An intelligent vehicle lane-changing trajectory planning method and system on wet and slippery roads

Abstract

The present invention relates to a method and system for planning a lane-changing trajectory on a wet and slippery road surface of an intelligent vehicle, belonging to the technical field of intelligent driving. The method includes: obtaining vehicle state information and environmental information of the currently traveled wet and slippery road; based on the state information and the environmental information, establishing a nonlinear programming model for the optimal lateral and longitudinal acceleration during lane change, and calculating the optimal lateral and longitudinal acceleration during lane change; based on the optimal lateral and longitudinal acceleration during lane change, establishing a trajectory generation model to generate a lane-changing trajectory. This method solves the problem that the existing lane-changing trajectory planning methods for intelligent driving vehicles do not consider extreme road conditions (such as wet and slippery road surfaces in rainy and snowy days), so that they cannot be applied to wet and slippery road surface scenarios. Compared with the existing methods, this method is more applicable to the actual wet and slippery road surface scenarios.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent driving, and particularly relates to a method and a system for planning a lane-changing trajectory of an intelligent vehicle on a wet and slippery road surface. Background Art

[0002] When a vehicle is driving on a road, two common driving behaviors include following a vehicle and changing lanes. Compared with the following behavior, the lane-changing behavior of a vehicle is more complex. And improper lane changes pose a greater safety hazard. When the road environment deteriorates, such as when the road surface is slippery after rain or snow, the friction provided by the road surface decreases, and the driving performance of the vehicle will change to a certain extent, and the possibility of traffic accidents is higher.

[0003] The realization of vehicle lane change mainly includes lane-changing trajectory planning and longitudinal and lateral control of the vehicle. The goal of intelligent vehicle trajectory planning is to calculate a safe trajectory based on the longitudinal and lateral positions, speeds, accelerations and other information of the target vehicle. A vehicle is a complex parameter-coupled system, which generates longitudinal and lateral movements driven by the forces of the tires. The lateral control of the vehicle can be understood as steering control, and the longitudinal control can be understood as speed control in the driving direction. In terms of the lane-changing behavior of the vehicle, it is actually a combination of the lateral and longitudinal controls of the vehicle. Existing research has solved the problem of vehicle lane-changing trajectory planning to a certain extent. However, in the research on vehicle lane-changing trajectory planning methods, in order to facilitate analysis and simplify the solution, the longitudinal and lateral controls are usually designed as two independent controllers. At the same time, most existing research is based on the analysis of the lane-changing behaviors of drivers in highway (mostly seen in ramp merges) scenarios and general road environments, and does not consider the possible influence of side slip, so it is not applicable to extreme road conditions (such as rainy and snowy wet and slippery road surfaces). Summary of the Invention

[0004] In view of the above analysis, the present invention aims to provide a lane-changing trajectory planning method and system suitable for actual wet and slippery road surface scenarios, so as to solve the problem that the existing technology cannot be applied to wet and slippery road scenarios in rainy and snowy days for safe lane changes.

[0005] On the one hand, a method for planning a lane-changing trajectory of an intelligent vehicle on a wet and slippery road surface is provided, which specifically includes the following steps:

[0006] Obtain the vehicle state information and the environmental information of the current wet and slippery road on which it is driving;

[0007] Based on the state information and the environmental information, establish a non-linear programming model for the optimal lane-changing longitudinal and lateral accelerations, and calculate the optimal longitudinal and lateral lane-changing accelerations;

[0008] Based on the optimal lateral and longitudinal lane-changing accelerations, a trajectory generation model is established to generate a lane-changing trajectory. Furthermore, the nonlinear programming model includes an objective function and constraints. The objective function represents the functional relationship between the longitudinal and lateral accelerations of the lane-changing vehicle during the lane-changing process and the state information and the environmental information, respectively. The constraints include the range of values for the longitudinal and lateral accelerations during the lane-changing process, and the requirement that longitudinal safety and lateral stability requirements be met during the lane-changing process.

[0009] Furthermore, the status information includes status information of the lane-changing vehicle, status information of the lagging vehicle in the target lane, and status information of the leading vehicle in the current lane.

[0010] Furthermore, when the actual distance between the lane-changing vehicle and the trailing vehicle in the target lane is greater than or equal to the safety distance, and the actual distance between the lane-changing vehicle and the leading vehicle in the current lane is greater than or equal to the safety distance, it is determined that the lane-changing longitudinal safety requirement is met.

[0011] Furthermore, when the tire force saturation factor of each tire of the lane-changing vehicle is less than the tire force saturation threshold, it is considered that the vehicle will not slip laterally and is judged to meet the lane-changing lateral stability requirement.

[0012] Furthermore, the tire force saturation factor of the kth tire of the lane-changing vehicle is calculated using the following formula:

[0013]

[0014] Among them, λ TFSCk is the tire force saturation factor of the kth tire of the lane-changing vehicle, F xk 、F yk 、F zk represent the longitudinal, lateral and vertical tire forces of the kth tire of the lane-changing vehicle, respectively, and F ymaxk represents the maximum lateral tire force of the kth tire of the vehicle, and μ is the friction coefficient of the wet road surface.

[0015] Furthermore, the objective function of the nonlinear programming model includes:

[0016]

[0017] in, Indicates the relationship between the relative speed difference between the lane-changing vehicle and the trailing vehicle in the target lane and the longitudinal acceleration that the lane-changing vehicle should adopt during the lane-changing process; a x (t) is the longitudinal acceleration of the lane-changing vehicle; Δv1 refers to the relative speed difference between the lane-changing vehicle and the trailing vehicle; v ego (0), v r (0) represents the initial speed of the lane-changing vehicle and the lagging vehicle respectively; v finis the speed of the lane-changing vehicle at the end point of lane change; a r (t) is the longitudinal acceleration of the trailing vehicle; x ego (0), x r (0) represent the longitudinal initial positions of the lane-changing vehicle and the trailing vehicle respectively; l ego is the length of the lane-changing vehicle;

[0018] represents the relationship between the relative speed difference between the lane-changing vehicle and the leading vehicle in the current lane and the lateral acceleration that the lane-changing vehicle should adopt during the lane-changing process; a y (t) is the lateral acceleration of the lane-changing vehicle; Δv2 refers to the relative speed difference between the lane-changing vehicle and the leading vehicle; v f (t) represents the speed of the leading vehicle; W is the lane width; x f (0) is the longitudinal initial position of the leading vehicle; l f is the length of the leading vehicle.

[0019] Furthermore, generating the lane-changing trajectory by using the trajectory generation model includes:

[0020] Generating the lane-changing trajectory of the lane-changing vehicle by using the sine lane-changing trajectory function based on the optimal lateral and longitudinal lane-changing accelerations.

[0021] Furthermore, the sine lane-changing trajectory function y sin (x ego (t)) is characterized as:

[0022]

[0023] wherein, x ego (t) represents the longitudinal position of the lane-changing vehicle; t represents the time step.

[0024] On the other hand, the present invention also provides an intelligent vehicle lane-changing trajectory planning system on a wet road surface, including:

[0025] An information acquisition module, configured to acquire vehicle state information and environmental information of the current wet road surface on which the vehicle is traveling;

[0026] An optimal lateral and longitudinal lane-changing acceleration calculation module, configured to establish a non-linear programming model of the optimal lane-changing lateral and longitudinal accelerations based on the state information and the environmental information, and calculate the optimal lateral and longitudinal lane-changing accelerations;

[0027] A lane-changing trajectory generation module, configured to establish a trajectory generation model to generate a lane-changing trajectory based on the optimal lateral and longitudinal lane-changing accelerations.

[0028] The present invention can at least achieve one of the following beneficial effects:

[0029] 1. By considering the environmental information of a slippery road and taking into account the lateral and longitudinal coupling effects of the tires during vehicle movement, a model is established to transform the actual application scenario problem into a solvable mathematical problem, thus solving the problem that the existing vehicle lane-changing trajectory planning is not applicable to the slippery road surface scenario;

[0030] 2. Through the objective function and constraint conditions of the designed non-linear programming model, the optimal lateral and longitudinal lane-changing accelerations during the lane-changing process are calculated to accurately plan the lane-changing trajectory, fully ensuring the safety of lane-changing on a slippery road surface;

[0031] 3. Through the lane-changing trajectory generation module, a lane-changing trajectory is generated based on the optimal lateral and longitudinal lane-changing accelerations, solving the problem of vehicle lane-changing trajectory planning based on the estimation of collision and sideslip coupling risks, and improving the drawbacks of the existing research that designs two separate lateral and longitudinal planning controllers to plan the lane-changing trajectory respectively.

[0032] Other features and advantages of the present invention will be described in the following specification, and some advantages can be made obvious from the specification, or can be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained from the content specifically pointed out in the specification, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings are only for the purpose of showing specific embodiments and are not considered as limiting the present invention. Throughout the drawings, the same reference signs represent the same components.

[0034] Figure 1 It is a schematic flowchart of the method according to the embodiment of the present invention;

[0035] Figure 2 It is a schematic diagram of the lane-changing scenario of an intelligent vehicle on a slippery road surface according to an embodiment of the present invention;

[0036] Figure 3 It is the vehicle lateral acceleration under different algorithms according to the embodiment of the present invention;

[0037] Figure 4 It is the vehicle tire force saturation factor under different algorithms according to the embodiment of the present invention;

[0038] Figure 5 It is the vehicle lane-changing trajectory under different algorithms according to the embodiment of the present invention;

[0039] Figure 6 It is the relative distance between the lane-changing vehicle and the leading and trailing vehicles during lane-changing under different algorithms according to the embodiment of the present invention;

[0040] Figure 7 It is the structural diagram of the intelligent vehicle slippery road surface lane-changing trajectory planning system according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The preferred embodiments of the present invention will be specifically described below with reference to the accompanying drawings. The accompanying drawings form a part of this application and are used together with the embodiments of the present invention to explain the principles of the present invention, rather than to limit the scope of the present invention.

[0042] Method Embodiment

[0043] An intelligent vehicle lane-changing trajectory planning method in this embodiment studies a lane-changing scenario of an intelligent vehicle on a wet and slippery road surface as shown in Figure 2 The following:

[0044] (1) The lane-changing vehicle V ego Is located at the lane-changing starting point of the vehicle in the current lane;

[0045] (2) During the lane-changing process, the lane-changing vehicle performs a uniform acceleration motion longitudinally (the vehicle traveling direction) and a variable acceleration motion laterally (the direction perpendicular to the vehicle traveling direction); considering that the lane-changing time is short and the longitudinal safety can be maximally guaranteed, it can be considered that the leading vehicle V in the current lane f Performs a uniform motion during the lane-changing time, and the trailing vehicle V in the target lane r Performs an acceleration motion during the lane-changing time;

[0046] (3) At the end of the lane change, the speed of the lane-changing vehicle needs to be the sum of the speed of the trailing vehicle in the target lane and a certain gain value to prevent traffic accidents caused by the lane-changing vehicle suddenly changing lanes and the trailing vehicle in the target lane braking;

[0047] (4) The lane-changing vehicle is a vehicle with intelligent driving functions and can obtain vehicle state information and environmental information of the current wet and slippery road on which it is traveling.

[0048] The lane-changing trajectory planning method includes the following steps:

[0049] Step 1: Obtain vehicle state information and environmental information of the current wet and slippery road on which it is traveling;

[0050] Specifically, the vehicle state information includes the state information of the lane-changing vehicle (Table 1), the state information of the trailing vehicle in the target lane (Table 2), and the state information of the leading vehicle in the current lane (Table 3); the state information of the above vehicles can be obtained through the on-vehicle sensing device of the lane-changing vehicle or road monitoring facilities. The environmental information includes the road surface friction coefficient μ and the lane width W; among them, the road surface friction coefficient μ can be obtained through the on-vehicle sensing device.

[0051] Table 1 Lane-changing vehicle state information

[0052] Parameter Description <![CDATA[x ego (0)]]> Initial longitudinal position information of the lane-changing vehicle <![CDATA[v ego (0)]]> Initial speed of the lane-changing vehicle <![CDATA[l ego > Length of the lane-changing vehicle

[0053] Table 2 State information of the trailing vehicle in the target lane

[0054] <![CDATA[x r (0)]]> Initial longitudinal position information of the trailing vehicle <![CDATA[a r (t)]]> Longitudinal acceleration of the trailing vehicle <![CDATA[v r (0)]]> Initial speed of the trailing vehicle

[0055] Table 3 Status information of the leading vehicle in the current lane

[0056] <![CDATA[x f (0)]]> Initial longitudinal position information of the leading vehicle <![CDATA[v f (t)]]> Speed of the leading vehicle <![CDATA[l f > Length of the leading vehicle

[0057] Step 2: Based on the status information, environmental information, and the established non - linear programming model for the optimal lateral and longitudinal lane - changing accelerations, calculate the optimal lateral and longitudinal lane - changing accelerations. Specifically, the non - linear programming model includes an objective function and constraint conditions.

[0058] The established non - linear programming model is expressed as follows:

[0059]

[0060] Among them, is the objective function; max M represents solving the maximum values of the lateral and longitudinal lane - changing accelerations for the objective function; α1 and α2 are weight coefficients, α1 + α2 = 1, and the selection of the values of the weight coefficients is related to the driver's emphasis on longitudinal safety and lateral stability. Exemplarily, in this embodiment, both are set to 0.5.

[0061] Furthermore, the objective function is used to characterize the functional relationships between the longitudinal and lateral accelerations of the lane - changing vehicle during lane - changing and the status information and environmental information, as follows:

[0062]

[0063] Among them, represents the relationship between the relative speed difference between the lane - changing vehicle and the trailing vehicle in the target lane during lane - changing and the longitudinal acceleration adopted by the lane - changing vehicle; a x (t) is the longitudinal acceleration of the lane - changing vehicle; Δv1 refers to the relative speed difference between the lane - changing vehicle and the trailing vehicle; x ego (t), x r (t) respectively represent the longitudinal positions of the lane - changing vehicle and the trailing vehicle; l ego is the length of the lane - changing vehicle; v ego (0), v r (0) respectively represent the initial speeds of the lane - changing vehicle and the trailing vehicle; t represents the time step; a r (t) is the longitudinal acceleration of the trailing vehicle; x ego (0), x r (0) respectively represent the longitudinal initial positions of the lane - changing vehicle and the trailing vehicle; v fin is the speed of the lane - changing vehicle at the end point of lane - changing;

[0064]

[0065] Among them, represents the relationship between the relative speed difference between the lane-changing vehicle and the leading vehicle in the current lane and the lateral acceleration adopted by the lane-changing vehicle during the lane-changing process; a y (t) is the lateral acceleration of the lane-changing vehicle; Δv2 refers to the relative speed difference between the lane-changing vehicle and the leading vehicle; x f (t) represents the longitudinal position of the leading vehicle; l f is the length of the leading vehicle; v f (t) represents the speed of the leading vehicle; x f (0) is the longitudinal initial position of the leading vehicle; l f is the length of the leading vehicle; W is the lane width.

[0066] In this scenario, the vehicle behind in the target lane accelerates, and the leading vehicle in the current lane travels at a constant speed. The lateral acceleration of the lane-changing vehicle mainly considers not colliding with the vehicle in front. Therefore, the lateral acceleration is related to the speed difference and distance from the vehicle in front. At the same time, based on the calculation principle of the lateral acceleration of the sine function curve, it can be deduced that:

[0067]

[0068] Considering that the lane-changing trajectory planning needs to consider the longitudinal and lateral accelerations that the vehicle itself can achieve to ensure the feasibility of the lane-changing trajectory, and the safety during the lane-changing process, the constraint conditions include the value ranges of the longitudinal and lateral accelerations during the lane-changing process, and the requirements of longitudinal safety and lateral stability during the lane-changing process must be met.

[0069] The value ranges of the longitudinal and lateral accelerations are characterized as:

[0070] a x,min ≤a x (t)≤a x,max ,a y,min ≤a y (t)≤a y,max

[0071] Among them, a x,min and a x,max are the maximum and minimum values of the longitudinal acceleration of the lane-changing vehicle; a y,min and a y,max are the maximum and minimum values of the lateral acceleration of the lane-changing vehicle. By restricting the value ranges of the longitudinal and lateral accelerations, the feasibility of the lane-changing trajectory planned for the lane-changing vehicle is ensured.

[0072] During the lane change process, the longitudinal safety of the lane change needs to be satisfied. Specifically, when the actual distance between the lane-changing vehicle and the vehicle behind in the target lane is greater than or equal to the safety distance, and the actual distance between the lane-changing vehicle and the vehicle in the lead in the current lane is greater than or equal to the safety distance, it is determined that the longitudinal safety requirements for the lane change are met.

[0073] This embodiment is applied to a wet road scenario, and it is necessary to model the lane change scenario based on the existing Gipps safety distance model. The Gipps safety distance model is characterized by the following formula:

[0074]

[0075] Among them, V f represents the leading vehicle; V r represents the trailing vehicle; F(V f , V r ) represents the relative safety distance between the leading vehicle and the trailing vehicle; v f (t), v r (t) represent the speeds of the leading vehicle and the trailing vehicle respectively; a f,brake , a r,brake represent the maximum braking decelerations of the leading vehicle and the trailing vehicle respectively; ρ represents the driver's reaction time.

[0076] According to the above Gipps safety distance model, the safety distance between the lane-changing vehicle and the trailing vehicle in the target lane should satisfy the following formula:

[0077]

[0078] Among them, F1(t) represents the safety distance between the lane-changing vehicle and the trailing vehicle in the target lane; v r (t) and v ego (t) represent the speeds of the trailing vehicle and the lane-changing vehicle respectively; a ego,brake , a r,brake represent the maximum braking decelerations of the lane-changing vehicle and the trailing vehicle respectively; ρ represents the driver's reaction time, which can be set to 1 second; t represents the time step.

[0079] The safety distance between the lane-changing vehicle and the leading vehicle in the current lane should satisfy the following formula:

[0080]

[0081] Among them, F2(t) represents the safety distance between the lane-changing vehicle and the leading vehicle in the current lane; v f (t) represents the speed of the leading vehicle; a f,brake represents the maximum braking deceleration of the leading vehicle.

[0082] To determine whether the longitudinal safety of lane change is satisfied, calculate the safety distance S1(t) between the lane-changing vehicle and the vehicle lagging behind in the target lane, and the actual distance S2(t) between the lane-changing vehicle and the vehicle leading in the current lane, as shown below respectively:

[0083]

[0084] where x ego (t), x r (t), x f (t) are the longitudinal positions of the lane-changing vehicle, the vehicle lagging behind in the target lane, and the vehicle leading in the current lane respectively; l ego , l f are the vehicle lengths of the lane-changing vehicle and the vehicle leading in the current lane respectively; v ego (0), v r (0) represent the initial speeds of the lane-changing vehicle and the lagging vehicle respectively; v f (t) represents the speed of the leading vehicle; a x (t), a r (t) represent the longitudinal accelerations of the lane-changing vehicle and the lagging vehicle respectively.

[0085] To simplify the above formula form, let:

[0086] C1 = x ego (0) - x r (0) - l ego

[0087]

[0088] C3 = v ego (0) - v r (0)

[0089]

[0090] D1 = x f (0) - x ego (0) - l f

[0091]

[0092] D3 = v f (0) - v ego (0)

[0093]

[0094] To satisfy S1(t) ≥ F1(t) and S2(t) ≥ F2(t), substitute the above formulas and derive and simplify to obtain the following lane-changing longitudinal safety constraints:

[0095]

[0096] During the lane-changing process, the lateral stability during lane-changing needs to be satisfied. In this embodiment: when the tire force saturation factors of all tires of the lane-changing vehicle are less than the tire force saturation threshold, it is considered that the vehicle will not skid laterally, and it is judged that the requirement of lateral stability during lane-changing is satisfied.

[0097] The tire force saturation factor is an index used to measure whether there will be a lateral slip that causes instability during the lane-changing process of the vehicle. If the tire force saturation factor exceeds or is equal to the threshold, the lateral movement will cause the vehicle to become unstable, and in this case, the vehicle should keep driving straight; if the tire force saturation factor is less than the threshold, the vehicle can perform a lane-changing operation, and in this case, the lateral stability requirement is satisfied.

[0098] The tire force saturation factor λ of the k-th tire of the lane-changing vehicle is calculated using the following formula TFSCk :

[0099]

[0100] where λ TFSC is the tire force saturation factor of the k-th tire of the lane-changing vehicle; F xk 、F yk 、F zk respectively represent the longitudinal, lateral, and vertical tire forces of the k-th tire of the lane-changing vehicle, which are obtained by the vehicle sensing device; F ymaxk represents the maximum lateral tire force of the k-th tire of the vehicle, which is known data obtained based on experiments; μ is the friction coefficient of the wet and slippery road surface.

[0101] Take the maximum value of all tire force saturation factors:

[0102]

[0103] The tire force saturation threshold is determined to be 1 through experiments. When λ TFSCmax < 1, the vehicle is laterally safe.

[0104] Through the objective function and constraint conditions described above, the optimal lateral and longitudinal accelerations during lane-changing are calculated using a nonlinear programming model. Specifically, in this embodiment, the sequential unconstrained minimization technique (SUMT), genetic algorithm (GA), and gradient descent method (GD) can be used to solve the nonlinear programming problem of lane-changing trajectory planning. The numerical values of the vehicle state information are shown in Table 4, and the friction coefficient of the wet and slippery road surface is set to 0.3, which is different from the friction coefficient of the normal road surface (about 0.85). The calculation results are shown in Table 5. The lateral acceleration and tire force saturation coefficient of the lane-changing vehicle obtained by the different algorithms are as Figure 3 、 4 shown.

[0105] Table 4 Numerical values of vehicle state information

[0106]

[0107]

[0108] Table 5 Optimal lateral and longitudinal accelerations obtained by different algorithms for lane change

[0109] Solution algorithm <![CDATA[Lateral acceleration (m / s 2 )]]> <![CDATA[Longitudinal acceleration (m / s 2 )]]> SUMT 0.6946 1.1783 GA 0.7035 1.1944 GD 0.6989 1.1929

[0110] From the above data, it can be seen that the differences in the optimal lateral acceleration and longitudinal acceleration obtained by the three algorithms are small, and all three algorithms are suitable for solving the optimal lateral and longitudinal accelerations.

[0111] Step 3: Based on the optimal lateral and longitudinal lane-changing accelerations, establish a trajectory generation model to generate a lane-changing trajectory. Specifically:

[0112] Through the above steps, the optimal lateral and longitudinal lane-changing accelerations of each algorithm can be determined based on the time step. After determining the optimal lateral and longitudinal accelerations of vehicle lane change, use the sine lane-changing trajectory function to generate the lane-changing trajectory of the vehicle. Assuming that the longitudinal and lateral accelerations of the vehicle at the initial and end states of lane change are 0, the time for the vehicle to complete lane change can be determined according to the lateral and longitudinal accelerations, and the lateral and longitudinal coordinates of the vehicle at each time step can be further determined. The lane-changing trajectory y sin (x ego (t)) can be generated by the following formula.

[0113]

[0114] The driving trajectories of the lane-changing vehicles obtained by the different algorithms are as Figure 5 shown. During the lane-changing process, the relative distances between the lane-changing vehicle and the leading and trailing vehicles are as Figure 6 shown. Under the different algorithms, the generated vehicle lane-changing trajectories are smooth and continuous, and the fluctuations of the lateral acceleration of vehicle lane change are small, indicating that the lane-changing trajectory planning method described in this embodiment has good effects.

[0115] System embodiment

[0116] An intelligent vehicle lane-changing trajectory planning system for wet roads includes: an information acquisition module, an optimal lateral and longitudinal lane-changing acceleration calculation module, and also includes a lane-changing trajectory generation module.

[0117] The information acquisition module is used to acquire vehicle state information and environmental information of the current wet road being traveled; among them, the specific information acquired and the specific process of acquiring information can refer to the corresponding descriptions in the above method embodiment.

[0118] The optimal lateral and longitudinal lane-changing acceleration calculation module is used to establish a non-linear programming model for the optimal lateral and longitudinal lane-changing acceleration based on the state information and the environmental information, and calculate the optimal lateral and longitudinal lane-changing acceleration; wherein, the non-linear programming model, its objective function, constraints, and solution process can refer to the corresponding descriptions in the above method embodiments.

[0119] The lane-changing trajectory generation module is used to generate a lane-changing trajectory by establishing a trajectory generation model based on the optimal lateral and longitudinal lane-changing acceleration. Among them, the trajectory generation model and the driving trajectories of the lane-changing vehicle obtained by different algorithms can refer to the corresponding descriptions in the above method embodiments.

[0120] Since the intelligent vehicle lane-changing trajectory planning system on a wet road surface and the intelligent vehicle lane-changing trajectory planning method on a wet road surface are based on the same inventive concept and can draw on each other in relevant aspects, the same technical effects can be achieved.

[0121] As mentioned above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A lane-changing trajectory planning method for intelligent vehicles on slippery roads, characterized in that, It includes the following steps: Obtain the vehicle state information and the environmental information of the current wet road being traveled; Based on the state information, the environmental information, and the established nonlinear programming model of the optimal longitudinal and lateral lane-changing accelerations, calculate the optimal longitudinal and lateral lane-changing accelerations; the nonlinear programming model includes an objective function and constraint conditions; The objective function is used to characterize the functional relationships between the longitudinal and lateral accelerations of the lane-changing vehicle during lane change and the state information and the environmental information respectively, and the constraint conditions include the value ranges of the longitudinal and lateral accelerations during the lane-changing process and the requirements of longitudinal safety and lateral stability during the lane-changing process must be satisfied; The objective function of the nonlinear programming model includes: Among them, represents the relationship between the relative speed difference between the lane-changing vehicle and the lagging vehicle in the target lane during the lane-changing process and the longitudinal acceleration that the lane-changing vehicle should adopt; a x (t) is the longitudinal acceleration of the lane-changing vehicle; Δv1 refers to the relative speed difference between the lane-changing vehicle and the lagging vehicle; v ego (0), v r (0) respectively represent the initial speeds of the lane-changing vehicle and the lagging vehicle; v fin is the speed of the lane-changing vehicle at the end point of the lane change; a r (t) is the longitudinal acceleration of the lagging vehicle; x ego (0), x r (0) respectively represent the longitudinal initial positions of the lane-changing vehicle and the lagging vehicle; l ego is the length of the lane-changing vehicle; It represents the relationship between the relative speed difference between the lane-changing vehicle and the leading vehicle in the current lane during the lane-changing process and the lateral acceleration that the lane-changing vehicle should adopt; a y (t) is the lateral acceleration of the lane-changing vehicle; Δv2 refers to the relative speed difference between the lane-changing vehicle and the leading vehicle; v f (t) represents the speed of the leading vehicle; W is the lane width; x f (0) is the longitudinal initial position of the leading vehicle; l f is the length of the leading vehicle; Based on the optimal longitudinal and lateral lane-changing accelerations, establish a trajectory generation model to generate a lane-changing trajectory.

2. The lane-changing trajectory planning method according to claim 1, wherein The state information includes the state information of the lane-changing vehicle, the state information of the vehicle behind in the target lane, and the state information of the vehicle in the lead in the current lane.

3. The lane-changing trajectory planning method according to claim 2, wherein When the actual distance between the lane-changing vehicle and the vehicle behind in the target lane is greater than or equal to the safety distance, and the actual distance between the lane-changing vehicle and the vehicle in the lead in the current lane is greater than or equal to the safety distance, it is determined that the requirement of longitudinal safety for lane change is met.

4. The lane change trajectory planning method according to claim 3, wherein When the tire force saturation factor of each tire of the lane-changing vehicle is less than the tire force saturation threshold, it is considered that the vehicle will not skid laterally, and it is determined that the requirement of lateral stability for lane change is met.

5. The lane-changing trajectory planning method according to claim 4, wherein Use the following formula to calculate the tire force saturation factor of the kth tire of the lane-changing vehicle: Among them, λ TFSCk is the tire force saturation factor of the k-th tire of the lane-changing vehicle, F xk , F yk , F zk respectively represent the longitudinal, lateral and vertical tire forces of the k-th tire of the lane-changing vehicle, F ymaxk represents the maximum lateral tire force of the k-th tire of the vehicle, and μ is the friction coefficient of the wet road surface.

6. The lane-changing trajectory planning method according to claim 5, characterized in that Using the trajectory generation model to generate a lane-changing trajectory includes: Based on the optimal longitudinal and lateral lane-changing accelerations, use the sine lane-changing trajectory function to generate the lane-changing trajectory of the lane-changing vehicle.

7. The lane-changing trajectory planning method according to claim 6, wherein The sine lane-changing trajectory function y sin (x ego (t)) is characterized as: where x ego (t) represents the longitudinal position of the lane-changing vehicle; t represents the time step.

8. An intelligent vehicle lane-changing trajectory planning system on a wet road surface, characterized in that, It includes: An information acquisition module, which is used to obtain the vehicle state information and the environmental information of the current wet road being traveled; An optimal longitudinal and lateral lane-changing acceleration calculation module, which is used to establish a nonlinear programming model of the optimal longitudinal and lateral lane-changing accelerations based on the state information and the environmental information, and calculate the optimal longitudinal and lateral lane-changing accelerations; the nonlinear programming model includes an objective function and constraint conditions; the objective function is used to characterize the functional relationships between the longitudinal and lateral accelerations of the lane-changing vehicle during lane change and the state information and the environmental information respectively, and the constraint conditions include the value ranges of the longitudinal and lateral accelerations during the lane-changing process and the requirements of longitudinal safety and lateral stability during the lane-changing process must be satisfied; The objective function of the nonlinear programming model includes: Among them, f S1 (a x (t), Δv1) represents the relationship between the relative speed difference between the lane-changing vehicle and the trailing vehicle in the target lane during the lane-changing process and the longitudinal acceleration that the lane-changing vehicle should adopt; a x (t) is the longitudinal acceleration of the lane-changing vehicle; Δv1 refers to the relative speed difference between the lane-changing vehicle and the trailing vehicle; v ego (0), v r (0) represent the initial speeds of the lane-changing vehicle and the trailing vehicle respectively; v fin is the speed of the lane-changing vehicle at the end point of the lane change; a r (t) is the longitudinal acceleration of the trailing vehicle; x ego (0), x r (0) represent the longitudinal initial positions of the lane-changing vehicle and the trailing vehicle respectively; l ego is the length of the lane-changing vehicle; It represents the relationship between the relative speed difference between the lane-changing vehicle and the leading vehicle in the current lane during the lane-changing process and the lateral acceleration that the lane-changing vehicle should adopt; a y (t) is the lateral acceleration of the lane-changing vehicle; Δv2 refers to the relative speed difference between the lane-changing vehicle and the leading vehicle; v f (t) represents the speed of the leading vehicle; W is the lane width; x f (0) is the longitudinal initial position of the leading vehicle; l f is the length of the leading vehicle; A lane-changing trajectory generation module, which is used to establish a trajectory generation model to generate a lane-changing trajectory based on the optimal longitudinal and lateral lane-changing accelerations.

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