Distributed drive electric vehicle lane change trajectory planning method, device and storage medium

Abstract

The present invention discloses a method, device, and storage medium for planning lane-changing trajectories for a distributed drive electric vehicle. The method comprises: generating an unconstrained generalized lane-changing trajectory cluster based on a vehicle trajectory planning curve fitting function; selecting a lane-changing trajectory cluster that satisfies the stability domain of the distributed drive electric vehicle from the generalized lane-changing trajectory cluster; calculating the vehicle's feasible domain within the lane-changing trajectory cluster that satisfies the stability domain of the distributed drive electric vehicle based on environmental geometric constraints and road boundaries; and selecting the optimal lane-changing trajectory by evaluating stability indicators, trajectory tracking accuracy indicators, comfort indicators, and lane-changing efficiency indicators based on an improved algorithm combining the analytic hierarchy process and ideal approximation techniques. The path planning algorithm of the present invention fully utilizes the advantages of the distributed drive electric vehicle's four wheels being independently controllable and incorporates the vehicle's new dynamic characteristics into the trajectory planning algorithm, thereby improving the efficiency and safety of the electric vehicle's driving.

Description

Technical Field

[0001] The present invention relates to a distributed drive intelligent electric vehicle lane change trajectory planning method, relates to a distributed drive intelligent electric vehicle path planning technology, and belongs to the field of new energy vehicle design and manufacturing. Background Art

[0002] Distributed-drive electric vehicles, compared to traditional centralized electric vehicles (EVs) that have switched from gasoline to electricity, feature a completely new chassis-based drive configuration. The drive motor is mounted directly within or near the drive wheels. This new powertrain chassis offers the advantages of omnidirectional mobility and is gradually evolving towards a digital chassis. Therefore, automotive experts consider it a dedicated chassis for smart electric vehicles and the optimal vehicle for achieving safe and efficient autonomous driving. It has become a mainstream trend in the future development of smart electric vehicles. The continuous growth in vehicle ownership in recent years has led to increasingly prominent issues, including frequent traffic accidents and increased traffic congestion. The electrification, networking, and intelligence of vehicles are the future trends, and therefore the highly intelligent nature of distributed-drive electric vehicles is an inevitable trend. Autonomous driving technology is categorized from Level 0 to Level 5. As the level of driving automation increases, the vehicle system becomes less dependent on the driver. Currently, mass-produced smart electric vehicles operate at Levels 2 to 3. In other words, more mature autonomous driving technologies can only achieve conditional autonomous driving capabilities. Therefore, autonomous driving technology for smart electric vehicles must be more adaptable to diverse traffic scenarios and maintain vehicle stability in scenarios such as high-speed collision avoidance and emergency obstacle avoidance. Distributed drive electric vehicles have a higher degree of control freedom, and the research and development of automotive intelligent technology will be more difficult. The intelligence of distributed drive electric vehicles is challenging.

[0003] Vehicle trajectory planning technology is a key component in the implementation of autonomous driving technology. Since vehicles typically operate in complex traffic environments or at high speeds, the requirements for vehicle trajectory planning algorithms are generally higher than those for indoor robots, and more kinematic and dynamic factors need to be considered. Commonly used trajectory planning algorithms include those based on graph search, curve fitting, numerical optimization, artificial potential fields, sampling, and intelligent methods. Vehicle lane change trajectory planning is a common traffic scenario. When a vehicle is driving on the road and encounters scenarios such as overtaking and obstacle avoidance, it is necessary to plan a driving trajectory in advance that conforms to human driving habits while satisfying vehicle stability constraints, environmental geometry constraints, and road boundary constraints. The planned path must be an optimal path with high lane change efficiency and easy tracking and control, thus achieving multi-objective trajectory optimization. Currently, centralized trajectory planning algorithms for intelligent electric vehicles are generally conservative, focusing more on kinematics and neglecting dynamics. The rational study of the stability domain mechanisms of distributed drive electric vehicles significantly influences the final path planning results. The integration of kinematics and dynamics in vehicle planning is a key issue in trajectory planning for distributed drive intelligent electric vehicles and a necessary step towards highly intelligent vehicles. In the context of vehicle intelligence, fully utilizing the stability domain of distributed drive electric vehicles for high-level path planning is crucial. The intelligent driving layer and chassis control layer of distributed drive electric vehicles are inextricably linked. Summary of the Invention

[0004] The technical problem to be solved by this invention is the key issue of lane-changing trajectory planning for distributed-drive intelligent electric vehicles. A method, device, and storage medium for lane-changing trajectory planning for distributed-drive intelligent electric vehicles are proposed. The proposed trajectory planning algorithm can handle traffic scenarios such as lane changing, overtaking, and emergency obstacle avoidance in complex traffic environments, and is equally applicable to both high- and low-speed driving conditions. The path planning algorithm fully utilizes the independent controllability of the four wheels of distributed-drive electric vehicles, improving their dynamic performance compared to centralized systems. By integrating the vehicle's kinematic and dynamic characteristics into the trajectory planning algorithm, the efficiency and safety of electric vehicle driving are enhanced.

[0005] The technical solution adopted by the present invention to solve the technical problem includes the following steps:

[0006] A method for planning lane-changing trajectories of a distributed drive electric vehicle, comprising the following steps:

[0007] Generate unconstrained generalized lane-changing trajectory clusters based on the vehicle trajectory planning curve fitting function;

[0008] Among the generated unconstrained generalized lane-changing trajectory clusters, the lane-changing trajectory clusters that meet the stability domain of distributed drive electric vehicles are selected;

[0009] In the selected lane-changing trajectory cluster that satisfies the stability domain of the distributed drive electric vehicle, the feasible domain of the vehicle is calculated according to the geometric constraints of the environment and the road boundary.

[0010] In the calculated vehicle feasible region, an improved algorithm based on the combination of hierarchical analysis method and ideal approximation technology is used to select the optimal lane-changing trajectory by evaluating stability index, trajectory tracking accuracy index, comfort index and lane-changing efficiency index.

[0011] Designing an unconstrained generalized lane-changing trajectory cluster consists of the following parts:

[0012] (I) Determination of the optimal longitudinal displacement, velocity, acceleration, and jerk expressions.

[0013] a) It is necessary to minimize longitudinal fluctuations and establish performance indicators for minimizing longitudinal fluctuations:

[0014]

[0015] The following constraints need to be met:

[0016]

[0017] Among them, η x is the cost function of longitudinal fluctuation, minη x To solve the minimum value of the performance index that minimizes longitudinal fluctuation; τ0 is the initial moment of the lane change trajectory, is the end time of the lane changing process, and the initial time τ0 = 0, so the lane changing process time is x(t) is the longitudinal displacement function of the vehicle during lane changing, and v x (t) represents the longitudinal speed of the vehicle during the lane change process; and a x (t) represents the longitudinal acceleration of the vehicle during lane change; and j x (t) represents the longitudinal jerk of the vehicle during lane change; L is the longitudinal distance of the vehicle during lane change, v x0 is the initial value of the longitudinal velocity during the lane-changing process, is the final value of the longitudinal speed during the lane-changing process;

[0018] b) To solve the above performance indicators, construct the Hamiltonian function H x for

[0019]

[0020] Among them, κ x1 For the corresponding v x The Lagrangian operator, κx2 For a x The Lagrangian operator, κ x3 For the corresponding j x The Lagrangian operator.

[0021] c) According to the Pontryagin maximum principle, the co-state equation is expressed as:

[0022] The solution is: κ x1 =m0

[0023] The solution is: x2 =m1-m0t

[0024] The solution is:

[0025] Among them, m0, m1 and m2 are unknown constants.

[0026] The extreme value condition is:

[0027] The solution is:

[0028] in,

[0029] d) In the geodetic coordinate system, the expressions for the optimal longitudinal displacement, velocity, acceleration, and jerk during the lane change process can be obtained according to the above steps:

[0030]

[0031]

[0032]

[0033]

[0034] Among them, j x (t), a x (t), v x (t) and x(t) represent the functions of vehicle longitudinal jerk, acceleration, velocity and displacement respectively, t is the independent variable of the function of time, is the lane changing time, L is the longitudinal distance of the vehicle changing lanes, v x0 is the initial value of the longitudinal velocity.

[0035] (II) Determination of the optimal lateral displacement, velocity, acceleration, and jerk expressions.

[0036] a) It is necessary to minimize lateral fluctuations and build performance indicators that minimize lateral fluctuations:

[0037]

[0038] The following constraints need to be met:

[0039]

[0040] Among them, η y is the cost function of lateral fluctuation, minη y To solve the minimum value of the performance index that minimizes lateral fluctuation; τ0 is the initial moment of the lane-changing trajectory, is the end time of the lane changing process, and the initial time τ0 = 0, so the lane changing process time is y(t) represents the lateral displacement function of the vehicle during lane changing, and v y (t) represents the lateral speed of the vehicle during the lane change process; and a y (t) represents the lateral acceleration of the vehicle during lane change; and j y (t) represents the lateral jerk of the vehicle during lane change; D is the lateral distance of the vehicle during lane change; v y0 is the initial value of the lateral velocity during the lane changing process, is the final value of the lateral speed during the lane changing process;

[0041] b) To solve the above performance indicators, construct the Hamiltonian function H y for

[0042]

[0043] Among them, κ y1 For the corresponding v y The Lagrangian operator, κ y2 For the corresponding a y The Lagrangian operator, κ y3 For the corresponding j y The Lagrangian operator.

[0044] c) According to the Pontryagin maximum principle, the co-state equation is expressed as:

[0045] The solution is: y1 =n0

[0046] The solution is: y2 =n1-n0t

[0047] The solution is:

[0048] Among them, n0, n1 and n2 are unknown constants.

[0049] The extreme value condition is:

[0050] The solution is:

[0051] in,

[0052] d) In the geodetic coordinate system, the expressions for the optimal lateral jerk, acceleration, velocity, and displacement during the lane change process can be obtained according to the above steps:

[0053]

[0054]

[0055]

[0056]

[0057] Among them, j y (t), a y (t), v y (t) and y(t) represent the functions of vehicle lateral jerk, acceleration, velocity and displacement respectively, t is the independent variable of the function of time, is the lane changing time, D is the lateral distance of the vehicle changing lanes, v y0 is the initial value of the lateral velocity.

[0058] (III) Determination of lane-changing trajectory expression of distributed drive intelligent electric vehicles based on quintic polynomial.

[0059] During the lane change process, the vehicle's longitudinal speed remains constant, and the longitudinal acceleration remains unchanged to minimize the vehicle's longitudinal fluctuation. Therefore, we can obtain:

[0060] v x (t) = v x0

[0061] Among them, v x (t) represents the longitudinal vehicle speed function during lane change, v x0 is the initial value of the longitudinal vehicle speed.

[0062] The lane-changing trajectory of a distributed drive intelligent electric vehicle based on a quintic polynomial is expressed as:

[0063]

[0064] Among them, x(t) is the longitudinal displacement of the lane-changing process, y(t) is the lateral displacement of the lane-changing process, t is the independent variable of the function, and at a certain initial speed vx0 Different lane change times Thus, a series of unconstrained generalized lane-changing trajectory clusters are obtained.

[0065] The selection of lane-changing trajectory clusters that meet the stability domain of distributed drive electric vehicles includes the following parts:

[0066] (I) Analysis of the stability domain mechanism.

[0067] a). To analyze the impact mechanism of the stability domain of distributed drive electric vehicles, it is necessary to establish a four-wheel vehicle model of distributed drive electric vehicles, and its dynamic equation is:

[0068]

[0069]

[0070] Among them, F yij is the tire lateral force, its subscripts ij = fl, fr, rl, rr represent the left front wheel, right front wheel, left rear wheel and right rear wheel of the tire respectively, r represents the yaw angular velocity, is the first-order derivative of the yaw rate; β represents the sideslip angle of the center of mass, is the first-order derivative of the sideslip angle of the center of mass; v x is the longitudinal speed; δ f is the front wheel turning angle of the vehicle; a is the distance from the center of gravity of the vehicle to the front axle, b is the distance from the center of gravity of the vehicle to the rear axle; l f is the front axle wheelbase of the vehicle, l r is the rear axle wheelbase of the vehicle; m is the vehicle mass; I z is the moment of inertia about the z-axis.

[0071] b). Tire slip angle α ij The calculation formula is:

[0072]

[0073]

[0074]

[0075]

[0076] Among them, α ij is the tire slip angle, and its subscripts ij = fl, fr, rl, rr represent the left front wheel, right front wheel, left rear wheel and right rear wheel of the tire respectively.

[0077] c). Tire vertical load F zij The calculation formula is:

[0078]

[0079]

[0080]

[0081]

[0082] Among them, F zij is the vertical load of the tire, where the subscripts ij = fl, fr, rl, and rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire respectively; a x is the vehicle longitudinal acceleration, a y is the lateral acceleration of the vehicle; h is the height of the vehicle's center of mass.

[0083] d) Calculate the tire lateral force equation based on the Fiala tire model:

[0084]

[0085]

[0086] Among them, F yij is the tire lateral force, where the subscripts ij = fl, fr, rl, and rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire respectively; C α is the tire cornering stiffness; α slij is the side slip angle corresponding to the tire entering the saturation area, and its subscripts ij = fl, fr, rl, rr represent the left front wheel, right front wheel, left rear wheel and right rear wheel of the tire respectively; μ is the road adhesion coefficient.

[0087] (II) Analysis of vehicle stability mechanism based on phase plane method.

[0088] a). List the differential equations based on the system state equation:

[0089]

[0090] From the above formula, we can get:

[0091]

[0092] Among them, x1 and x2 are the state parameters of the vehicle system, and f1(x1,x2) and f2(x1,x2) are the differential equations of the vehicle system.

[0093] b) In the vehicle system, assume that the state trajectory x(t) starting from the initial state x0 = (x1(0), x2(0)) remains within the local range and meets the following conditions:

[0094]

[0095] Among them, x(t) is the function of vehicle state parameters changing with time, is a fixed constant. If the condition is met, the system is asymptotically stable locally, so the system is stable. In phase plane analysis, stable trajectories eventually converge to the equilibrium point, while unstable trajectories fail to converge and eventually diverge.

[0096] (III) Based on the phase plane analysis method, the stability domain analysis of different vehicle states is performed.

[0097] Select the influence of different absolute vehicle speeds, road adhesion coefficients, and front wheel angles on the vehicle stability domain. The specific steps include the following:

[0098] a) According to the phase plane analysis method, the functional equation of the vehicle's sideslip angle β and yaw rate r can be obtained. β is the independent variable of the function, and r is the dependent variable of the function. The functional expression of the stable region is:

[0099]

[0100] In the above function expression, b0, b1, b2, and b3 are respectively:

[0101] b0=b / v x , b1=tan(α slrl +α slrl ), b2=(r2-r1) / (β2-β1), b3=r1-β1(r2-r1) / (β2-β1)

[0102] r1=ug / v x , r2=v x / (a+b)(tan((α slfl +α slfr ) / 2+δ max )-tan((α slrl +α slrr ) / 2)),

[0103]

[0104] β2=b / (a+b)(tan((α slfl +α slfr ) / 2+δ max )-tan((α slrl +α slrr ) / 2))+tan((α slrl +α slrr ) / 2).

[0105] In the above formula, δ max The expression is:

[0106]

[0107] Among them, b0, b1, b2, b3 are the unknown coefficients of the stable domain function expression, r1, r2, β1, β2, δ max As an intermediate variable; α slfl , α slfr , α slrl , α slrr are the side slip angles corresponding to the left front, right front, left rear, and right rear tires entering the saturation area; a is the distance from the vehicle's center of gravity to the front axle, b is the distance from the vehicle's center of gravity to the rear axle; v x is the longitudinal velocity of the vehicle, μ is the road adhesion coefficient, and g is the acceleration due to gravity.

[0108] b) Based on the range of the stability domain, different absolute vehicle speeds, road adhesion coefficients, and front wheel angles are selected to analyze different stability domains. The specific steps are as follows:

[0109] When the road adhesion coefficient and the front wheel angle remain unchanged, different vehicle absolute speeds of 5, 10, 15, 20, 25, 30, 35, and 40 m / s are selected for stability domain analysis;

[0110] When the absolute vehicle speed and front wheel angle remain unchanged, different road adhesion coefficients of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 are selected for stability domain analysis;

[0111] When the absolute vehicle speed and road adhesion coefficient remain unchanged, different front wheel steering angles of -30, -25, -20, -15, -10, -5, 0, 5, 10, 15, 20, 25, and 30 degrees are selected for stability domain analysis.

[0112] c) Based on the stability domain set obtained from the above analysis, combined with the vehicle's real-time center of mass sideslip angle and yaw rate, the calculation formula is:

[0113]

[0114]

[0115] Among them, v x is the longitudinal speed of the vehicle, v y is the lateral speed of the vehicle, a y is the lateral acceleration of the vehicle, r is the yaw rate of the vehicle, and β is the sideslip angle of the vehicle's center of mass.

[0116] The vehicle state is judged whether it exceeds the stable domain range based on the sideslip angle and yaw angular velocity of the center of mass. Lane-changing trajectories that exceed the stable domain will be eliminated, and finally lane-changing trajectories that meet the vehicle's stable domain will be retained.

[0117] Considering surrounding vehicles and pedestrians as environmental geometric constraints, and lane lines and traffic regulations as road boundaries, the global feasible region of the vehicle is calculated to include the following parts:

[0118] (I) Take the geometric center of the vehicle and the preceding vehicle and the maximum length from the geometric center to the vehicle body as the geometric circle radius, denoted by R l and R f , a geometric circle is drawn, therefore, the tangent point of the geometric circle of the vehicle and the vehicle in front becomes the critical point of collision, so a boundary line of the global feasible region can be obtained.

[0119] (II) Considering the constraints of lane lines, traffic regulations, or oncoming vehicles, the tangent point between the geometric circle or lane line of the vehicle and the vehicle behind becomes the critical point of collision. Combining the above steps, a closed global feasible region of the vehicle can be obtained. Among the lane change trajectory clusters that meet the stability of distributed drive electric vehicles, trajectory clusters that meet the environmental geometric constraints, lane lines, and traffic regulations are further selected.

[0120] An improved algorithm based on the combination of the analytic hierarchy process (AHP) and the technique of approaching the ideal (TOPSIS) selects the optimal lane-changing trajectory by evaluating stability, trajectory tracking accuracy, comfort, and lane-changing efficiency. The algorithm includes the following parts:

[0121] (I) Establishment of evaluation indicators.

[0122] a) Establish evaluation indicators for lane-changing trajectory planning stability, trajectory tracking accuracy, comfort, and lane-changing efficiency:

[0123] Constructing vehicle stability index:

[0124]

[0125] Among them, J s is the vehicle stability evaluation index, is the lane changing time, F yi (t) is the function expression of the lateral force of the front or rear axle with respect to time, F zi (t) is the vertical load on the front or rear axle as a function of time, is the threshold value of the road adhesion coefficient.

[0126] b) Construct vehicle trajectory tracking accuracy indicators:

[0127]

[0128] Among them, J t is the vehicle trajectory tracking accuracy evaluation index, is the lane changing time, v x is the vehicle longitudinal speed, is the angular velocity function expression of the vehicle's center of mass sideslip angle, is the center of mass sideslip angle threshold; h(t) is the ideal planned trajectory of the vehicle; y(t) is the actual driving trajectory of the vehicle; is the threshold value of the error between the ideal planned trajectory and the actual driving trajectory.

[0129] c). Constructing vehicle comfort index:

[0130]

[0131] Among them, J c is the vehicle comfort evaluation index, is the lane changing time, a y (t) is the longitudinal acceleration of the vehicle, is the vehicle lateral acceleration threshold, θ(t) is the roll angle, is the roll angle threshold.

[0132] d) Constructing lane-changing efficiency indicators:

[0133]

[0134] Among them, J e is the vehicle lane-changing efficiency evaluation index, The lane change time.

[0135] (II) An improved algorithm based on the combination of the analytic hierarchy process (AHP) and the technique of approaching the ideal (TOPSIS).

[0136] a). Construction of judgment matrix. Calculate the optimal lane-changing trajectory cluster from the above trajectory clusters that meet the constraints. This can overcome the tediousness of using the TOPSIS algorithm alone in the multi-objective calculation process and the subjectivity of using the AHP algorithm alone in the calculation process. The target layer A includes m evaluation indicators A1, A2, A3, ..., A m , the target layer A corresponds to the determined influence indicators J1, J2, J3, J4, ..., J of matrix B respectively n , construct a judgment matrix B, whose order is n×n, and the matrix B is as follows:

[0137]

[0138] Among them, the element b in the matrix ij J is the evaluation index for lane-changing trajectory planning i For the lane-changing trajectory evaluation index J j The importance of b ji =1 / bij When element b ij = 1, the two lane-changing trajectory planning evaluation indicators are equally important; when element b ij =3, the lane-changing trajectory planning evaluation index J i Lane-changing trajectory planning evaluation index J j Slightly important; when element b ij =5, the lane-changing trajectory planning evaluation index J i Lane-changing trajectory planning evaluation index J j Obviously important; when element b ij =7, the lane-changing trajectory planning evaluation index J i Lane-changing trajectory planning evaluation index J j Strongly important; when element b ij =9, the lane-changing trajectory planning evaluation index J i Lane-changing trajectory planning evaluation index J j Absolutely important; when the value of an element is 2, 4, 6, 8, it means it is in the middle of the value of 1, 3, 5, 7, 9.

[0139] b) Determine the indicator weights. Based on the above judgment matrix, calculate the sum of each column and normalize the elements of each column. Then, add the normalized results row by row to calculate the square root vector. Finally, the normalized square root vector is used to obtain the ranking weight vector. The calculation formula is:

[0140]

[0141]

[0142]

[0143] in, is the result of normalizing each column element, b ij is the element in the judgment matrix, W i The normalized processing result is the result of adding rows, W i is the sorting weight vector.

[0144] c) Consistency test. First, calculate the maximum eigenvalue of the judgment matrix B, then perform a consistency test on it to obtain the consistency ratio. When the consistency ratio is less than 0.1, the consistency of the judgment matrix meets the conditions.

[0145]

[0146]

[0147]

[0148] Among them, λmax is the maximum eigenvalue of the judgment matrix, matrix B is the judgment matrix, W i is the sorting weight vector, w is the element in the sorting weight vector, CI is the consistency test standard, CR is the consistency ratio, RI is the average random consistency index, and n is the order of rows or columns of the judgment matrix.

[0149] d). Total ranking of the levels. Based on the results of the single ranking of the levels, calculate the optimal weight of the index level relative to the target level. Assume that the target level A includes m evaluation indicators A1, A2, A3, ..., A m , the weights corresponding to the target layer evaluation indicators a1, a2, a3, ..., a m , the indicator layer J includes n evaluation indicators J1, J2, J3, J4, ..., J n , corresponding to a target layer A i The weight is c 1i ,c 2i ,c 3i ,…,c ni Therefore, the weights corresponding to the indicators in the indicator layer are c1, c2, c3, ..., c n .

[0150]

[0151] Among them, c j is the weight corresponding to each indicator in the indicator layer, c ij For a target layer A i The weight of a i is the weight corresponding to the evaluation index of the target layer.

[0152] e) Initial evaluation index establishment. Assume that n evaluation indexes are J={J1,J2,J3,...,J n}, where each evaluation index has m characteristic indexes R = {r1, r2, r3, ..., r m}, then the initial evaluation matrix is:

[0153]

[0154] Among them, r ij is the jth indicator of the i-th evaluation target in the target layer.

[0155] f). Matrix normalization. Because each evaluation index has different dimensions, each evaluation index is normalized. The calculation formula is:

[0156]

[0157] The calculation process of the weighted normalization matrix is:

[0158] H=(v ij ) n×m =(ω j r ij ) n×m

[0159] Among them, r ij is the jth evaluation index of the i-th evaluation target in the target layer, v ij represents the weighted element in row i and column j, ω j Represents the weight of the j-th evaluation index.

[0160] g). Calculation of positive ideal solution, negative ideal solution and the distance between them. The positive ideal solution means that each evaluation index takes the most ideal value solution, and the negative ideal solution means that each evaluation index takes the worst value solution. The expression is:

[0161] The positive ideal solution is:

[0162] The negative ideal solution is:

[0163] Among them, V + is a positive ideal solution, V - is a negative ideal solution, J1 is a benefit-type indicator set, J2 is a cost-type indicator set, v ij Represents the weighted element in row i and column j.

[0164] The distance between each evaluation index and the positive ideal solution and the negative ideal solution is:

[0165]

[0166]

[0167] in, is the distance between each evaluation index and the positive ideal value, is the distance between each evaluation index and the negative ideal value, v ij represents the weighted element in row i and column j, and Corresponding to the positive ideal solution V + and negative ideal solution V - Elements in .

[0168] h). Closeness calculation: calculate the relative closeness between the evaluation index and the ideal solution. The greater the closeness, the better the lane change trajectory.

[0169] Proximity:

[0170] Among them, C i For closeness, is the distance between each evaluation index and the positive ideal value, is the distance between each evaluation index and the negative ideal value. When the closeness C i The larger it is, that is, the closer it is to 1, the better the lane-changing trajectory is. Therefore, the optimal lane-changing trajectory is ultimately obtained.

[0171] The present invention also provides a device, characterized in that it includes:

[0172] one or more processors;

[0173] a memory for storing one or more programs;

[0174] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned distributed drive electric vehicle lane change trajectory planning method.

[0175] The present invention also provides a storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the above-mentioned distributed drive electric vehicle lane change trajectory planning method.

[0176] Compared with the prior art, the present invention has the following beneficial effects:

[0177] 1. Reveal the mechanical constraints and stability domain mechanisms between the functional subsystems of distributed drive electric vehicles, integrate the vehicle's kinematic and dynamic characteristics into the trajectory planning algorithm, and achieve efficient calculation of the optimal trajectory in the vehicle's upper-level planning;

[0178] 2. Based on constraints such as the vehicle stability domain, environmental geometry, and road boundaries, the global feasible region of the vehicle is divided to improve the efficiency of calculating the optimal lane-changing trajectory;

[0179] 3. An improved algorithm based on the combination of the analytic hierarchy process (AHP) and the technique of approaching the ideal (TOPSIS) is used to evaluate the stability index, trajectory tracking accuracy index, comfort index and lane changing efficiency index to select the optimal lane changing trajectory. This algorithm can overcome the tediousness of the multi-objective calculation process when using the TOPSIS algorithm alone and overcome the subjectivity of the calculation process when using the AHP algorithm alone. BRIEF DESCRIPTION OF THE DRAWINGS

[0180] Figure 1 This is a block diagram of the lane-changing trajectory planning system for a distributed drive intelligent electric vehicle in an example of the present invention.

[0181] Figure 2 It is a dynamic model of distributed drive intelligent electric vehicle.

[0182] Figure 3 This is the stability domain analysis result of distributed drive smart electric vehicles regarding vehicle speed.

[0183] Figure 4 This is the stability domain analysis result of the distributed drive smart electric vehicle regarding the adhesion coefficient.

[0184] Figure 5 This is the stability domain analysis result diagram of the distributed drive smart electric vehicle regarding the front wheel angle. DETAILED DESCRIPTION

[0185] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0186] The present invention proposes a distributed drive intelligent electric vehicle lane change trajectory planning method, such as Figure 1-5 As shown, the method of the present invention specifically comprises the following steps:

[0187] The technical solution adopted by the present invention to solve the technical problem includes the following steps:

[0188] Step 1: Based on the curve interpolation method, the vehicle performs curve fitting of the trajectory under certain specific conditions. A quintic polynomial function is selected as the trajectory planning curve fitting function to generate an unconstrained generalized lane change trajectory cluster.

[0189] Step 2: Analyze and summarize the impact mechanism of the stability domain of distributed drive electric vehicles, and select lane-changing trajectory clusters that meet the stability domain of distributed drive electric vehicles based on the above series of lane-changing trajectory clusters;

[0190] Step 3: Consider surrounding vehicles and pedestrians as environmental geometric constraints, and lane lines and traffic regulations as road boundaries to calculate the feasible region of the vehicle.

[0191] In step 4, an improved algorithm based on the combination of the Analytic Hierarchy Process (AHP) and the Technique for Order Preference for Similarity to Ideal Solution (TOPSIS) is used to select the optimal lane-changing trajectory by evaluating the stability index, trajectory tracking accuracy index, comfort index, and lane-changing efficiency index.

[0192] As a further optimization of the above solution, the unconstrained generalized lane-changing trajectory cluster designed in step 1 includes the following parts:

[0193] (I) Determination of the optimal longitudinal displacement, velocity, acceleration, and jerk expressions.

[0194] a) It is necessary to minimize longitudinal fluctuations and establish performance indicators for minimizing longitudinal fluctuations:

[0195]

[0196] The following constraints need to be met:

[0197]

[0198] Among them, η x is the cost function of longitudinal fluctuation, minη x To solve the minimum value of the performance index that minimizes longitudinal fluctuation; τ0 is the initial moment of the lane change trajectory, is the end time of the lane changing process, and the initial time τ0 = 0, so the lane changing process time is x(t) is the longitudinal displacement function of the vehicle during lane changing, and v x (t) represents the longitudinal speed of the vehicle during the lane change process; and a x (t) represents the longitudinal acceleration of the vehicle during lane change; and j x (t) represents the longitudinal jerk of the vehicle during lane change; L is the longitudinal distance of the vehicle during lane change, v x0 is the initial value of the longitudinal velocity during the lane-changing process, is the final value of the longitudinal speed during the lane-changing process;

[0199] b) To solve the above performance indicators, construct the Hamiltonian function H x for

[0200]

[0201] Among them, κ x1 For the corresponding v x The Lagrangian operator, κ x2 For the corresponding a x The Lagrangian operator, κ x3 For the corresponding j x The Lagrangian operator.

[0202] c) According to the Pontryagin maximum principle, the co-state equation is expressed as:

[0203] The solution is: x1 =m0

[0204] The solution is: x2 =m1-m0t

[0205] The solution is:

[0206] Among them, m0, m1 and m2 are unknown constants.

[0207] The extreme value condition is:

[0208] The solution is:

[0209] in,

[0210] d) In the geodetic coordinate system, the expressions for the optimal longitudinal displacement, velocity, acceleration, and jerk during the lane change process can be obtained according to the above steps:

[0211]

[0212]

[0213]

[0214]

[0215] Among them, j x (t), a x (t), v x (t) and x(t) represent the functions of vehicle longitudinal jerk, acceleration, velocity and displacement respectively, t is the independent variable of the function of time, is the lane changing time, L is the longitudinal distance of the vehicle changing lanes, v x0 is the initial value of the longitudinal velocity.

[0216] (II) Determination of the optimal lateral displacement, velocity, acceleration, and jerk expressions.

[0217] a) It is necessary to minimize lateral fluctuations and build performance indicators that minimize lateral fluctuations:

[0218]

[0219] The following constraints need to be met:

[0220]

[0221] Among them, η y is the cost function of lateral fluctuation, minη y To solve the minimum value of the performance index that minimizes lateral fluctuation; τ0 is the initial moment of the lane-changing trajectory, is the end time of the lane changing process, and the initial time τ0 = 0, so the lane changing process time is y(t) represents the lateral displacement function of the vehicle during lane changing, and v y(t) represents the lateral speed of the vehicle during the lane change process; and a y (t) represents the lateral acceleration of the vehicle during lane change; and j y (t) represents the lateral jerk of the vehicle during lane change; D is the lateral distance of the vehicle during lane change; v y0 is the initial value of the lateral velocity during the lane changing process, is the final value of the lateral speed during the lane changing process;

[0222] b) To solve the above performance indicators, construct the Hamiltonian function H y for

[0223]

[0224] Among them, κ y1 For the corresponding v y The Lagrangian operator, κ y2 For a y The Lagrangian operator, κ y3 For the corresponding j y The Lagrangian operator.

[0225] c) According to the Pontryagin maximum principle, the co-state equation is expressed as:

[0226] The solution is: y1 =n0

[0227] The solution is: y2 =n1-n0t

[0228] The solution is:

[0229] Among them, n0, n1 and n2 are unknown constants.

[0230] The extreme value condition is:

[0231] The solution is:

[0232] in,

[0233] d) In the geodetic coordinate system, the expressions for the optimal lateral jerk, acceleration, velocity, and displacement during the lane change process can be obtained according to the above steps:

[0234]

[0235]

[0236]

[0237]

[0238] Among them, j y (t), a y (t), v y (t) and y(t) represent the functions of vehicle lateral jerk, acceleration, velocity and displacement respectively, t is the independent variable of the function of time, is the lane changing time, D is the lateral distance of the vehicle changing lanes, v y0 is the initial value of the lateral velocity.

[0239] (III) Determination of lane-changing trajectory expression of distributed drive intelligent electric vehicles based on quintic polynomial.

[0240] During the lane change process, the vehicle's longitudinal speed remains constant, and the longitudinal acceleration remains unchanged to minimize the vehicle's longitudinal fluctuation. Therefore, we can obtain:

[0241] v x (t) = v x0

[0242] Among them, v x (t) represents the longitudinal vehicle speed function during lane change, v x0 is the initial value of the longitudinal vehicle speed.

[0243] The lane-changing trajectory of a distributed drive intelligent electric vehicle based on a quintic polynomial is expressed as:

[0244]

[0245] Among them, x(t) is the longitudinal displacement of the lane-changing process, y(t) is the lateral displacement of the lane-changing process, t is the independent variable of the function, and at a certain initial speed v x0 Different lane change times Thus, a series of unconstrained generalized lane-changing trajectory clusters are obtained.

[0246] As a further optimization of the above solution, step 2 of screening lane change trajectory clusters that meet the stability domain of distributed drive electric vehicles includes the following parts:

[0247] (I) Analysis of the stability domain mechanism.

[0248] a). To analyze the impact mechanism of the stability domain of distributed drive electric vehicles, it is necessary to establish a four-wheel vehicle model of distributed drive electric vehicles, such as Figure 2 As shown, its dynamic equation is

[0249]

[0250]

[0251] Among them, F yij is the tire lateral force, its subscripts ij = fl, fr, rl, rr represent the left front wheel, right front wheel, left rear wheel and right rear wheel of the tire respectively, r represents the yaw angular velocity, is the first-order derivative of the yaw rate; β represents the sideslip angle of the center of mass, is the first-order derivative of the sideslip angle of the center of mass; v x is the longitudinal speed; δ f is the front wheel turning angle of the vehicle; a is the distance from the center of gravity of the vehicle to the front axle, b is the distance from the center of gravity of the vehicle to the rear axle; l f is the front axle wheelbase of the vehicle, l r is the rear axle wheelbase of the vehicle; m is the vehicle mass; I z is the moment of inertia about the z-axis.

[0252] b). Tire slip angle α ij The calculation formula is:

[0253]

[0254]

[0255]

[0256] Among them, α ij is the tire slip angle, and its subscripts ij = fl, fr, rl, rr represent the left front wheel, right front wheel, left rear wheel and right rear wheel of the tire respectively.

[0257] c). Tire vertical load F zij The calculation formula is:

[0258]

[0259]

[0260]

[0261]

[0262] Among them, F zij is the vertical load of the tire, where the subscripts ij = fl, fr, rl, and rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire respectively; a x is the vehicle longitudinal acceleration, a y is the lateral acceleration of the vehicle; h is the height of the vehicle's center of mass.

[0263] d) Calculate the tire lateral force equation based on the Fiala tire model:

[0264]

[0265]

[0266] Among them, F yij is the tire lateral force, where the subscripts ij = fl, fr, rl, and rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire respectively; C α is the tire cornering stiffness; α slij is the side slip angle corresponding to the tire entering the saturation area, and its subscripts ij = fl, fr, rl, rr represent the left front wheel, right front wheel, left rear wheel and right rear wheel of the tire respectively; μ is the road adhesion coefficient.

[0267] (II) Analysis of vehicle stability mechanism based on phase plane method.

[0268] a). List the differential equations based on the system state equation:

[0269]

[0270] From the above formula, we can get:

[0271]

[0272] Among them, x1 and x2 are the state parameters of the vehicle system, and f1(x1,x2) and f2(x1,x2) are the differential equations of the vehicle system.

[0273] b) In the vehicle system, assume that the state trajectory x(t) starting from the initial state x0 = (x1(0), x2(0)) remains within the local range and meets the following conditions:

[0274]

[0275] Among them, x(t) is the function of vehicle state parameters changing with time, is a fixed constant. If the condition is met, the system is asymptotically stable locally, so the system is stable. In phase plane analysis, stable trajectories eventually converge to the equilibrium point, while unstable trajectories fail to converge and eventually diverge.

[0276] (III) Based on the phase plane analysis method, the stability domain analysis of different vehicle states is performed.

[0277] Select the influence of different absolute vehicle speeds, road adhesion coefficients, and front wheel angles on the vehicle stability domain. The specific steps include the following:

[0278] a) According to the phase plane analysis method, the functional equation of the vehicle's sideslip angle β and yaw rate r can be obtained. β is the independent variable of the function, and r is the dependent variable of the function. The functional expression of the stable region is:

[0279]

[0280] In the above function expression, b0, b1, b2, and b3 are respectively:

[0281] b0=b / v x , b1=tan(α slrl +α slrl ), b2=(r2-r1) / (β2-β1), b3=r1-β1(r2-r1) / (β2-β1)

[0282] r1=ug / v x , r2=v x / (a+b)(tan((α slfl +α slfr ) / 2+δ max )-tan((α slrl +α slrr ) / 2)),

[0283]

[0284] β2=b / (a+b)(tan((α slfl +α slfr ) / 2+δ max )-tan((α slrl +α slrr ) / 2))+tan((α slrl +α slrr ) / 2).

[0285] In the above formula, δ max The expression is:

[0286]

[0287] Among them, b0, b1, b2, b3 are the unknown coefficients of the stable domain function expression, r1, r2, β1, β2, δ max As an intermediate variable; α slfl , α slfr , α slrl , α slrr are the side slip angles corresponding to the left front, right front, left rear, and right rear tires entering the saturation area; a is the distance from the vehicle's center of gravity to the front axle, b is the distance from the vehicle's center of gravity to the rear axle; v x is the longitudinal velocity of the vehicle, μ is the road adhesion coefficient, and g is the acceleration due to gravity.

[0288] b). According to the division range of the stability domain, such as Figure 3-5 As shown in the figure, different vehicle absolute speeds, road adhesion coefficients, and front wheel angles are selected to analyze different stability domains. The specific steps are as follows:

[0289] When the road adhesion coefficient and the front wheel angle remain unchanged, different vehicle absolute speeds of 10, 15, 20, and 25 m / s are selected for stability domain analysis;

[0290] When the absolute vehicle speed and front wheel angle remain unchanged, different road adhesion coefficients of 0.2, 0.4, 0.6, and 0.8 are selected and the stability domain analysis is performed respectively;

[0291] When the absolute vehicle speed and road adhesion coefficient remain unchanged, different front wheel steering angles of 0, 5, 10, and 15 degrees are selected for stability domain analysis.

[0292] c) Based on the stability domain set obtained from the above analysis, combined with the vehicle's real-time center of mass sideslip angle and yaw rate, the calculation formula is:

[0293]

[0294]

[0295] Among them, v x is the longitudinal speed of the vehicle, v y is the lateral speed of the vehicle, a y is the lateral acceleration of the vehicle, r is the yaw rate of the vehicle, and β is the sideslip angle of the vehicle's center of mass.

[0296] The vehicle state is judged whether it exceeds the stable domain range based on the sideslip angle and yaw angular velocity of the center of mass. Lane-changing trajectories that exceed the stable domain will be eliminated, and finally lane-changing trajectories that meet the vehicle's stable domain will be retained.

[0297] As a further optimization of the above solution, step 3 considers surrounding vehicles and pedestrians as environmental geometric constraints, and lane lines and traffic regulations as road boundaries, and calculates the global feasible region of the vehicle, which includes the following parts:

[0298] (I) Take the geometric center of the vehicle and the preceding vehicle and the maximum length from the geometric center to the vehicle body as the geometric circle radius, denoted by R l and R f , a geometric circle is drawn, therefore, the tangent point of the geometric circle of the vehicle and the vehicle in front becomes the critical point of collision, so a boundary line of the global feasible region can be obtained.

[0299] (II) Considering the constraints of lane lines, traffic regulations, or oncoming vehicles, the tangent point between the geometric circle or lane line of the vehicle and the vehicle behind becomes the critical point of collision. Combining the above steps, a closed global feasible region of the vehicle can be obtained. Among the lane change trajectory clusters that meet the stability of distributed drive electric vehicles, trajectory clusters that meet the environmental geometric constraints, lane lines, and traffic regulations are further selected.

[0300] As a further optimization of the above scheme, step 4 is based on an improved algorithm that combines the analytic hierarchy process (AHP) and the technique of approaching the ideal (TOPSIS). It selects the optimal lane-changing trajectory by evaluating the stability index, trajectory tracking accuracy index, comfort index, and lane-changing efficiency index. It includes the following parts:

[0301] (I) Establishment of evaluation indicators.

[0302] a) Establish evaluation indicators for lane-changing trajectory planning stability, trajectory tracking accuracy, comfort, and lane-changing efficiency:

[0303] Constructing vehicle stability index:

[0304]

[0305] Among them, J s is the vehicle stability evaluation index, is the lane changing time, F yi (t) is the function expression of the lateral force of the front or rear axle with respect to time, F zi (t) is the vertical load on the front or rear axle as a function of time, is the threshold value of the road adhesion coefficient.

[0306] b) Construct vehicle trajectory tracking accuracy indicators:

[0307]

[0308] Among them, J t is the vehicle trajectory tracking accuracy evaluation index, is the lane changing time, v x is the vehicle longitudinal speed, is the angular velocity function expression of the vehicle's center of mass sideslip angle, is the center of mass sideslip angle threshold; h(t) is the ideal planned trajectory of the vehicle; y(t) is the actual driving trajectory of the vehicle; is the threshold value of the error between the ideal planned trajectory and the actual driving trajectory.

[0309] c). Constructing vehicle comfort index:

[0310]

[0311] Among them, J c is the vehicle comfort evaluation index, is the lane changing time, a y (t) is the longitudinal acceleration of the vehicle, is the vehicle lateral acceleration threshold, θ(t) is the roll angle, is the roll angle threshold.

[0312] d) Constructing lane-changing efficiency indicators:

[0313]

[0314] Among them, J e is the vehicle lane-changing efficiency evaluation index, The lane change time.

[0315] (II) An improved algorithm based on the combination of the analytic hierarchy process (AHP) and the technique of approaching the ideal (TOPSIS).

[0316] a). Construction of judgment matrix. Calculate the optimal lane-changing trajectory cluster from the above trajectory clusters that meet the constraints. This can overcome the tediousness of using the TOPSIS algorithm alone in the multi-objective calculation process and the subjectivity of using the AHP algorithm alone in the calculation process. The target layer A includes m evaluation indicators A1, A2, A3, ..., A m , the target layer A corresponds to the determined influence indicators J1, J2, J3, J4, ..., J of matrix B respectively n , construct a judgment matrix B, whose order is n×n, and the matrix B is as follows:

[0317]

[0318] Among them, the element b in the matrix ij J is the evaluation index for lane-changing trajectory planning i For the lane-changing trajectory evaluation index J j The importance of b ji =1 / b ij When element b ij = 1, the two lane-changing trajectory planning evaluation indicators are equally important; when element b ij =3, the lane-changing trajectory planning evaluation index J i Lane-changing trajectory planning evaluation index J j Slightly important; when element b ij =5, the lane-changing trajectory planning evaluation index J i Lane-changing trajectory planning evaluation index J j Obviously important; when element b ij =7, the lane-changing trajectory planning evaluation index Ji Lane-changing trajectory planning evaluation index J j Strongly important; when element b ij =9, the lane-changing trajectory planning evaluation index J i Lane-changing trajectory planning evaluation index J j Absolutely important; when the value of an element is 2, 4, 6, 8, it means it is in the middle of the value of 1, 3, 5, 7, 9.

[0319] b) Determine the indicator weights. Based on the above judgment matrix, calculate the sum of each column and normalize the elements of each column. Then, add the normalized results row by row to calculate the square root vector. Finally, the normalized square root vector is used to obtain the ranking weight vector. The calculation formula is:

[0320]

[0321]

[0322]

[0323] in, is the result of normalizing each column element, b ij is the element in the judgment matrix, The normalized processing result is the result of adding rows, W i is the sorting weight vector.

[0324] c) Consistency test. First, calculate the maximum eigenvalue of the judgment matrix B, then perform a consistency test on it to obtain the consistency ratio. When the consistency ratio is less than 0.1, the consistency of the judgment matrix meets the conditions.

[0325]

[0326]

[0327]

[0328] Among them, λ max is the maximum eigenvalue of the judgment matrix, matrix B is the judgment matrix, W i is the sorting weight vector, w is the element in the sorting weight vector, CI is the consistency test standard, CR is the consistency ratio, RI is the average random consistency index, and n is the order of rows or columns of the judgment matrix.

[0329] d). Total ranking of the levels. Based on the results of the single ranking of the levels, calculate the optimal weight of the index level relative to the target level. Assume that the target level A includes m evaluation indicators A1, A2, A3, ..., A m, the weights corresponding to the target layer evaluation indicators a1, a2, a3, ..., a m , the indicator layer J includes n evaluation indicators J1, J2, J3, J4, ..., J n , corresponding to a target layer A i The weight is c 1i ,c 2i ,c 3i ,…,c ni Therefore, the weights corresponding to the indicators in the indicator layer are c1, c2, c3, ..., c n .

[0330]

[0331] Among them, c j is the weight corresponding to each indicator in the indicator layer, c ij For a target layer A i The weight of a i is the weight corresponding to the evaluation index of the target layer.

[0332] e) Initial evaluation index establishment. Assume that n evaluation indexes are J={J1,J2,J3,...,J n}, where each evaluation index has m characteristic indexes R = {r1, r2, r3, ..., r m}, then the initial evaluation matrix is:

[0333]

[0334] Among them, r ij is the jth indicator of the i-th evaluation target in the target layer.

[0335] f). Matrix normalization. Because each evaluation index has different dimensions, each evaluation index is normalized. The calculation formula is:

[0336]

[0337] The calculation process of the weighted normalization matrix is:

[0338] H=(v ij ) n×m =(ω j r ij ) n×m

[0339] Among them, r ij is the jth evaluation index of the i-th evaluation target in the target layer, v ij represents the weighted element in row i and column j, ω j Represents the weight of the j-th evaluation index.

[0340] g). Calculation of positive ideal solution, negative ideal solution and the distance between them. The positive ideal solution means that each evaluation index takes the most ideal value solution, and the negative ideal solution means that each evaluation index takes the worst value solution. The expression is:

[0341] The positive ideal solution is:

[0342] The negative ideal solution is:

[0343] Among them, V + is a positive ideal solution, V - is a negative ideal solution, J1 is a benefit-type indicator set, J2 is a cost-type indicator set, v ij Represents the weighted element in row i and column j.

[0344] The distance between each evaluation index and the positive ideal solution and the negative ideal solution is:

[0345]

[0346]

[0347] in, is the distance between each evaluation index and the positive ideal value, is the distance between each evaluation index and the negative ideal value, v ij represents the weighted element in row i and column j, and Corresponding to the positive ideal solution V + and negative ideal solution V - Elements in .

[0348] h). Closeness calculation: calculate the relative closeness between the evaluation index and the ideal solution. The greater the closeness, the better the lane change trajectory.

[0349] Proximity:

[0350] Among them, C i For closeness, is the distance between each evaluation index and the positive ideal value, is the distance between each evaluation index and the negative ideal value. When the closeness C i The larger it is, that is, the closer it is to 1, the better the lane-changing trajectory is. Therefore, the optimal lane-changing trajectory is ultimately obtained.

[0351] An embodiment of the present invention further provides an electronic device, including:

[0352] one or more processors;

[0353] a memory for storing one or more programs;

[0354] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned distributed drive electric vehicle lane change trajectory planning method.

[0355] Through this device, a lane-changing trajectory planning method for a distributed drive electric vehicle can be obtained, and the obtained optimal trajectory can be sent to the distributed drive intelligent electric vehicle.

[0356] An embodiment of the present invention further provides a storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for planning lane-changing trajectories of a distributed drive electric vehicle.

[0357] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as such herein, will not be interpreted in an idealized or overly formal sense.

[0358] The meaning of "and / or" in this application means that both situations where each exists alone or both exist at the same time are included.

[0359] The term “connection” as used in this application may mean a direct connection between components or an indirect connection between components via other components.

[0360] With the above-described preferred embodiments of the present invention as a guide, and with reference to the above description, relevant personnel are fully capable of making various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification and must be determined according to the scope of the claims.

Claims

1. A distributed drive electric vehicle lane change trajectory planning method, characterized in that: The following steps are involved: Generate unconstrained generalized lane-changing trajectory clusters based on the vehicle trajectory planning curve fitting function; Among the generated unconstrained generalized lane-changing trajectory clusters, the lane-changing trajectory clusters that meet the stability domain of distributed drive electric vehicles are selected; In the selected lane-changing trajectory cluster that satisfies the stability domain of the distributed drive electric vehicle, the feasible domain of the vehicle is calculated according to the geometric constraints of the environment and the road boundary. In the calculated vehicle feasible region, an improved algorithm combining the analytic hierarchy process and the ideal approximation technique is used to select the optimal lane-changing trajectory by evaluating stability, trajectory tracking accuracy, comfort, and lane-changing efficiency. Among the generated unconstrained generalized lane-changing trajectory clusters, the lane-changing trajectory clusters that satisfy the stability domain of distributed drive electric vehicles are selected, including: Build a four-wheel vehicle model of a distributed drive electric vehicle: Among them, F yij is the tire lateral force, its subscripts ij = fl, fr, rl, rr represent the left front wheel, right front wheel, left rear wheel and right rear wheel of the tire respectively, r represents the yaw angular velocity, is the first-order derivative of the yaw rate; β represents the sideslip angle of the center of mass, is the first-order derivative of the sideslip angle of the center of mass; v x is the longitudinal speed; δ f is the front wheel turning angle of the vehicle; a is the distance from the center of gravity of the vehicle to the front axle, b is the distance from the center of gravity of the vehicle to the rear axle; l f is the front axle wheelbase of the vehicle, l r is the rear axle wheelbase of the vehicle; m is the vehicle mass; I z is the moment of inertia around the z-axis; Calculate the slip angles of the four tires: Among them, α ij is the tire slip angle, where the subscripts ij = fl, fr, rl, and rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire respectively; Calculate the vertical load on the four tires: Among them, F zij is the vertical load of the tire, where the subscripts ij = fl, fr, rl, and rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire respectively; a x is the vehicle longitudinal acceleration, a y is the lateral acceleration of the vehicle; h is the height of the vehicle's center of mass; Calculate the tire lateral force equation based on the Fiala tire model: Among them, F yij is the tire lateral force, where the subscripts ij = fl, fr, rl, and rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire respectively; C α is the tire cornering stiffness; α slij is the side slip angle corresponding to the tire entering the saturation area, where the subscripts ij = fl, fr, rl, and rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the tire respectively; μ is the road adhesion coefficient; The vehicle stability domain mechanism is analyzed based on the phase plane method, and the differential equations are listed according to the system state equation: From the above formula, we can get: Among them, x1 and x2 are the state parameters of the vehicle system, f1(x1,x2) and f2(x1,x2) are the differential equations of the vehicle system; In a vehicle system, it is assumed that the state trajectory x(t) starting from the initial state x0 = (x1(0), x2(0)) remains within the local range and meets the following conditions: Among them, x(t) is the function of vehicle state parameters changing with time, x l ∈R is a certain constant; Based on the phase plane analysis method, the stability domain of different vehicle states is analyzed. The influence of different absolute vehicle speeds, road adhesion coefficients, and front wheel angles on the vehicle stability domain are selected. The specific steps include the following: According to the phase plane analysis method, the functional equation of the vehicle's center of mass sideslip angle β and yaw rate r can be obtained. β is the independent variable of the function, r is the dependent variable of the function, and the functional expression of the stable region is: In the above function expression, b0, b1, b2, and b3 are respectively: b0=b / v x ,b1=tan(α slrl +a slrl ),b2=(r2-r1) / (β2-β1),b3=r1-β1(r2-r1) / (β2-β1) r1=ug / v x ,r2=v x / (a+b)(tan((α slfl +α slfr ) / 2+δ max )-tan((α slrl +α slrr ) / 2)), In the above formula, δ max The expression is: Among them, b0, b1, b2, b3 are the unknown coefficients of the stable domain function expression, r1, r2, β1, β2, δ max As an intermediate variable; α slfl , α slfr , α slrl , α slrr are the side slip angles corresponding to the left front, right front, left rear, and right rear tires entering the saturation area; a is the distance from the vehicle's center of gravity to the front axle, b is the distance from the vehicle's center of gravity to the rear axle; v x is the longitudinal velocity of the vehicle, μ is the road adhesion coefficient, and g is the acceleration due to gravity; According to the division range of the stability domain, different vehicle absolute speeds, road adhesion coefficients, and front wheel angles are selected to analyze different stability domains; According to the stability domain set obtained from the above analysis, combined with the vehicle's real-time center of mass sideslip angle and yaw rate calculation formula: Among them, v x is the longitudinal speed of the vehicle, v y is the lateral speed of the vehicle, a y is the lateral acceleration of the vehicle, r is the yaw rate of the vehicle, and β is the sideslip angle of the vehicle's center of mass; The vehicle state is judged whether it exceeds the stable domain range based on the sideslip angle and yaw angular velocity of the center of mass. Lane-changing trajectories that exceed the stable domain will be eliminated, and finally lane-changing trajectories that meet the vehicle's stable domain will be retained.

2. A distributed drive electric vehicle lane change trajectory planning method according to claim 1, characterized in that: According to the vehicle trajectory planning curve fitting function, the expression of the unconstrained generalized lane-changing trajectory cluster is generated as follows: Among them, x(t) is the longitudinal displacement of the lane changing process, y(t) is the lateral displacement of the lane changing process, t is the independent variable of the function, and v x0 is the initial vehicle speed, is the lane changing time, and D is the lateral distance of the vehicle changing lanes.

3. The method for planning lane-changing trajectories of a distributed drive electric vehicle according to claim 1, wherein: Generate a cluster of unconstrained generalized lane-changing trajectories, including: Constructing performance indicators that minimize longitudinal fluctuations: The constraints that need to be met are: Among them, η x is the cost function of longitudinal fluctuation, minη x To solve the minimum value of the performance index that minimizes longitudinal fluctuation; τ0 is the initial moment of the lane change trajectory, is the end time of the lane changing process, and the initial time τ0 = 0, so the lane changing process time is x(t) is the longitudinal displacement function of the vehicle during lane changing, and v x (t) represents the longitudinal speed of the vehicle during the lane change process; and a x (t) represents the longitudinal acceleration of the vehicle during lane change; and j x (t) represents the longitudinal jerk of the vehicle during lane change; L is the longitudinal distance of the vehicle during lane change, v x0 is the initial value of the longitudinal velocity during the lane-changing process, is the final value of the longitudinal speed during the lane changing process; The constructed Hamiltonian function H x for: Among them, κ x1 For the corresponding v x The Lagrangian operator, κ x1 =m0;κ x2 For the corresponding a x The Lagrangian operator, κ x2 =m1-m0t;κ x3 For the corresponding j x Lagrangian operator; in, According to the Hamiltonian function H x , solve the performance index: In the geodetic coordinate system, the expressions for the optimal longitudinal displacement, velocity, acceleration, and jerk during the lane change process are obtained as follows: Among them, j x (t), a x (t), v x (t) and x(t) represent the functions of vehicle longitudinal jerk, acceleration, velocity and displacement respectively, t is the independent variable of the function of time, is the lane changing time, L is the longitudinal distance of the vehicle changing lanes, v x0 is the initial value of the longitudinal velocity; It is necessary to minimize lateral fluctuations and construct a performance indicator that minimizes lateral fluctuations: The following constraints need to be met: Among them, η y is the cost function of lateral fluctuation, minη y To solve the minimum value of the performance index that minimizes lateral fluctuation; τ0 is the initial moment of the lane-changing trajectory, is the end time of the lane changing process, and the initial time τ0 = 0, so the lane changing process time is y(t) represents the lateral displacement function of the vehicle during lane changing, and v y (t) represents the lateral speed of the vehicle during the lane change process; and a y (t) represents the lateral acceleration of the vehicle during lane change; and j y (t) represents the lateral jerk of the vehicle during lane change; D is the lateral distance of the vehicle during lane change; v y0 is the initial value of the lateral velocity during the lane changing process, is the final value of the lateral speed during the lane changing process; Construct Hamiltonian function H y : Among them, κ y1 For the corresponding v y The Lagrangian operator, κ y1 =n0;κ y2 For the corresponding a y The Lagrangian operator, κ y2 =n1-n0t;κ y3 For the corresponding j y The Lagrangian operator, in, According to the Hamiltonian function H y , solve the performance index: In the geodetic coordinate system, the expressions for the optimal lateral jerk, acceleration, velocity, and displacement during the lane change process are obtained as follows: Among them, j y (t), a y (t), v y (t) and y(t) are functions of the vehicle’s lateral jerk, acceleration, velocity, and displacement, respectively. D is the lateral distance the vehicle changes lanes. v y0 is the initial value of the lateral velocity; During the lane change process, the vehicle's longitudinal speed remains constant, and the longitudinal acceleration remains unchanged to minimize the vehicle's longitudinal fluctuation. Therefore, we can obtain: v x (t)=v x0 Among them, v x (t) represents the longitudinal vehicle speed function during lane change, v x0 is the initial value of the longitudinal vehicle speed; At a certain initial speed v x0 Different lane change times Thus, a series of unconstrained generalized lane-changing trajectory clusters are obtained:

4. The method for planning lane-changing trajectories of a distributed drive electric vehicle according to claim 1, wherein: In the selected lane-changing trajectory cluster that satisfies the stability domain of the distributed drive electric vehicle, the feasible domain of the vehicle is calculated according to the environmental geometric constraints and road boundaries, including: Take the geometric center of the vehicle and the vehicle in front as the geometric circle radius, and take the maximum length from the geometric center to the vehicle body as the geometric circle radius, respectively denoted as R l and R f , draw a geometric circle; Considering the lane lines, traffic regulations, or constraints of the vehicle behind, the tangent point between the geometric circle or lane line of the vehicle behind becomes the critical point of collision; Combined with the final retained lane-changing trajectory that satisfies the vehicle's stability domain, a closed global feasible domain of the vehicle is obtained.

5. The method for planning lane-changing trajectories of a distributed drive electric vehicle according to claim 1, wherein: In the calculated vehicle feasible region, an improved algorithm combining the analytic hierarchy process and the ideal approximation technique is used to select the optimal lane-changing trajectory by evaluating stability, trajectory tracking accuracy, comfort, and lane-changing efficiency. These indicators include: Establish stability index, trajectory tracking accuracy index, comfort index and lane changing efficiency index for lane changing trajectory planning respectively; Based on an improved algorithm combining the analytic hierarchy process and the ideal approximation technique, the optimal lane-changing trajectory cluster is calculated from the trajectory clusters that meet the constraints.

6. The method for planning lane-changing trajectories of a distributed drive electric vehicle according to claim 3, characterized in that: Constructed vehicle stability index: Among them, J s is the vehicle stability evaluation index, is the lane changing time, F yi (t) is the function expression of the lateral force of the front or rear axle with respect to time, F zi (t) is the vertical load on the front or rear axle as a function of time, is the threshold value of the road adhesion coefficient; The constructed vehicle trajectory tracking accuracy index is: Among them, J t is the vehicle trajectory tracking accuracy evaluation index, is the lane changing time, v x is the vehicle longitudinal speed, is the angular velocity function expression of the vehicle's center of mass sideslip angle, is the center of mass sideslip angle threshold; h(t) is the ideal planned trajectory of the vehicle; y(t) is the actual driving trajectory of the vehicle; is the threshold value of the error between the ideal planned trajectory and the actual driving trajectory; The constructed vehicle comfort index is: Among them, J c is the vehicle comfort evaluation index, is the lane changing time, a y (t) is the longitudinal acceleration of the vehicle, is the vehicle lateral acceleration threshold, θ(t) is the roll angle, is the roll angle threshold; Constructed lane-changing efficiency index: Among them, J e is the vehicle lane-changing efficiency evaluation index, is the lane change time; Based on an improved algorithm combining the analytic hierarchy process and the ideal approximation technique, the optimal lane-changing trajectory cluster is calculated from the trajectory clusters that meet the constraints.

7. The method for planning lane-changing trajectories of a distributed drive electric vehicle according to claim 6, characterized in that: The steps of the improved algorithm are as follows: The target layer A includes m evaluation indicators A1, A2, A3, ..., A m , the target layer A corresponds to the determination of the influence indicators J1, J2, J3, J4, ..., J of the judgment matrix B. n , construct a judgment matrix B, whose order is n×n, and the judgment matrix B is as follows: Among them, the element b in the matrix ij J is the evaluation index for lane-changing trajectory planning i For the lane-changing trajectory evaluation index J j The importance of b ji =1 / b ij ; Determine the indicator weights. According to the above judgment matrix, calculate the sum of each column and normalize the elements of each column. Then add the normalized results by row to calculate the square root vector. Finally, normalize the square root vector to get the ranking weight vector. The calculation formula is: in, is the result of normalizing each column element, b ij is the element in the judgment matrix, The normalized processing result is the result of adding rows, W i is the sorting weight vector; Consistency test: first calculate the maximum eigenvalue of the judgment matrix B, then perform a consistency test on it to obtain the consistency ratio. When the consistency ratio is less than 0.1, the consistency of the judgment matrix meets the conditions. Among them, λ max is the maximum eigenvalue of the judgment matrix, matrix B is the judgment matrix, W i is the sorting weight vector, w is the element in the sorting weight vector, CI is the consistency test standard, CR is the consistency ratio, RI is the average random consistency index, and n is the order of rows or columns of the judgment matrix; The total ranking of the levels is based on the results of the single ranking of the levels. The optimal synthetic weight of the indicator level relative to the target level is calculated. Assume that the target level A includes m evaluation indicators A1, A2, A3, ..., A m , the weights corresponding to the target layer evaluation indicators a1, a2, a3, ..., a m , the indicator layer J includes n evaluation indicators J1, J2, J3, J4, ..., J n , corresponding to a target layer A i The weight is c 1i ,c 2i ,c 3i ,…,c ni ; Therefore, the weights corresponding to the various indicators in the indicator layer are c1, c2, c3, ..., c n : Among them, c j is the weight corresponding to each indicator in the indicator layer, c ij For a target layer A i The weight of a i is the weight corresponding to the evaluation index of the target layer; Initial evaluation index is established, assuming that n evaluation indexes are J = {J1, J2, J3, ..., J n }, where each evaluation index has m characteristic indexes R = {r1, r2, r3, ..., r m }, then the initial evaluation matrix is: Among them, r ij is the jth indicator of the i-th evaluation target in the target layer; Matrix standardization normalizes each evaluation index, and the calculation formula is: The calculation process of the weighted normalization matrix is: H=(v ij ) n×m =(ω j r ij ) n×m Among them, r ij is the jth evaluation index of the i-th evaluation target in the target layer, v ij represents the weighted element in row i and column j, ω j represents the weight of the jth evaluation index; The calculation of positive ideal solution, negative ideal solution and the distance between them. The positive ideal solution means that each evaluation index takes the most ideal value solution, and the negative ideal solution means that each evaluation index takes the worst value solution. The expression is: The positive ideal solution is: The negative ideal solution is: Among them, V + is a positive ideal solution, V - is a negative ideal solution, J1 is a benefit-type indicator set, J2 is a cost-type indicator set, v ij Represents the weighted element in row i and column j; The distance between each evaluation index and the positive ideal solution and the negative ideal solution is: in, is the distance between each evaluation index and the positive ideal value, is the distance between each evaluation index and the negative ideal value, v ij represents the weighted element in row i and column j, and Corresponding to the positive ideal solution V + and negative ideal solution V - Elements in Closeness calculation, calculate the relative closeness between the evaluation index and the ideal solution: Proximity: Among them, C i For closeness, is the distance between each evaluation index and the positive ideal value, is the distance between each evaluation index and the negative ideal value. When the closeness C i The larger it is, that is, the closer it is to 1, the better the lane-changing trajectory is. Therefore, the optimal lane-changing trajectory is ultimately obtained.

8. A device, characterized in that include: one or more processors; a memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the lane change trajectory planning method for a distributed drive electric vehicle according to any one of claims 1 to 7.

9. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the distributed drive electric vehicle lane change trajectory planning method according to any one of claims 1 to 7 is implemented.

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