods for implementing the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0147] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A vehicle speed decision method, characterized in that: include: Obtain the speed curve of vehicle dynamic planning; According to the speed curve, a longitudinal decision is made for each obstacle, wherein the longitudinal decision includes a following strategy, a surpassing strategy and an ignoring strategy; Finding a target obstacle, wherein the target obstacle includes an obstacle selected from obstacles for which a following strategy is made; According to the target obstacle, a longitudinal simulation is performed on the vehicle in the forward direction within the lane to obtain a final speed curve.

2. The vehicle speed decision method according to claim 1, characterized in that: The making longitudinal decisions on each obstacle according to the speed curve includes: Obtain the minimum and maximum values ​​of the obstacle in the s direction at the minimum time point on the ST graph; The minimum and maximum values ​​of the obstacle in the s direction at the minimum time point on the ST graph are compared with the position of the vehicle in the s direction at the minimum time point, where: When the position of the vehicle in the s direction at the minimum time point is greater than the maximum value, making an overtaking decision on the obstacle; When the position of the ego vehicle in the s direction at the minimum time point is less than the minimum value, or the sum of the position of the ego vehicle in the s direction at the minimum time point and the length of the ego vehicle is less than the maximum value, a following decision is made for the obstacle, otherwise an ignoring strategy is adopted for the obstacle.

3. The vehicle speed decision method according to claim 1, characterized in that: The step of searching for a target obstacle comprises: Traverse each obstacle for which a following decision is made to find the obstacle closest to the ego vehicle as the target obstacle.

4. The vehicle speed decision method according to claim 3, characterized in that: Also includes: If there is no obstacle for which a following decision is made, a virtual obstacle is constructed and used as the target obstacle, wherein the distance between the position of the virtual obstacle and the ego vehicle is greater than a preset distance and the speed of the virtual obstacle is a reference speed of the ego vehicle.

5. The vehicle speed decision method according to claim 1, characterized in that: The method of performing a forward longitudinal simulation of the vehicle in the lane according to the target obstacle to obtain a final speed curve includes: At each moment, calculate the expected distance between the ego vehicle and the target obstacle; Based on the expected distance, calculating the acceleration of the improved intelligent driver model and calculating the acceleration of the isoacceleration inspired model; Comparing the acceleration of the improved intelligent driver model with the acceleration of the equal acceleration inspiration model to obtain the acceleration at time t; According to the acceleration at time t, the vehicle position and the target obstacle position at the next time t+Δt are updated, so as to obtain the final speed curve according to the update result.

6. The vehicle speed decision method according to any one of claims 1 to 5, characterized in that: The step of obtaining a speed curve for vehicle dynamic planning includes: By sampling on the ST graph, multiple feasible solutions for the entire time are forward calculated, and the minimum cost value of each feasible solution among the multiple feasible solutions is obtained by reverse search, and the speed curve corresponding to the minimum cost value is used as the speed curve for vehicle dynamic planning.

7. A vehicle speed decision system, characterized in that: include: An acquisition module is used to obtain a speed curve of a vehicle dynamic planning; A longitudinal decision module, used for making longitudinal decisions on each obstacle according to the speed curve, wherein the longitudinal decision includes a following strategy, a surpassing strategy and an ignoring strategy; A search module, configured to search for a target obstacle, wherein the target obstacle comprises an obstacle selected from obstacles for which a following strategy is made; The speed decision module is used to perform a forward longitudinal simulation of the vehicle in the lane according to the target obstacle to obtain a final speed curve.

8. A vehicle, characterized in that: include: The vehicle speed decision system according to claim 7.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the vehicle speed decision method according to any one of claims 1-6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the vehicle speed decision method according to any one of claims 1 to 6 is implemented.

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