Electric vehicle lane changing path planning method and system suitable for variable adhesion coefficient road surface

Abstract

The invention discloses an electric vehicle lane changing path planning method and system suitable for a variable adhesion coefficient road surface, and relates to the technical field of electric vehicle unmanned driving. The method comprises the following steps: receiving road adhesion information of a to-be-changed area and information of surrounding obstacle vehicles; introducing concepts of lane changing time and offset rate to define different lane changing paths, and generating a basic lane changing path cluster based on a quintic Bezier curve; proposing a path stability criterion in a curvature form and a lane changing time form based on a vehicle stability domain; generating a stable lane changing path cluster containing a symmetric path and an asymmetric path by using a path stability criterion and road surface attachment information; and generating a lane changing path meeting optimal comfort, stability, safety and efficiency based on an approximate ideal solution sorting algorithm. According to the method, the change of the road surface attachment information in the future lane changing area is considered, the stability of the vehicle during lane changing under the road surface with the variable attachment coefficient is ensured by using the asymmetric lane changing path, and the lane changing safety of the vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned driving of electric vehicles, and in particular to a lane-changing path planning method and system for electric vehicles suitable for variable adhesion coefficient roads. Background Art

[0002] A safe and effective path planning algorithm is an important guarantee for improving lane changing safety in high-speed scenarios. Lane changing path planning is based on the traffic environment information obtained by the intelligent driving perception layer and the lane changing instructions issued by the decision-making layer to plan a safe driving path that the vehicle can follow. In high-speed scenarios, due to the complex and changeable road adhesion conditions and the time-varying characteristics of the vehicle dynamics stability boundary, lane changing at higher speeds is prone to unstable phenomena such as vehicle skidding and tail swinging. The path planning algorithm must strictly consider the surrounding environment information and the vehicle's dynamic stability to ensure that the planned path can meet safety requirements. Therefore, the lane changing path planning algorithm in high-speed traffic scenarios is crucial to the driving safety of unmanned vehicles. In the few existing lane changing path planning algorithms that consider vehicle dynamics constraints, the impact of changes in the road adhesion coefficient on the stability of the vehicle during lane changing is ignored, which easily leads to lane changing instability. For this reason, the present invention proposes a lane changing path planning method and system for electric vehicles suitable for variable adhesion coefficient roads. Summary of the Invention

[0003] The purpose of the present invention is to provide a lane-changing path planning method and system for electric vehicles suitable for variable adhesion coefficient roads, which fully considers the impact of changes in road adhesion coefficient on vehicle stability during lane changing and improves the safety of vehicle lane changing in high-speed scenarios.

[0004] According to a first aspect of the present invention, in order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a lane change path planning method for an electric vehicle on a road with a variable adhesion coefficient, comprising the following steps: Receive road adhesion information of the area to be changed and information of surrounding obstacle vehicles; Based on quintic Bezier curve modeling, the concept of lane change time and offset rate is introduced to define different lane change paths and generate basic lane change path clusters. The zero-line boundary stability domain is used to describe the vehicle dynamic instability boundary, and path stability criteria in the form of curvature and lane-changing time are proposed based on the vehicle boundary stability domain. Based on the path stability criterion and road adhesion information, a stable lane-changing path cluster is obtained from the basic lane-changing path clusters. By judging the intersection of vehicle contours, a safe lane-changing path cluster without collision is obtained from the stable lane-changing path cluster. The performance evaluation index of each path in the safe lane-changing path cluster is calculated, and the optimal lane-changing path under the performance evaluation index is generated based on the approximate ideal solution sorting algorithm.

[0005] Furthermore, the road adhesion information of the lane-changing area includes the road adhesion coefficient before the mutation, the road adhesion coefficient after the mutation, and the docking position, and the information of the surrounding obstacle vehicles includes the spatial position, speed, and acceleration of the surrounding obstacle vehicles.

[0006] Furthermore, based on quintic Bezier curve modeling, the concepts of lane change time and offset rate are introduced to define different lane change paths and generate basic lane change path clusters, as follows: (31) The lane-changing path of the vehicle is described using a quintic Bezier curve: (1) Where u represents the parameter of the Bezier curve, ranging from 0 to 1; superscripts 1, 2, 3, 4, and 5 are powers of u; the control points satisfy the following conditions: P0, P1, and P2 are collinear, and P3, P4, and P5 are collinear. P1 and P4 are the midpoints of the line segments P0P2 and P3P5, respectively, and P2 and P3 have the same horizontal coordinates. The intersection of the line connecting points P2 and P3 and the path is defined as M, with coordinates (L m , 0.5W); define the P0M segment on the path as the front segment, the MP5 segment as the back segment, and the longitudinal coordinate of point M as L m The values are: (2) Where, L is the vertical coordinate of P5, α is the offset, which is used to quantitatively describe the degree of offset of the longitudinal coordinate of point M relative to 0.5L; when α = 0, point M is the midpoint of the path, and the front and back sections of the path are symmetrical. α When <0, point M moves to the left and the front path is compressed. Conversely, point M moves to the right and the back path is compressed. Therefore, the offset affects the shape of the path. (32) The longitudinal displacement of the generated path is larger than the lateral displacement, so it is assumed that: (3) Where, v x is the vehicle speed, so if the vehicle speed remains unchanged, the lane change time τ Changes in affect the shape of the path; Therefore, by adjusting the lane change time τ and offset α A series of paths can be generated and defined as basic path clusters; paths with zero offset are defined as symmetric paths, and paths with non-zero offset are defined as asymmetric paths.

[0007] Furthermore, the zero-line boundary stability domain is used to describe the vehicle dynamic instability boundary. Based on the vehicle boundary stability domain, path stability criteria in the form of curvature and lane-changing time are proposed, as follows: (41) The zero-line boundary stability domain is used to describe the vehicle dynamic instability boundary, and its boundary expression is as follows: (4) Where, β and γ are the vehicle's center of mass sideslip angle and yaw rate respectively; i , Θ i , i =1, 2 are parameters for determining the boundary equation of the stable region; (42) Let the mass be m The longitudinal speed of the vehicle in the geodetic coordinate system is v x , which can track the planned path, and the curvature of the path point corresponding to time k is ρ , which can be calculated according to formula (1), then the reference center of mass sideslip angle and reference yaw rate of the vehicle at this time are expressed as: (5) Where, and are the distances from the vehicle's center of mass to the front and rear axles, is the cornering stiffness of the vehicle's rear wheels; If the trajectory of the vehicle reference center of mass sideslip angle and reference yaw rate calculated in formula (5) does not exceed the zero line boundary stability region, the vehicle is stable; therefore, the combined formula (5) can obtain the path stability criterion in the form of curvature: ,in, and are the upper and lower bounds of the curvature, which are functions of vehicle speed and road adhesion coefficient. The faster the vehicle speed or the smaller the road adhesion coefficient, the closer the value is to 0. If the curvature of each point on the lane change path satisfies the path stability criterion in the form of curvature, then the lane change path is stable, otherwise it is unstable.

[0008] (43) When the road adhesion coefficient and vehicle speed remain unchanged, the path that enables the vehicle to complete the lane change fastest can be obtained according to the curvature form of the path stability criterion in (42). The corresponding time is defined as the minimum lane change time, so the path stability criterion in the form of lane change time can be obtained: A path with a lane-changing time greater than the minimum lane-changing time is a stable path; a path with a lane-changing time less than the minimum lane-changing time is an unstable path.

[0009] Furthermore, based on the path stability criterion and road adhesion information, a stable lane change path cluster is obtained from the basic lane change path cluster, as follows: Assume that the distance in front of the vehicle is s meters and the adhesion coefficient is The high adhesion road surface, after which the adhesion coefficient is reduced to low adhesion road surface, s is the docking position; (51) Using the path stability criterion in the form of lane change time, a stable symmetric path is selected from the symmetric path cluster to generate a stable symmetric path cluster: According to the path stability criterion in the form of lane change time, there are two ways to achieve stable lane change in the form of a symmetrical path: Method 1: Based on the high-pressure road surface, a path is planned that can complete the lane change before entering the road with low adhesion coefficient; Method 2: Complete lane change based on low-lying road surface planning path; If the docking location s is far away, use method 1; if the docking location is close, use method 2; (52) Generate a stable symmetric path according to method 1 and method 2 based on the docking position s: (52.1) Assume that the vehicle speed is constant and known when it is traveling at high speed, the docking position s, the adhesion coefficient of the high adhesion road surface and low adhesion road adhesion coefficient Given: According to the path stability criterion in the form of lane change time, calculate and Corresponding minimum lane change time and , according to the speed-displacement relationship, when the lane-changing longitudinal distance is s, the lane-changing time is ; Considering the symmetrical paths with different lane changing times, according to and and There are three situations: Situation 1: ; Case 2: ; Case 3: ; (52.2) Based on the path stability criterion in the form of lane change time and two stable path planning methods, stable paths are determined for different situations: in situation 1, the stable path is generated based on method 2; in situations 2 and 3, the stable paths are generated based on methods 1 and 2. Stable symmetric path clusters are generated by judging the corresponding situation; (53) Using the curvature-based path stability criterion, a stable asymmetric path is selected from the asymmetric path cluster to generate a stable asymmetric path cluster: (53.1) Since the symmetrical path results in low road utilization, which is mainly reflected in Cases 1 and 2, an asymmetrical path is used to fill the space, which is defined as supplementary space; (53.2) According to the curvature-based path stability criterion, a high-adhesion road surface has a larger maximum curvature that can support path stability than a low-adhesion road surface. Therefore, the following method is used: Method 3: Perform aggressive steering on high-adhesion roads, then switch to gentle steering when or before entering low-adhesion roads until the lane change is completed; (53.3) Using method 3, we can sample asymmetric lane-changing paths from the supplementary space by adjusting the offset rate, and then determine their stability based on the path stability criterion in the form of curvature, thereby generating a stable asymmetric path cluster: Take any path with a lane change time from the supplementary space and adjust the offset rate α ,make α <0, several paths with the same lane-changing time but different offset rates are obtained. The curvature value of each point on each path is calculated one by one. The path stability criterion in the form of curvature can be used to determine whether the path is stable. Finally, all stable paths constitute an asymmetric stable lane-changing path cluster; All stable symmetric path clusters and stable asymmetric path clusters constitute a stable lane-changing path cluster.

[0010] Furthermore, by judging the intersection of vehicle contours, a safe lane-changing path cluster without collision is obtained from the stable lane-changing path cluster, as follows: (61) The vehicle in front of the vehicle in the same lane as the ego vehicle is called the leading vehicle, the vehicle in front of the vehicle in the adjacent lane is called the leading vehicle in the adjacent lane, and the vehicle behind the vehicle is called the trailing vehicle in the adjacent lane. The traffic environment is set as follows: (1) All vehicles near the ego vehicle have the same size and dynamic parameters, and the vehicle boundaries are described by rectangular boxes; (2) All surrounding vehicles are traveling in a straight line, their intentions are known, and there is no lane-changing behavior; (3) At the time of lane change, the information of all surrounding obstacle vehicles is transmitted to the ego vehicle via wireless communication, so the future positions of the surrounding obstacle vehicles can be calculated. Therefore, the collision detection problem is transformed into the problem of whether the rectangular outlines of each vehicle intersect in the future period of time: (61.1) First, determine the positions of the vertices of the ego vehicle's rectangular outline at the future time. For any path in the stable lane change path cluster, determine the coordinates of the ego vehicle's center of mass when tracking this path based on the ego vehicle's speed, and then obtain the position of the ego vehicle's rectangular outline at the future time. For the surrounding obstacle vehicles, their rectangular outlines are also determined in the same way at the future time. Therefore, determining whether the ego vehicle and the surrounding vehicles will collide at time t becomes determining whether the rectangular outlines of the two vehicles intersect. Finally, all collision-free paths in the stable lane-changing path cluster are defined as the safe lane-changing path cluster.

[0011] Furthermore, the performance evaluation index of each path in the safe lane change path cluster is calculated. Based on the approximate ideal solution sorting algorithm, the optimal lane change path under the performance evaluation index is generated as follows: (71) Specific performance indicators are as follows: (71.1) Comfort index: Use the maximum value of curvature With minimum value The difference is used to express comfort: (6); (71.2) Stability index: Lane change requires ensuring vehicle stability. Considering the change in road adhesion coefficient, the following stability index is defined: (7) Where, ρ ( x ) is the distance from the starting point on the lane change path x The curvature at , max is the maximum value, and are the maximum stable path curvatures of high adhesion road and low adhesion road calculated based on the path stability criterion in curvature form; (71.3) Collision avoidance safety index: The vehicle needs to maintain a safe distance from surrounding vehicles. The Bezier curve has the characteristic of convex hull, that is, the lane change path S satisfy: (8) Where Convex Hull ( ) is the convex hull generating function; the distance between the obstacle and the convex hull should be shortened, and the collision avoidance safety index is defined as follows: (9) Where, is the number of obstructing vehicles, for t Moment i The location of the obstructing vehicle, is the Euclidean distance; (71.4) Lane changing efficiency index: The lane changing time is used to describe the lane changing efficiency. The lane changing efficiency index is defined as: (10) According to the definitions of the four indicators of comfort, stability, collision avoidance, and lane-changing efficiency, smaller values indicate better performance for the corresponding path. Therefore, all four indicators are cost-based. The evaluation indicators are defined as follows: (11) For each path in the safe lane change path cluster, the above four evaluation indicators are calculated and expanded into a decision matrix: (12) Where q is the number of rows in the decision matrix, and is the number of paths in the safe lane change path cluster; (71.5) In order to give weight to the four indicators, Each element in the matrix To normalize: (13) Normalized matrix It is defined as a decision matrix where each element is ; (71.6) Weights are assigned to the four indicators of comfort, stability, collision avoidance, and lane-changing efficiency. First, an absolute weight is defined for each indicator, and then the relative weight is determined by normalization, as follows: Define the absolute weight vector obtained by the fuzzy inference method: (14) Normalize them to determine the relative weights: (15) Therefore, the decision matrix is weighted to obtain the weighted decision matrix: (16) Then, the positive ideal solution and the negative ideal solution are calculated based on formula (16): (17) Where, h ij is the element in the weighted decision matrix; Calculate the Euclidean distance between each path in the safe lane change path cluster and the positive ideal solution and the negative ideal solution, and obtain: (18) Where, and From (17), we can obtain: Then, the specific score is calculated based on the distance of each path in the safe lane change path cluster from the positive ideal solution and the negative ideal solution: (19) Where, and From (18), we can obtain: Finally, according to the score of each path, the paths are arranged in descending order. The path with the highest score is the optimal lane change path, and the corresponding lane change time is is the optimal lane change time under the four indicators, and the corresponding offset rate is the optimal offset rate. Based on this, the optimal lane change path can be obtained.

[0012] According to a second aspect of the present invention, a lane-changing path planning system for an electric vehicle on a road with variable adhesion coefficients is provided, which is used to implement the lane-changing path planning method for an electric vehicle on a road with variable adhesion coefficients, comprising: A receiving module, used to receive road adhesion information of the area to be changed and information of surrounding obstacle vehicles; A basic lane-changing path cluster generation module is used to define different lane-changing paths based on quintic Bezier curve modeling, introduce the concepts of lane-changing time and offset rate, and generate basic lane-changing path clusters; A path stability criterion generation module is used to describe the vehicle dynamic instability boundary using the zero-line boundary stability domain. Based on the vehicle boundary stability domain, path stability criteria in the form of curvature and lane change time are proposed. A stable lane-changing path cluster screening module is used to screen a stable lane-changing path cluster from the basic lane-changing path clusters based on the path stability criterion and road adhesion information; The safe lane-changing path cluster screening module is used to screen out collision-free safe lane-changing path clusters from stable lane-changing path clusters through vehicle profile intersection judgment; The calculation output module is used to calculate the performance evaluation index of each path in the safe lane change path cluster and generate the optimal lane change path under the performance evaluation index based on the approximate ideal solution sorting algorithm.

[0013] According to a third aspect of the present invention, the present invention provides a terminal device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned method for lane change path planning of an electric vehicle applicable to a variable adhesion coefficient road surface is adopted.

[0014] According to a fourth aspect of the present invention, the present invention provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the above-mentioned method for planning lane changes for electric vehicles on variable adhesion coefficient roads.

[0015] The present invention has at least the following beneficial effects: 1. The lane-changing path planning method for electric vehicles on variable-adhesion roads provided by the present invention takes into account changes in road adhesion information within the future lane-changing area at the lane-changing path planning layer. It utilizes an asymmetric lane-changing path to ensure vehicle stability when changing lanes on variable-adhesion roads, thereby improving vehicle lane-changing safety.

[0016] 2. The lane-changing path planning method for electric vehicles on variable-adhesion roads provided by the present invention ensures that when the vehicle changes lanes on variable-adhesion roads, it can take into account vehicle stability, safety, comfort, and lane-changing efficiency.

[0017] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the path planning method of the present invention; Figure 2 It is a schematic diagram of the framework of the path planning method of the present invention; Figure 3 is a lane-changing path curve diagram based on a quintic Bezier curve in the present invention; Figure 4 This is a schematic diagram of the vehicle stability region at the zero line boundary in the present invention; Figure 5 This is a schematic diagram of the traffic scene set in Example 1 of the present invention; Figure 6 Schematic diagram of symmetric path planning in the present invention, where (a) represents situation 1, (b) represents situation 2, and (c) represents situation 3; Figure 7 Schematic diagram of the asymmetric path cluster in the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0020] Example 1: See also Figure 1 and Figure 2 The present invention provides a technical solution: a lane-changing path planning method for an electric vehicle on a road with a variable adhesion coefficient, comprising the following steps: S1. Receive road adhesion information in the lane change area and information about surrounding obstacle vehicles; The road adhesion coefficient in the target lane-changing area changes only once, and this change is considered to be a sudden change from a high adhesion coefficient to a low adhesion coefficient. That is, the adhesion coefficient between the vehicle's current position and a certain position (defined as the docking position) is a certain value, and then suddenly changes to another value after the docking position. Therefore, the road adhesion information in the lane-changing area is the road adhesion coefficient before the sudden change, the road adhesion coefficient after the sudden change, and the docking position. The information about surrounding obstacle vehicles (environmental vehicle information) includes the spatial position, speed, and acceleration of the surrounding obstacle vehicles. Road adhesion information can be obtained through V2X technology: other vehicles that have previously passed through the target area can use their own estimation algorithms to obtain road adhesion information and then send it to the vehicle via onboard wireless communication; V2X, similar to the popular B2B and B2C concepts, stands for vehicle-to-everything, referring to the exchange of information between vehicles and the outside world. By integrating Global Positioning System (GPS) navigation technology, vehicle-to-vehicle communication, wireless communications, and remote sensing technologies, V2X has established a new direction for automotive technology development, enabling the compatibility of both manual and autonomous driving. S2. Based on the quintic Bezier curve modeling, the concepts of lane change time and offset rate are introduced to define different lane change paths, and basic lane change path clusters are generated, such as Figure 3 As shown, the details are as follows: (S21) The lane-changing path of the vehicle is described using a quintic Bezier curve: (1) Where u represents the parameter of the Bezier curve, which ranges from 0 to 1; the superscripts 1, 2, 3, 4, and 5 are powers of u, which are mathematical calculation methods. The control points satisfy the collinearity of P0, P1, and P2, and the collinearity of P3, P4, and P5. In order to reduce the complexity of path cluster generation and the amount of calculation, P1 and P4 are taken as the midpoints of the line segments P0P2 and P3P5, respectively, and the horizontal coordinates of P2 and P3 are the same. The intersection of the line connecting points P2 and P3 and the path is defined as M, with coordinates (L m , 0.5W); In order to facilitate path generation, the P0M segment on the path is defined as the front segment, the MP5 segment is defined as the back segment, and the longitudinal coordinate of point M is L m The values are: (2) Where, L is the vertical coordinate of P5, α is the offset, which is used to quantitatively describe the degree of offset of the longitudinal coordinate of point M relative to 0.5L; when α = 0, point M is the midpoint of the path, and the front and back sections of the path are symmetrical. αWhen <0, point M moves to the left and the front path is compressed. Conversely, point M moves to the right and the back path is compressed. Therefore, the offset affects the shape of the path. (S22) The longitudinal displacement of the generated path is larger than the lateral displacement, so it is assumed that: (3) Where, v x is the vehicle speed, so if the vehicle speed remains unchanged, the lane change time τ Changes in affect the shape of the path; Therefore, by adjusting the lane change time τ and offset α A series of paths can be generated and defined as the basic path cluster; the path with an offset of zero is defined as a symmetric path, and the path with a non-zero offset is defined as an asymmetric path; S3. The zero-line boundary stability domain is used to describe the vehicle dynamic instability boundary. Based on the vehicle boundary stability domain, the path stability criteria in the form of curvature and lane change time are proposed, such as Figure 4 As shown, the details are as follows: (S31) The zero-line boundary stability domain is used to describe the vehicle dynamic instability boundary, and its boundary expression is as follows: (4) In the formula, the area enclosed by AD, BC, AB, and CD is the vehicle stability region. β and γ are the vehicle's center of mass sideslip angle and yaw rate respectively; i , Θ i , i =1, 2 are parameters for determining the boundary equation of the stable region; (S32) Let the mass be m The longitudinal speed of the vehicle in the geodetic coordinate system is v x , which can just track the planned path, and the curvature of the path point corresponding to time k is ρ , which can be calculated according to formula (1), then the reference center of mass sideslip angle and reference yaw rate of the vehicle at this time are expressed as: (5) Where, and are the distances from the vehicle's center of mass to the front and rear axles, is the cornering stiffness of the vehicle's rear wheels; If the trajectory of the vehicle reference center of mass sideslip angle and reference yaw rate calculated in formula (5) does not exceed the zero line boundary stability region, the vehicle is stable (vehicle stability region); therefore, the simultaneous formula (5) can obtain the path stability criterion in the form of curvature: ,in, and are the upper and lower bounds of the curvature, which are functions of vehicle speed and road adhesion coefficient. The faster the vehicle speed or the smaller the road adhesion coefficient, the closer the value is to 0. For any lane change path expressed by formula (1), the curvature of each point on the path can be calculated. If the curvature satisfies the path stability criterion in the form of curvature, then the path is stable, otherwise it is unstable.

[0021] (S33) When the road adhesion coefficient and the vehicle speed remain unchanged, the path that enables the vehicle to complete the lane change fastest can be obtained according to the curvature form of the path stability criterion in (S32). The corresponding time is defined as the minimum lane change time. Therefore, the path stability criterion in the form of lane change time can be obtained: A path with a lane-changing time greater than the minimum lane-changing time is a stable path; a path with a lane-changing time less than the minimum lane-changing time is an unstable path.

[0022] S4. Based on the path stability criterion and road adhesion information, a stable lane change path cluster is obtained from the basic lane change path clusters, as follows: Assume that the distance in front of the vehicle is s meters and the adhesion coefficient is High adhesion road surface ( Figure 5 Indicated by dark color in the middle), after which the adhesion coefficient decreases to Low adhesion road surface ( Figure 5 Indicated by light color), s is the docking position; (S41) Using the path stability criterion in the form of lane change time, a stable symmetric path is selected from the symmetric path cluster to generate a stable symmetric path cluster: According to the path stability criterion in the form of lane change time, there are two ways to ensure that the vehicle changes lanes stably in the form of a symmetrical path under the above road conditions: Method 1: Plan a path based on high-adhesion roads that can complete lane changes before entering low-adhesion roads; Method 2: Complete lane change based on low-adhesion road surface planning path; It is worth noting that the above two methods are closely related to the docking position s. That is, if the docking position is far enough, method 1 can be used; if the docking position is close, method 2 can be used. Therefore, the following stable symmetric paths are generated according to the above two methods based on the docking position. (S42) Generate a stable symmetric path according to the docking position s according to the method 1 and the method 2: (S42.1) Assume that the vehicle speed is constant and known when traveling at high speed, the docking position s, the high adhesion road adhesion coefficient and low adhesion road adhesion coefficient Given: According to the path stability criterion in the form of lane change time, calculate and Corresponding minimum lane change time and , according to the speed-displacement relationship, when the lane-changing longitudinal distance is s, the lane-changing time is ;like Figure 6 As shown, considering the symmetric paths with different lane changing times, according to and and There are three situations: Situation 1: ; Case 2: ; Case 3: ; (S42.2) Based on the path stability criterion in the form of lane change time and two stable path planning methods, the stable paths in different situations are as follows: Figure 6 As shown; in case 1, the stable path is generated based on method 2; in cases 2 and 3, the stable path is generated based on methods 1 and 2. In actual layout, a stable symmetric path cluster is generated by judging the situation according to the above method; (S43) Using the curvature-based path stability criterion, a stable asymmetric path is selected from the asymmetric path cluster to generate a stable asymmetric path cluster: (S43.1) Due to the use of symmetrical routes, the road utilization rate is low, such as Figure 6 (a) Case 1 and Figure 6 In (b), the oblique line part of case 2 is used, so an asymmetric path is used to fill the space, and the space is defined as the supplementary space; (S43.2) According to the curvature-based path stability criterion, high-adhesion roads have a greater maximum curvature that supports path stability than low-adhesion roads. Therefore, the following approach is used: Method 3: Perform aggressive steering on high-adhesion roads, then switch to gentle steering when or before entering low-adhesion roads until the lane change is completed; (S43.3) Using Method 3, we can sample asymmetric lane-changing paths from the supplementary space by adjusting the offset rate. We then determine their stability based on the curvature-based path stability criterion, thereby generating a stable asymmetric path cluster: Take any path with a lane change time from the supplementary space and adjust the offset rate α ,make α <0, several paths with the same lane-changing time but different offset rates are obtained, such as Figure 7 As shown in the figure, the curvature value of each point on each path is calculated one by one, and the path stability criterion in the form of curvature can be used to determine whether the path is stable. Finally, all stable paths constitute an asymmetric stable lane change path cluster; All stable symmetric path clusters and stable asymmetric path clusters constitute a stable lane-changing path cluster; S5. Filter the stable lane change path clusters to obtain a collision-free safe lane change path cluster based on vehicle contour intersection judgment, as follows: (61) The vehicle in front of the vehicle in the same lane as the ego vehicle is called the leading vehicle, the vehicle in front of the vehicle in the adjacent lane is called the leading vehicle in the adjacent lane, and the vehicle behind the vehicle is called the trailing vehicle in the adjacent lane. like Figure 5 As shown in Figure 1, the traffic environment is set as follows: (1) All vehicles near the ego vehicle have the same size and dynamic parameters, and a rectangular box is used to describe the boundaries of the vehicle; (2) The surrounding vehicles all travel in a straight line, their intentions are known, and there is no lane changing behavior; (3) At the time of lane changing, the information of all surrounding obstacle vehicles is transmitted to the ego vehicle via wireless communication, so the position of the surrounding obstacle vehicles at the future time can be obtained by calculation. Therefore, the collision judgment detection problem (collision judgment) is transformed into the problem of calculating whether the rectangular box outlines of each vehicle intersect in the future period of time (outline calculation): (61.1) First, determine the positions of the vertices of the ego vehicle's rectangular outline at the future time. For any path in the stable lane change path cluster, determine the coordinates of the ego vehicle's center of mass when tracking this path based on the ego vehicle's speed, and then obtain the position of the ego vehicle's rectangular outline at the future time. For the surrounding obstacle vehicles, their rectangular outlines are also determined in the same way at the future time. Therefore, determining whether the ego vehicle and the surrounding vehicles will collide at time t becomes determining whether the rectangular outlines of the two vehicles intersect. Finally, all collision-free paths in the stable lane-changing path cluster are defined as the safe lane-changing path cluster; S6. Calculate the performance evaluation index for each path in the safe lane change path cluster and generate the optimal lane change path under the performance evaluation index using the approximate ideal solution sorting algorithm, as follows: (71) Specific performance indicators are as follows: (71.1) Comfort index: Use the maximum value of curvature With minimum value The difference is used to express comfort: (6); (71.2) Stability index: Lane change requires ensuring vehicle stability. Considering the change in road adhesion coefficient, the following stability index is defined: (7) Where, ρ ( x ) is the distance from the starting point on the lane change path x The curvature at , max is the maximum value, and are the maximum stable path curvatures of high adhesion road and low adhesion road calculated based on the path stability criterion in curvature form; (71.3) Collision avoidance safety index: The vehicle needs to maintain a safe distance from surrounding vehicles. The Bezier curve has the characteristic of convex hull, that is, the lane change path S satisfy: (8) Where Convex Hull ( ) is the convex hull generating function; the distance between the obstacle and the convex hull should be shortened, and the collision avoidance safety index is defined as follows: (9) Where, is the number of obstructing vehicles, for t Moment i The location of the obstructing vehicle, is the Euclidean distance; (71.4) Lane changing efficiency index: The lane changing time is used to describe the lane changing efficiency. The lane changing efficiency index is defined as: (10) According to the definitions of the four indicators of comfort, stability, collision avoidance, and lane-changing efficiency, smaller values indicate better performance for the corresponding path. Therefore, all four indicators are cost-based. The evaluation indicators are defined as follows: (11) For each path in the safe lane change path cluster, the above four evaluation indicators are calculated and expanded into a decision matrix: (12) Where q is the number of rows in the decision matrix, and is the number of paths in the safe lane change path cluster; (71.5) In order to give weight to the four indicators, Each element in the matrix To normalize: (13) Normalized matrix It is defined as a decision matrix where each element is ; (71.6) Weights are assigned to the four indicators of comfort, stability, collision avoidance, and lane-changing efficiency. First, an absolute weight is defined for each indicator, and then the relative weight is determined through normalization. According to normal driving experience, the weights corresponding to each indicator vary with the road adhesion coefficient and longitudinal vehicle speed: when the road adhesion coefficient is smaller and the vehicle longitudinal speed is greater, the vehicle stability boundary shrinks and the vehicle is more likely to become unstable, so the weight of the safety performance indicator (including safety and stability evaluation indicators) should be increased; when the road adhesion coefficient is higher and the vehicle longitudinal speed is lower, the vehicle stability domain is larger, and the weight of the secondary performance indicators (comfort and lane-changing efficiency evaluation indicators) should be increased. Considering that this article is mainly aimed at lane-changing path planning on docking roads, the average value of the adhesion coefficient before and after docking and the vehicle speed are used to determine the weights of the two types of performance indicators; because the relationship between the average value of the vehicle speed and the adhesion coefficient and the two types of performance indicators is relatively vague, a fuzzy inference system is used to determine the weights, as follows: Define the absolute weight vector obtained by the fuzzy inference method: (14) Normalize them to determine the relative weights: (15) Therefore, the decision matrix is weighted to obtain the weighted decision matrix: (16) Then, the positive ideal solution and the negative ideal solution are calculated based on formula (16): (17) Where, h ij is the element in the weighted decision matrix; Calculate the Euclidean distance between each path in the safe lane change path cluster and the positive ideal solution and the negative ideal solution, and obtain: (18) Where, and From (17), we can obtain: Then, the specific score is calculated based on the distance of each path in the safe lane change path cluster from the positive ideal solution and the negative ideal solution: (19) Where, and From (18), we can obtain: Finally, according to the score of each path, the paths are arranged in descending order. The path with the highest score is the optimal lane change path, and the corresponding lane change time is is the optimal lane change time under the four indicators, and the corresponding offset rate is the optimal offset rate. Based on this, the optimal lane change path can be obtained.

[0023] In summary, the present invention uses quintic Bezier curves defined by lane-changing time and offset rate to model lane-changing paths, including symmetric and asymmetric paths. Secondly, a path stability criterion in the form of curvature and lane-changing time is constructed based on the vehicle stability domain. This criterion and time-varying road adhesion information are used to generate stable candidate lane-changing paths. Finally, based on an approximate ideal solution sorting algorithm, a lane-changing path that meets the optimal requirements of comfort, stability, safety, and efficiency is generated. This method fully considers the impact of changes in the road adhesion coefficient on vehicle stability during the lane-changing process, thereby improving the safety of the vehicle during lane changes in high-speed scenarios.

[0024] Example 2: This embodiment provides a lane-changing path planning system for an electric vehicle on a road with variable adhesion coefficients, which is used to implement the above-mentioned lane-changing path planning method for an electric vehicle on a road with variable adhesion coefficients, including: A receiving module, used to receive road adhesion information of the area to be changed and information of surrounding obstacle vehicles; A basic lane-changing path cluster generation module is used to define different lane-changing paths based on quintic Bezier curve modeling, introduce the concepts of lane-changing time and offset rate, and generate basic lane-changing path clusters; A path stability criterion generation module is used to describe the vehicle dynamic instability boundary using the zero-line boundary stability domain. Based on the vehicle boundary stability domain, path stability criteria in the form of curvature and lane change time are proposed. A stable lane-changing path cluster screening module is used to screen a stable lane-changing path cluster from the basic lane-changing path clusters based on the path stability criterion and road adhesion information; The safe lane-changing path cluster screening module is used to screen out collision-free safe lane-changing path clusters from stable lane-changing path clusters through vehicle profile intersection judgment; The calculation output module is used to calculate the performance evaluation index of each path in the safe lane change path cluster and generate the optimal lane change path under the performance evaluation index based on the approximate ideal solution sorting algorithm.

[0025] Specifically, the above-mentioned receiving module, basic lane change path cluster generation module, path stability criterion generation module, stable lane change path cluster screening module, safe lane change path cluster screening module and calculation output module can be embedded in a computer processing system. The computer calls the above-mentioned modules to complete the task of ensuring the stability of the vehicle when changing lanes on a road with a variable adhesion coefficient based on the above-mentioned electric vehicle lane change path planning method applicable to a variable adhesion coefficient road surface; the above-mentioned receiving module, basic lane change path cluster generation module, path stability criterion generation module, stable lane change path cluster screening module, safe lane change path cluster screening module and calculation output module can perform operations according to the specific steps given in the above-mentioned electric vehicle lane change path planning method applicable to a road with a variable adhesion coefficient.

[0026] It should be noted that it should be understood that the division of the various modules of the above system is only a division of logical functions. In actual implementation, they can be fully or partially integrated into a physical entity, or they can be physically separated. Moreover, these modules can all be implemented in the form of software called by processing elements; or they can all be implemented in the form of hardware; or some modules can be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the receiving module can be a separately established processing element, or it can be integrated into a chip of the above-mentioned device. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and called by a processing element of the above-mentioned device to perform the functions of the above-mentioned signal processing module. The implementation of other modules is similar. In addition, these modules can all or partly be integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed by the hardware integrated logic circuit in the processor element or by instructions in the form of software.

[0027] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented by scheduling program code through a processing element, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0028] Example 3: The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned electric vehicle lane change path planning method applicable to variable adhesion coefficient roads is adopted.

[0029] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device can also include input and output devices, network access devices and buses, etc.

[0030] Furthermore, the processor may be a central processing unit (CPU). Of course, depending on the actual usage, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. may also be used. The general-purpose processor may be a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0031] Example 4: The present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to execute the above-mentioned electric vehicle lane change path planning method applicable to variable adhesion coefficient roads.

[0032] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that computer-readable medium includes but is not limited to the above-mentioned components.

[0033] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0034] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed on" or "set on" another element, it can be directly on the other element or there can be a central element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there can be a central element at the same time. The terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are for illustrative purposes only and are not intended to be the only embodiment.

[0035] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0036] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Claims

1. A lane-changing path planning method for electric vehicles on roads with variable adhesion coefficients, characterized in that: The following steps are involved: Receive road adhesion information of the area to be changed and information of surrounding obstacle vehicles; Based on quintic Bezier curve modeling, the concept of lane change time and offset rate is introduced to define different lane change paths and generate basic lane change path clusters. The zero-line boundary stability domain is used to describe the vehicle dynamic instability boundary, and path stability criteria in the form of curvature and lane-changing time are proposed. Based on the path stability criterion and road adhesion information, a stable lane-changing path cluster is obtained from the basic lane-changing path clusters. By judging the intersection of vehicle contours, a safe lane-changing path cluster without collision is obtained from the stable lane-changing path cluster. The performance evaluation index of each path in the safe lane-changing path cluster is calculated, and the optimal lane-changing path under the performance evaluation index is generated based on the approximate ideal solution sorting algorithm.

2. The lane-changing path planning method for an electric vehicle on a variable-adhesion road surface according to claim 1, characterized in that: The road adhesion information of the lane-changing area includes the road adhesion coefficient before the sudden change, the road adhesion coefficient after the sudden change, and the docking position. The information of the surrounding obstacle vehicles includes the spatial position, speed, and acceleration of the surrounding obstacle vehicles.

3. The lane-changing path planning method for electric vehicles on variable-adhesion roads according to claim 2, characterized in that: Based on the quintic Bezier curve modeling, the concept of lane change time and offset rate is introduced to define different lane change paths and generate basic lane change path clusters as follows: (31) The lane-changing path of the vehicle is described using a quintic Bezier curve: B(u)=(1-u) 5 P0+5u(1-u) 4 P1+10u 2 (1-in) 3 P2+10u 3 (1-in) 2 P3+5u 4 (1-u)P4+u 5 P5 (1) Where u represents the parameter of the Bezier curve, ranging from 0 to 1; superscripts 1, 2, 3, 4, and 5 are powers of u; the control points satisfy the following conditions: P0, P1, and P2 are collinear, and P3, P4, and P5 are collinear. P1 and P4 are the midpoints of the line segments P0P2 and P3P5, respectively, and P2 and P3 have the same horizontal coordinates. The intersection of the line connecting points P2 and P3 and the path is defined as M, with coordinates (L m ,0.5W)) defines the P0M segment on the path as the front segment, the MP5 segment as the back segment, and the longitudinal coordinate of point M is L m The values are: Where L is the vertical coordinate of P5, and α is the offset, which is used to quantitatively describe the degree of offset of the vertical coordinate of point M relative to 0.5L. When α = 0, point M is the midpoint of the path, and the front and back sections of the path are symmetrical. When α < 0, point M moves to the left, and the front section of the path is compressed. Conversely, when point M moves to the right, the back section of the path is compressed. Therefore, the offset affects the shape of the path. (32) The longitudinal displacement of the generated path is larger than the lateral displacement, so it is assumed that: L≈v x t(3) Where, v x is the vehicle speed, so when the vehicle speed remains unchanged, the change in lane change time τ affects the shape of the path; Therefore, by adjusting the lane change duration τ and the offset α, a series of paths can be generated, which are defined as the basic path cluster. Paths with a zero offset are defined as symmetric paths, and paths with a non-zero offset are defined as asymmetric paths.

4. The lane-changing path planning method for electric vehicles on variable-adhesion roads according to claim 2, characterized in that: The zero-line boundary stability domain is used to describe the vehicle dynamic instability boundary. Based on the vehicle boundary stability domain, path stability criteria in the form of curvature and lane-changing time are proposed, as follows: (41) The zero-line boundary stability domain is used to describe the vehicle dynamic instability boundary, and its boundary expression is as follows: Where β and γ are the vehicle's center of mass sideslip angle and yaw rate, respectively; i ,Θ i ,i=1,2 are parameters for determining the boundary equation of the stable region; (42) Let the vehicle with mass m have a longitudinal velocity v in the geodetic coordinate system. x , which can track the planned path. The curvature of the path point corresponding to time k is ρ, which can be calculated according to formula (1). Then the reference center of mass sideslip angle and reference yaw rate of the vehicle at this time are expressed as: Where, l f and l r are the distances from the vehicle's center of mass to the front and rear axles, C r is the cornering stiffness of the vehicle's rear wheels; If the trajectory of the vehicle reference center of mass sideslip angle and reference yaw rate calculated in formula (5) does not exceed the zero line boundary stability region, the vehicle is stable; therefore, the simultaneous formula (5) can obtain the path stability criterion in the form of curvature: in, and ρ are the upper and lower bounds of the curvature, which are functions of vehicle speed and road adhesion coefficient. The faster the vehicle speed or the smaller the road adhesion coefficient, the closer the value is to 0. If the curvature of each point on the lane change path satisfies the path stability criterion in the form of curvature, then the lane change path is stable, otherwise it is unstable. (43) When the road adhesion coefficient and vehicle speed remain unchanged, the path that enables the vehicle to complete the lane change fastest can be obtained according to the curvature form of the path stability criterion in (42). The corresponding time is defined as the minimum lane change time. Therefore, the path stability criterion in the form of lane change time can be obtained: A path with a lane-changing time greater than the minimum lane-changing time is a stable path; a path with a lane-changing time less than the minimum lane-changing time is an unstable path.

5. The lane-changing path planning method for electric vehicles on variable-adhesion roads according to claim 4, characterized in that: Based on the path stability criterion and road adhesion information, a stable lane change path cluster is obtained from the basic lane change path cluster, as follows: Assume that the distance in front of the vehicle is s meters and the adhesion coefficient is The high adhesion road surface, after which the adhesion coefficient is reduced to low adhesion road surface, s is the docking position; (51) Using the path stability criterion in the form of lane change time, a stable symmetric path is selected from the symmetric path cluster to generate a stable symmetric path cluster: According to the path stability criterion in the form of lane change time, there are two ways to achieve stable lane change in the form of a symmetrical path: Method 1: Plan a path based on high-adhesion roads that can complete lane changes before entering low-adhesion roads; Method 2: Complete lane change based on low-adhesion road surface planning path; If the docking location s is far away, use method 1; if the docking location is close, use method 2; (52) Generate a stable symmetric path according to method 1 and method 2 based on the docking position s: (52.1) Assume that the vehicle speed is constant and known when it is traveling at high speed, the docking position s, the high adhesion road adhesion coefficient and low adhesion road adhesion coefficient It is known that according to the path stability criterion in the form of lane change time, the calculation and Corresponding minimum lane change time and According to the speed-displacement relationship, the lane-changing time is τ when the longitudinal distance is s. s ; Considering the symmetric paths with different lane changing times, according to τ s and and There are three situations: Situation 1: Scenario 2: Scenario 3: (52.2) Based on the path stability criterion in the form of lane change time and two stable path planning methods, stable paths are determined for different situations: in situation 1, the stable path is generated based on method 2; in situations 2 and 3, the stable paths are generated based on methods 1 and 2. A stable symmetric path cluster is generated by judging the situation to which it belongs; (53) Using the curvature-based path stability criterion, a stable asymmetric path is selected from the asymmetric path cluster to generate a stable asymmetric path cluster: (53.1) Since the adoption of symmetrical paths results in low road utilization, which is mainly reflected in Cases 1 and 2, an asymmetrical path is adopted to fill the space, which is defined as supplementary space; (53.2) According to the curvature-based path stability criterion, a high-adhesion road surface has a larger maximum curvature that can support path stability than a low-adhesion road surface. Therefore, the following method is used: Method 3: Perform aggressive steering on high-adhesion roads, then switch to gentle steering when or before entering low-adhesion roads until the lane change is completed; (53.3) Using method 3, we can sample asymmetric lane-changing paths from the supplementary space by adjusting the offset rate, and judge their stability based on the path stability criterion in the form of curvature, thereby generating a stable asymmetric path cluster: From the supplementary space, we randomly select a path with a lane-changing time. By adjusting the offset ratio α to keep α < 0, we obtain several paths with the same lane-changing time but different offset ratios. We then calculate the curvature value of each point on each path. Using the curvature-based path stability criterion, we determine whether the path is stable. Finally, all stable paths form an asymmetric stable lane-changing path cluster. All stable symmetric path clusters and stable asymmetric path clusters constitute a stable lane-changing path cluster.

6. The lane-changing path planning method for an electric vehicle on a variable-adhesion road surface according to claim 5, characterized in that: By judging the intersection of vehicle contours, a safe lane change path cluster without collision is obtained from the stable lane change path cluster, as follows: (61) The vehicle in front of the vehicle in the same lane as the ego vehicle is called the leading vehicle, the vehicle in front of the vehicle in the adjacent lane is called the leading vehicle in the adjacent lane, and the vehicle behind the vehicle is called the trailing vehicle in the adjacent lane. The traffic environment is set as follows: (1) All vehicles near the ego vehicle have the same size and dynamic parameters, and the vehicle boundaries are described by rectangular boxes; (2) All surrounding vehicles are driving in a straight line, their intentions are known, and there is no lane change behavior; (3) At the time of lane change, the information of all surrounding obstacle vehicles is transmitted to the ego vehicle via wireless communication, so the future positions of the surrounding obstacle vehicles can be calculated. Therefore, the collision detection problem is transformed into the problem of whether the rectangular outlines of each vehicle will intersect in the future period of time: (61.1) First, determine the positions of the vertices of the ego vehicle's rectangular outline at the future time. For any path in the stable lane change path cluster, determine the coordinates of the ego vehicle's center of mass when tracking this path based on the ego vehicle's speed, and then obtain the position of the ego vehicle's rectangular outline at the future time. For the surrounding obstacle vehicles, their rectangular outlines are also determined in the same way. Therefore, determining whether the ego vehicle and the surrounding vehicles will collide at time t becomes determining whether the rectangular outlines of the two vehicles intersect. Finally, all collision-free paths in the stable lane-changing path cluster are defined as the safe lane-changing path cluster.

7. The lane-changing path planning method for an electric vehicle on a variable-adhesion road surface according to claim 6, characterized in that: Calculate the performance evaluation index of each path in the safe lane change path cluster and generate the optimal lane change path under the performance evaluation index based on the approximate ideal solution sorting algorithm, as follows: (71) The specific performance indicators are as follows: (71.1) Comfort index: using the maximum value of curvature ρ m1 With the minimum value ρ m2 The difference is used to express comfort: J C =|ρ m1 |+|r m2 | (6); (71.2) Stability index: Lane change requires ensuring vehicle stability. Considering the change in adhesion coefficient of the road surface, the following stability index is defined: Where ρ(x) is the curvature of the lane change path at the starting point x, max is the maximum value, and are the maximum stable path curvatures of high adhesion road and low adhesion road calculated based on the path stability criterion in curvature form; (71.3) Collision avoidance safety index: The vehicle must maintain a safe distance from surrounding vehicles. The Bezier curve has the property of a convex hull, that is, the lane change path S satisfies: S∈ConvexHull(P0,P1,...,P5)(8) Where ConvexHull() is the convex hull generating function; the distance between the obstacle and the convex hull should be shortened, and the collision avoidance safety index is defined as follows: Where N o is the number of obstacle vehicles, p i (t) is the position of the i-th obstacle vehicle at time t, d min is the Euclidean distance; (71.4) Lane-changing efficiency index: The lane-changing efficiency is described using the lane-changing time. The lane-changing efficiency index is defined as: J E =τ (10) According to the definitions of the four indicators of comfort, stability, collision avoidance, and lane-changing efficiency, smaller values indicate better performance for the corresponding path. Therefore, all four indicators are cost-based. The evaluation indicators are defined as follows: N=[1 / J C 1 / J E 1 / J D 1 / J S ] (11) For each path in the safe lane change path cluster, the above four evaluation indicators are calculated and expanded into a decision matrix: Where q is the number of rows in the decision matrix, and is the number of paths in the safe lane change path cluster; (71.5) In order to weight the four indicators, for each element a in the M1 matrix ij To normalize: The normalized matrix M2 is defined as the decision matrix, where each element is b ij ; (71.6) Weights are assigned to the four indicators of comfort, stability, collision avoidance, and lane-changing efficiency. First, an absolute weight is defined for each indicator, and then the relative weight is determined by normalization, as follows: Define the absolute weight vector obtained by the fuzzy inference method: λ=[λ C l E l D l S ] (14) Normalize them to determine the relative weights: Therefore, the decision matrix is weighted to obtain the weighted decision matrix: H=(h ij ) q×4 =(w j ×b ij ) q×4 ,i=1,2,…,q;j=1,2,3,4. (16) Then, the positive ideal solution and the negative ideal solution are calculated based on formula (16): Where h ij is the element in the weighted decision matrix; Calculate the Euclidean distance between each path in the safe lane change path cluster and the positive ideal solution and the negative ideal solution, and obtain: Where, and From (17), we can obtain: Then, the specific score is calculated based on the distance of each path in the safe lane change path cluster from the positive ideal solution and the negative ideal solution: Where, and From (18), we can obtain: Finally, according to the score of each path, the paths are arranged in descending order. The path with the highest score is the optimal lane change path, and the corresponding lane change time is τ is the optimal lane change time under the four indicators, and the corresponding offset rate is the optimal offset rate. Based on this, the optimal lane change path can be obtained.

8. A lane-changing path planning system for electric vehicles on roads with variable adhesion coefficients, used to implement the lane-changing path planning method for electric vehicles on roads with variable adhesion coefficients as claimed in any one of claims 1 to 7, characterized in that: include: A receiving module, used to receive road adhesion information of the area to be changed and information of surrounding obstacle vehicles; A basic lane-changing path cluster generation module is used to define different lane-changing paths based on quintic Bezier curve modeling, introduce the concepts of lane-changing time and offset rate, and generate basic lane-changing path clusters; A path stability criterion generation module is used to describe the vehicle dynamic instability boundary using a zero-line boundary stability domain and generate path stability criteria in the form of curvature and lane change time based on the vehicle boundary stability domain; A stable lane-changing path cluster screening module is used to screen a stable lane-changing path cluster from the basic lane-changing path clusters based on the path stability criterion and road adhesion information; The safe lane-changing path cluster screening module is used to screen out collision-free safe lane-changing path clusters from stable lane-changing path clusters through vehicle profile intersection judgment; The calculation output module is used to calculate the performance evaluation index of each path in the safe lane change path cluster and generate the optimal lane change path under the performance evaluation index based on the approximate ideal solution sorting algorithm.

9. A terminal device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, the lane change path planning method for an electric vehicle applicable to a variable adhesion coefficient road surface according to any one of claims 1 to 6 is adopted.

10. A storage medium containing computer-executable instructions, characterized in that: When executed by a computer processor, the computer executable instructions are used to execute the lane change path planning method for an electric vehicle applicable to a variable adhesion coefficient road surface according to any one of claims 1 to 6.

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