An intelligent networked vehicle lane-changing decision and trajectory planning method

Abstract

The application belongs to the field of intelligent networked automobile automatic driving or auxiliary driving, and discloses a lane-changing decision and trajectory planning method for intelligent networked automobile, wherein sensors such as laser radar, millimeter wave radar and vehicle-mounted camera carried by an intelligent networked automobile SC are used to perceive the road traffic environment, the position and speed information of a front vehicle FC in a current lane, a front vehicle PC in a target lane and a rear vehicle LC are considered, an optimal strategy is solved through dynamic game, and the optimal lane-changing trajectory of the vehicle is planned based on different strategies, the comfort of the driver and the lane-changing efficiency. The application can effectively improve the safety and efficiency of lane-changing.

Description

Technical Field

[0001] This invention belongs to the field of autonomous driving or assisted driving of intelligent connected vehicles, and specifically relates to a method for lane changing decision and trajectory planning of intelligent connected vehicles. Background Technology

[0002] With the rapid breakthroughs in advanced technologies such as artificial intelligence, big data, 5G communication, and cloud computing in recent years, the field of intelligent connected vehicles has also shown a vigorous development trend. Under this trend, vehicles are able to achieve more intelligent perception, decision-making, and execution functions, providing richer and more feasible solutions to problems such as traffic congestion and frequent traffic accidents. Autonomous lane changing, as one of the basic functions of intelligent connected vehicles, directly affects the safety and traffic efficiency of the vehicle itself and road traffic through lane-changing decisions and corresponding lane-changing trajectory planning.

[0003] A review of relevant patents and papers reveals that existing methods for lane-changing decision-making and trajectory planning in intelligent connected vehicles rarely consider the impact of changes in the surrounding traffic environment during lane-changing. Patent CN106777776A discloses a vehicle lane-changing decision-making method based on a support vector machine model; patent CN110298131B discloses a method for establishing an autonomous driving lane-changing decision-making model in a hybrid driving environment, establishing a multi-step dynamic game framework to determine the final lane-changing decision and trajectory planning method. However, neither of these methods considers the impact of changes in the motion state of surrounding vehicles during lane-changing on lane-changing decisions and trajectory planning, and their practical application effectiveness needs improvement. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method for lane-changing decision-making and trajectory planning for intelligent connected vehicles. This invention aims to solve the problem that existing methods do not consider the impact of changes in the motion state of surrounding vehicles during lane-changing on lane-changing decision-making and trajectory planning, resulting in poor application performance.

[0005] This invention provides a method for lane-changing decision-making and trajectory planning of intelligent connected vehicles, comprising the following steps:

[0006] S1. Define the payoff function for the master-slave game between lane-changing intelligent connected vehicles (SC) and right-of-way competing vehicles (LC);

[0007] Vehicles competing for right-of-way refer to vehicles in the target lane that compete for position with intelligent connected vehicles changing lanes, i.e., vehicles following in the target lane;

[0008] S2. Establish a Cartesian coordinate system with the current location of the lane-changing vehicle as the origin, the horizontal driving direction as the X-axis, and the direction from the lane where the lane-changing vehicle is located to the target lane and perpendicular to the X-axis as the Y-axis.

[0009] S3. Use the sensing devices of intelligent connected vehicles to collect driving status data and road environment information of themselves and competing vehicles changing lanes, and solve the game equilibrium to obtain the decision result;

[0010] If the decision result is to continue changing lanes, proceed to step S4;

[0011] If the decision result is lane keeping, proceed to step S5;

[0012] If the decision result is to revert to the previous horizontal line, then proceed to step S6;

[0013] S4. Based on the continuous lane-changing strategy obtained in step S3, plan the lane-changing trajectory;

[0014] S5. Based on the lane keeping strategy obtained in step S3, perform lane keeping trajectory planning;

[0015] S6. Based on the rollback strategy obtained in step S3, plan the vehicle's driving trajectory to roll back to the position of the previous horizontal line;

[0016] S7. Repeat steps S3 to S6 until the intelligent connected vehicle completes the lane change.

[0017] Furthermore, step S1 includes the following:

[0018] S1.1 defines a vehicle;

[0019] The vehicle that changes lanes is defined as SC, the vehicle in front of the vehicle changing lanes is FC, the vehicle behind the target lane is LC, and the vehicle in front of the target lane is PC.

[0020] In this game, the lane-changing intelligent connected vehicle (SC) and the right-of-way competing vehicle (LC) are the main and follower participants, with SC being the leader and LC being the follower.

[0021] S1.2 Calculate the game payoff for SC. The payoff function is:

[0022] E SC =(1-μ SC )*E safe1 +μ SC E vel

[0023] In the formula, E SC For the total revenue of lane-changing vehicles SC; μ SC This is the weighting coefficient for the vehicle's SC driving style; the value represents the driving type, μ. SC The larger the value, the more aggressive the driving behavior; E safe1 For the safety benefits of lane-changing vehicles SC; E vel For the speed gain of lane-changing vehicles (SC);

[0024] E safe1 The calculation formula is as follows:

[0025]

[0026] In the formula, L is the vehicle width; d is the lateral distance between vehicle SC and vehicle LC; a represents the lateral safety distance between the two vehicles; e1 is the safety factor of vehicle SC; T h T is the headway of vehicle SC relative to vehicle LC. e1 T1 represents the expected headway of vehicle SC; T2 represents the initial headway between vehicle FC and vehicle SC; W represents the lateral distance between the centerlines of the two lanes; T3 represents the initial headway between vehicle PC and vehicle LC.

[0027] E vel The calculation formula is as follows:

[0028]

[0029] In the formula, v PC v is the speed of the vehicle PC ahead in the target lane. FC v is the speed of the vehicle FC. desire The desired speed of SC;

[0030] S1.3 Calculate the game payoff for vehicle LC competing for right-of-way, the payoff function is:

[0031] E LC =(1-μ LC )*E safe2 +μ LC *E space

[0032] In the formula, E LC The total revenue of vehicle LC; μ LC The weighting coefficient for LC driver driving style; the value represents the driver type, μ. LC The larger the value, the more aggressive the driver's driving behavior; E safe2 For the safety benefits of vehicle LC; E space For the space benefits of vehicle LC;

[0033] E safe2 and E space The calculation formula is as follows:

[0034]

[0035]

[0036]

[0037] In the formula, d is the lateral distance between vehicle SC and vehicle LC; L is the vehicle width; a represents the safe lateral distance between the two vehicles; T h e2 is the headway of vehicle SC relative to vehicle LC; e2 is the safety factor of vehicle LC; T e2 This represents the expected headway of car number 2.

[0038] Furthermore, the specific content of step S2 is as follows: taking the lateral distance between the center line of the current lane of the intelligent connected vehicle and the center line of the target lane as a benchmark, five horizontal lines along the direction of vehicle travel are set at equal intervals to obtain the vertical coordinate yi of each horizontal line, where i represents the sequence number of each horizontal line, and the value range is 1-5. Each vertical coordinate yi serves as a node for the intelligent connected vehicle to perform a repeated game.

[0039] Furthermore, in step S3, the expression for the optimal merging error function is as follows. In step S3, the process of solving the game equilibrium to obtain the decision result is as follows:

[0040] Based on the payoff functions designed in steps S1.2 and S1.3, determine the payoff matrix {E} of lane-changing vehicle SC and competing vehicle LC in the target lane under different game strategies. payoff (a x ,b y )};

[0041] In the formula, E payoff This represents the total return of SC or LC under the current strategy combination; x = {1, 2, 3}, y = {1, 2}, a1 indicates that SC continues to change lanes, a2 indicates that SC moves along the current horizontal line y. i Driving, a3 indicates SC reverts to the previous horizontal line y. i-1 b1 represents LC acceleration, b2 represents LC deceleration, (a x ,b y () represents a combination of game strategies;

[0042] II. Solve using the payoff matrix of a master-slave game to find the equilibrium strategy, as shown in the following equation:

[0043]

[0044] In the formula, a* and b* represent the corresponding strategies that achieve the maximum payoff under the game equilibrium of SC and LC.

[0045] III. Add minimum headway to lane change safety constraints;

[0046] The minimum safe headway is set to 2 seconds, and the following formula applies:

[0047]

[0048] In the formula, T safe For safe headway.

[0049] Furthermore, step S4 includes the following sub-steps:

[0050] S4.1 Fit the lane-changing trajectory based on the cubic polynomial parametric equation;

[0051]

[0052] In the formula, x(t) and y(t) are the longitudinal and lateral positions of the vehicle at time t, respectively; a0, a1, a2, a3, b0, b1, b2, b3 are the coefficients of the polynomial curve;

[0053] S4.2 At each time step, update the coordinate system with the current vehicle position as the origin, and assuming the velocity v of SC is constant, we get:

[0054]

[0055] Ultimately, we have:

[0056]

[0057] In the formula, T is the total lane-changing time for vehicles; θ i X represents the vehicle's heading angle at the current moment; X represents the longitudinal displacement of the vehicle's lane-changing trajectory; Y represents the lateral displacement of the vehicle's lane-changing trajectory.

[0058] S4.3 Optimal trajectory planning, defining the lane-changing cost function J as follows:

[0059]

[0060] In the formula, λ represents the driver's driving style coefficient; This is the tangential acceleration at the end of the lane change, when the curvature of the lane change trajectory is at its maximum.

[0061]

[0062] In the formula, J is the minimum value of the cost function; T is the time required for the optimal lane-changing trajectory; This represents the value of t taken as T after taking the second derivative with respect to y(t); This represents the value of t taken as T after taking the first derivative with respect to y(t); This represents the value of t taken as T after taking the second derivative with respect to x(t); This represents the value of t taken as T after taking the first derivative with respect to x(t);

[0063] Therefore, the longitudinal displacement X of the lane change is:

[0064] X=f(v,T,θ i ,Y)

[0065] In the formula, f(·) represents the solution process for longitudinal displacement X when the lane change cost function is minimized; Y represents y(T).

[0066] Beneficial effects:

[0067] This invention considers the impact of real-time changes in the position and posture information of surrounding vehicles on the lane-changing process of intelligent connected vehicles. It designs a dynamic, repeated master-slave game-based lane-changing decision-making and trajectory planning method. During the lane-changing process, the lateral space is discretized, and repeated game-playing is performed at a fixed horizontal position to update the decision and planning, which can effectively improve the safety and efficiency of lane changing.

[0068] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0069] Figure 1 This is a flowchart of a method for lane-changing decision-making and trajectory planning for intelligent connected vehicles according to the present invention;

[0070] Figure 2 This is a schematic diagram of lane-changing based on dynamic repeated master-slave game. Detailed Implementation

[0071] To make the technical solutions, advantages, and objectives of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the protection scope of this application.

[0072] like Figure 1 and Figure 2 As shown, this invention proposes a lane-changing decision-making and trajectory planning method based on dynamic repeated master-slave game. By using sensors such as lidar, millimeter-wave radar, and on-board cameras mounted on the intelligent connected vehicle (SC) to perceive the road traffic environment, the method considers the position and speed information of the vehicle in front (FC) in the current lane, the vehicle in front (PC) in the target lane, and the vehicle behind (LC), and solves the optimal strategy through dynamic game. Based on different strategies and the driver's comfort and lane-changing efficiency, the optimal lane-changing trajectory of the vehicle is planned.

[0073] This embodiment provides a method for lane-changing decision-making and trajectory planning for intelligent connected vehicles, including the following steps:

[0074] S1. Define the payoff function for the master-slave game between lane-changing intelligent connected vehicles (SC) and right-of-way competing vehicles (LC);

[0075] Vehicles competing for right-of-way refer to vehicles in the target lane that compete for position with intelligent connected vehicles changing lanes, i.e., vehicles following in the target lane;

[0076] S1.1 defines a vehicle;

[0077] The vehicle that changes lanes is defined as SC, the vehicle in front of the vehicle changing lanes is FC, the vehicle behind the target lane is LC, and the vehicle in front of the target lane is PC.

[0078] In this game, the lane-changing intelligent connected vehicle (SC) and the right-of-way competing vehicle (LC) are the main and follower participants, with SC being the leader and LC being the follower.

[0079] S1.2 Calculate the game payoff for SC. The payoff function is:

[0080] E SC =(1-μ SC )*E safe1 +μ SC E vel

[0081] In the formula, E SC For the total revenue of lane-changing vehicles SC; μ SC This is the weighting coefficient for the vehicle's SC driving style; the value represents the driving type, μ. SC The larger the value, the more aggressive the driving behavior; E safe1 For the safety benefits of lane-changing vehicles SC; E vel For the speed gain of lane-changing vehicles (SC);

[0082] E safe1 The calculation formula is as follows:

[0083]

[0084]

[0085] In the formula, L is the vehicle width; d is the lateral distance between vehicle SC and vehicle LC; a represents the lateral safety distance between the two vehicles; e1 is the safety factor of vehicle SC; T h T is the headway of vehicle SC relative to vehicle LC. e1 The expected headway of vehicle SC is represented by T. e1=min(3,T1) is calculated; T1 represents the initial headway between vehicle FC and vehicle SC; W represents the lateral distance between the center lines of the two lanes; T2 represents the initial headway between vehicle PC and vehicle LC;

[0086] E vel The calculation formula is as follows:

[0087]

[0088] In the formula, v PC v is the speed of the vehicle PC ahead in the target lane. FC v is the speed of the vehicle FC. desire The desired speed of SC;

[0089] S1.3 Calculate the game payoff for vehicle LC competing for right-of-way, the payoff function is:

[0090] E LC =(1-μ LC )*E safe2 +μ LC *E space

[0091] In the formula, E LC The total revenue of vehicle LC; μ LC The weighting coefficient for LC driver driving style; the value represents the driver type, μ. LC The larger the value, the more aggressive the driver's driving behavior; E safe2 For the safety benefits of vehicle LC; E space For the space benefits of vehicle LC;

[0092] E safe2 and E space The calculation formula is as follows:

[0093]

[0094]

[0095]

[0096] In the formula, d is the lateral distance between vehicle SC and vehicle LC; L is the vehicle width; a represents the safe lateral distance between the two vehicles; T h e2 is the headway of vehicle SC relative to vehicle LC; e2 is the safety factor of vehicle LC; T e2 The expected headway of car number 2 can be determined by T. e2 =min(3,T2) yields the result.

[0097] S2. Establish a Cartesian coordinate system with the current location of the lane-changing vehicle as the origin, the horizontal driving direction as the X-axis, and the direction from the lane where the lane-changing vehicle is located to the target lane and perpendicular to the X-axis as the Y-axis.

[0098] Based on the lateral distance between the center line of the current lane of the intelligent connected vehicle and the center line of the target lane, five horizontal lines are set at equal intervals along the direction of vehicle travel. The vertical coordinate yi of each horizontal line is obtained, where i represents the sequence number of each horizontal line, with a value range of 1-5. Each vertical coordinate yi serves as a node for the intelligent connected vehicle to perform a repeated game.

[0099] S3. Use the sensing devices of intelligent connected vehicles to collect driving status data and road environment information of themselves and competing vehicles changing lanes, and solve the game equilibrium to obtain the decision result;

[0100] If the decision result is to continue changing lanes, proceed to step S4;

[0101] If the decision result is lane keeping, proceed to step S5;

[0102] If the decision result is to revert to the previous horizontal line, then proceed to step S6;

[0103] The process of finding the game equilibrium and obtaining the decision result is as follows:

[0104] Based on the payoff functions designed in steps S1.2 and S1.3, determine the payoff matrix {E} of lane-changing vehicle SC and competing vehicle LC in the target lane under different game strategies. payoff (a x ,b y )};

[0105] In the formula, E payoff This represents the total return of SC or LC under the current strategy combination; x = {1, 2, 3}, y = {1, 2}, a1 indicates that SC continues to change lanes, a2 indicates that SC moves along the current horizontal line y. i Driving, a3 indicates SC reverts to the previous horizontal line y. i-1 b1 represents LC acceleration, b2 represents LC deceleration, (a x ,b y () represents a combination of game strategies;

[0106] II. Solve using the payoff matrix of a master-slave game to find the equilibrium strategy, as shown in the following equation:

[0107]

[0108] In the formula, a* and b* represent the corresponding strategies that achieve the maximum payoff under the game equilibrium of SC and LC.

[0109] The payoff matrix is ​​shown in Table 1 below:

[0110] Table 1 Game Payoff Matrix

[0111]

[0112] III. Add minimum headway to lane change safety constraints;

[0113] The minimum safe headway is set to 2 seconds, and the following formula applies:

[0114]

[0115] In the formula, T safe For safe headway.

[0116] S4. Based on the continuous lane-changing strategy obtained in step S3, plan the lane-changing trajectory;

[0117] S4.1 Fit the lane-changing trajectory based on the cubic polynomial parametric equation;

[0118]

[0119] In the formula, x(t) and y(t) are the longitudinal and lateral positions of the vehicle at time t, respectively; a0, a1, a2, a3, b0, b1, b2, b3 are the coefficients of the polynomial curve;

[0120] S4.2 At each time step, update the coordinate system with the current vehicle position as the origin, and assuming the velocity v of SC is constant, we get:

[0121]

[0122] Ultimately, we have:

[0123]

[0124] In the formula, T is the total lane-changing time for vehicles; θ i X represents the vehicle's heading angle at the current moment; X represents the longitudinal displacement of the vehicle's lane-changing trajectory; Y represents the lateral displacement of the vehicle's lane-changing trajectory.

[0125] S4.3 Optimal trajectory planning needs to consider both the comfort and efficiency of lane changes, so the lane change cost function J is defined as follows:

[0126]

[0127] In the formula, λ represents the driver's driving style coefficient, which is taken as 0.5 here; This is the tangential acceleration at the end of the lane change, when the curvature of the lane change trajectory is at its maximum.

[0128]

[0129] In the formula, J is the minimum value of the cost function; T is the time required for the optimal lane-changing trajectory; This represents the value of t taken as T after taking the second derivative with respect to y(t); This represents the value of t taken as T after taking the first derivative with respect to y(t); This represents the value of t taken as T after taking the second derivative with respect to x(t); This represents the value of t taken as T after taking the first derivative with respect to x(t);

[0130] Therefore, the longitudinal displacement X of the lane change is:

[0131] X=f(v,T,θ i ,Y)

[0132] In the formula, f(·) represents the solution process for longitudinal displacement X when the lane change cost function is minimized; Y represents y(T).

[0133] S5. Based on the lane keeping strategy obtained in step S3, perform lane keeping trajectory planning;

[0134] When the optimal strategy is determined to be lane keeping, a lane keeping trajectory is generated, and the lane-changing vehicle SC maintains its current lateral position and travels in a straight line.

[0135] S6. Based on the rollback strategy obtained in step S3, plan the vehicle's driving trajectory to roll back to the position of the previous horizontal line;

[0136] When the optimal strategy is determined to be backtracking, the lane-changing vehicle SC backtracks to its previous lateral position and generates the following cubic polynomial lane-changing trajectory:

[0137]

[0138] In the formula, Y represents the lateral displacement required for vehicle 1 to change lanes when it was in the previous lateral position. Based on the cost function defined above, the optimal longitudinal distance X in the reverse case can be obtained as follows:

[0139] X=f(v,T,θ i ,Y)

[0140] In the formula, f(·) represents the solution process for longitudinal displacement X when the lane change cost function is minimized; Y represents y(T).

[0141] S7. Repeat steps S3 to S6 until the intelligent connected vehicle completes the lane change.

[0142] It is hereby declared that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for lane-changing decision-making and trajectory planning for intelligent connected vehicles, characterized in that, Includes the following steps: S1. Define the payoff function for the master-slave game between lane-changing intelligent connected vehicles (SC) and right-of-way competing vehicles (LC); Vehicles competing for right-of-way refer to vehicles in the target lane that compete for position with intelligent connected vehicles changing lanes, i.e., vehicles following in the target lane; S1.1 defines a vehicle; The vehicle that changes lanes is defined as SC, the vehicle in front of the vehicle changing lanes is FC, the vehicle behind the target lane is LC, and the vehicle in front of the target lane is PC. In this game, the lane-changing intelligent connected vehicle (SC) and the right-of-way competing vehicle (LC) are the main and follower participants, with SC being the leader and LC being the follower. S1.2 Calculate the game payoff for SC. The payoff function is: And SC =(1-μ SC )*E safe1 +μ SC And vel In the formula, E SC For the total revenue of lane-changing vehicles SC; μ SC This is the weighting coefficient for the vehicle's SC driving style; the value represents the driving type, μ. SC The larger the value, the more aggressive the driving behavior; E safe1 For the safety benefits of lane-changing vehicles SC; E vel For the speed gain of lane-changing vehicles (SC); E safe1 The calculation formula is as follows: In the formula, L is the vehicle width; d is the lateral distance between vehicle SC and vehicle LC; a represents the lateral safety distance between the two vehicles; e1 is the safety factor of vehicle SC; T h T is the headway of vehicle SC relative to vehicle LC. e1 T1 represents the expected headway of vehicle SC; T2 represents the initial headway between vehicle FC and vehicle SC; W represents the lateral distance between the centerlines of the two lanes; T3 represents the initial headway between vehicle PC and vehicle LC. E vel The calculation formula is as follows: In the formula, v PC v is the speed of the vehicle PC ahead in the target lane. FC v is the speed of the vehicle FC. desire The desired speed of SC; S1.3 Calculate the game payoff for vehicle LC competing for right-of-way, with the payoff function as follows: And LC =(1-μ LC )*E safe2 +μ LC *E space In the formula, E LC The total revenue of vehicle LC; μ LC This is a weighting coefficient for the driving style of LC drivers; the value represents the driver type, μ. LC The larger the value, the more aggressive the driver's driving behavior; E safe2 For the safety benefits of vehicle LC; E space For the space benefits of vehicle LC; E safe2 and E space The calculation formula is as follows: In the formula, d is the lateral distance between vehicle SC and vehicle LC; L is the vehicle width; a represents the safe lateral distance between the two vehicles; T h e2 is the headway of vehicle SC relative to vehicle LC; e2 is the safety factor of vehicle LC; T e2 The expected headway for car number 2; S2. Establish a Cartesian coordinate system with the current location of the lane-changing vehicle as the origin, the horizontal driving direction as the X-axis, and the direction from the lane where the lane-changing vehicle is located to the target lane and perpendicular to the X-axis as the Y-axis. S3. Use the sensing devices of intelligent connected vehicles to collect driving status data and road environment information of themselves and competing vehicles changing lanes, and solve the game equilibrium to obtain the decision result; If the decision result is to continue changing lanes, proceed to step S4; If the decision result is lane keeping, proceed to step S5; If the decision result is to revert to the previous horizontal line, then proceed to step S6; S4. Based on the continuous lane-changing strategy obtained in step S3, plan the lane-changing trajectory; S5. Based on the lane keeping strategy obtained in step S3, perform lane keeping trajectory planning; S6. Based on the rollback strategy obtained in step S3, plan the vehicle's driving trajectory to roll back to the position of the previous horizontal line; S7. Repeat steps S3 to S6 until the intelligent connected vehicle completes the lane change.

2. The intelligent connected vehicle lane-changing decision-making and trajectory planning method according to claim 1, characterized in that: The specific content of step S2 is as follows: taking the lateral distance between the center line of the current lane of the intelligent connected vehicle and the center line of the target lane as the benchmark, five horizontal lines along the direction of vehicle travel are set at equal intervals to obtain the vertical coordinate yi of each horizontal line, where i represents the sequence number of each horizontal line, and the value range is 1-5. Each vertical coordinate yi is used as a node for the intelligent connected vehicle to perform a repeated game.

3. The intelligent connected vehicle lane-changing decision-making and trajectory planning method according to claim 2, characterized in that: In step S3, the process of solving the game equilibrium to obtain the decision result is as follows: Based on the payoff functions designed in steps S1.2 and S1.3, determine the payoff matrix {E} of lane-changing vehicle SC and competing vehicle LC in the target lane under different game strategies. payoff (a x ,b y )}; In the formula, E payoff This represents the total return of SC or LC under the current strategy combination; x = {1, 2, 3}, y = {1, 2}, a1 indicates that SC continues to change lanes, a2 indicates that SC moves along the current horizontal line y. i Driving, a3 indicates SC reverts to the previous horizontal line y. i-1 b1 represents LC acceleration, b2 represents LC deceleration, (a x ,b y () represents a combination of game strategies; II. Solve using the payoff matrix of a master-slave game to find the equilibrium strategy, as shown in the following equation: In the formula, a* and b* represent the corresponding strategies that achieve the maximum payoff under the game equilibrium of SC and LC. III. Add minimum headway to lane change safety constraints; The minimum safe headway is set to 2 seconds, and the following formula applies: In the formula, T safe For safe headway.

4. The intelligent connected vehicle lane-changing decision-making and trajectory planning method according to claim 3, characterized in that: Step S4 includes the following sub-steps: S4.1 Fit the lane-changing trajectory based on the cubic polynomial parametric equation; In the formula, x(t) and y(t) are the longitudinal and lateral positions of the vehicle at time t, respectively; a0, a1, a2, a3, b0, b1, b2, b3 are the coefficients of the polynomial curve; S4.2 At each time step, update the coordinate system with the current vehicle position as the origin, and assuming the velocity v of SC is constant, we get: Ultimately, we have: In the formula, T is the total lane-changing time for vehicles; θ i X represents the vehicle's heading angle at the current moment; X represents the longitudinal displacement of the vehicle's lane-changing trajectory; Y represents the lateral displacement of the vehicle's lane-changing trajectory. S4.3 Optimal trajectory planning, defining the lane-changing cost function J as follows: In the formula, λ represents the driver's driving style coefficient; This is the tangential acceleration at the end of the lane change, when the curvature of the lane change trajectory is at its maximum. In the formula, J is the minimum value of the cost function; T is the time required for the optimal lane-changing trajectory; This represents the value of t taken as T after taking the second derivative with respect to y(t); This represents the value of t taken as T after taking the first derivative with respect to y(t); This represents the value of t taken as T after taking the second derivative with respect to x(t); This represents the value of t taken as T after taking the first derivative with respect to x(t); Therefore, the longitudinal displacement X of the lane change is: X=f(v,T,θ i ,Y) In the formula, f(·) represents the solution process for longitudinal displacement X when the lane change cost function is minimized; Y represents y(T).

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