Method for Reducing Computational Complexity of Intelligent Vehicle Intersection Passage Sorting and Trajectory Planning

Abstract

The present invention relates to a method for reducing the computational complexity of intelligent vehicle intersection passing sequence and trajectory planning, including the following steps: S1. Determine whether there are other intelligent connected vehicle fleets driving out of the intersection conflict area; S2. Regard v i as an intelligent connected vehicle fleet with a team leader of 1, and move v i into the conflict vehicle fleet set P cf to form an ungrouped conflict vehicle fleet set P cf_up ; S3. Determine whether it can merge into the vehicle fleet in front of its path within the intersection control area; S4. Determine the grouping strategy of the intelligent connected vehicle v i according to the numerical comparison result of the average delay d up of ungrouped vehicles and the average delay d p of grouped vehicles; S5. Realize the dynamic update of the passing sequence and trajectory planning of intelligent connected vehicles at the intersection. The present invention takes the grouping strategy of intelligent connected vehicles as a decision variable, making the grouping behavior of intelligent connected vehicles more controllable, reducing the problem scale, and not requiring independent trajectory planning. Only the initial trajectory needs to be corrected according to the trajectory planning result of the vehicle fleet in front, reducing the computational burden.

Description

Technical Field

[0001] The present invention relates to the technical field of trajectory planning and passing sequence solving for intelligent connected vehicles, and in particular to a method for reducing the computational complexity of passing sequence sorting and trajectory planning for intelligent vehicles at intersections. Background Art

[0002] With the further development of communication technology and control technology, intelligent connected vehicles will gradually become popular, and they are regarded as emerging solutions to solve traffic congestion and traffic safety. In the context of V2X, the passing sequence and driving trajectory of intelligent connected vehicles will be centrally scheduled by the central controller inside the intersection. However, as the number of intelligent connected vehicles inside the intersection increases, the computational burden on the central controller increases rapidly. Therefore, an efficient method for solving the passing sequence and trajectory planning of intelligent connected vehicles is needed.

[0003] In the prior art, the control objects of passing sequence and trajectory planning are both individual intelligent connected vehicles. When the number of intelligent connected vehicles inside the intersection reaches a certain threshold, a reasonable passing sequence solving method will find a solution for intelligent connected vehicles to pass through the intersection in a team, and supplemented with a single vehicle trajectory planning method, intelligent connected vehicles can pass through the intersection in a team. However, the existing passing sequence solving method and trajectory planning method both control each intelligent connected vehicle separately, and the computational complexity increases rapidly as the number of vehicles increases. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for reducing the computational complexity of passing sequence sorting and trajectory planning for intelligent vehicles at intersections, which solves the passing sequence of intelligent connected vehicles and conducts trajectory planning with the vehicle team as the control object, reducing the computational burden on the central controller.

[0005] The technical solution adopted by the present invention to solve its technical problems is to provide a method for reducing the computational complexity of passing sequence sorting and trajectory planning for intelligent vehicles at intersections, including the following steps:

[0006] S1. When the intelligent connected vehicle v i arrives at the intersection control area, it is judged whether there are other intelligent connected vehicle teams driving out of the intersection conflict area. If so, they are removed from the conflict vehicle team set P cf ;

[0007] S2. Regarding v i as an intelligent connected vehicle team with a team leader of 1, moving v i into the conflict vehicle team set P cf , forming an ungrouped conflict vehicle team set P cf_up , and using a heuristic algorithm to calculate the optimal passing sequence D cf_up among the vehicle teams in the ungrouped conflict vehicle team set P up and the delay d of the ungrouped vehiclesup ;

[0008] S3. Can it merge into the front vehicle platoon on its path within the intersection control area? If so, update the information of the front vehicle platoon and update the conflict vehicle platoon set P cf , form the grouped conflict vehicle platoon set P cf_p , use a heuristic algorithm to calculate the optimal passing order D cf_p among the vehicle platoons in the grouped conflict vehicle platoon set P p and the average delay d of the grouped vehicles p ;

[0009] S4. Determine the platooning strategy of the connected and automated vehicle v up according to the comparison result of the average delay d of ungrouped vehicles p and the average delay d of grouped vehicles i ;

[0010] S5. When each connected and automated vehicle arrives at the intersection control area, execute steps S1 - S4 to achieve dynamic update of the passing order and trajectory planning of connected and automated vehicles at the intersection.

[0011] According to the above solution, step S1 includes the following steps:

[0012] S101. Obtain the moment when the connected and automated vehicle v i arrives at the intersection control area, where the intersection control area is the area enclosed by the warning lines of the four approach roads and the signal transmission boundary;

[0013] S102. Determine whether there is a connected and automated vehicle platoon driving out of the conflict area within the time range when the previous connected and automated vehicle v i-1 and the current connected and automated vehicle v i arrive at the control area, where the conflict area is the area enclosed by the warning lines of the four approach roads;

[0014] t a,i-1 ≤T d ≤t a,i

[0015] In the formula, t a,i-1 represents the moment when the previous connected and automated vehicle v i-1 arrives at the control area, t a,i represents the moment when the current connected and automated vehicle v i arrives at the control area, and T d represents the set of moments when the connected and automated vehicle platoon drives out of the intersection conflict area within the time range of t a,i-1 and t a,i ;

[0016] S103. If the set T d in step S102 is an empty set, it means that t a,i-1and t a,i There is no vehicle fleet leaving the conflict area of the intersection within the time range;

[0017] S104. If the set T in step S102 d is non-empty, then the corresponding vehicle fleet P in T d needs to be removed from the conflict vehicle set P d as follows: cf :

[0018]

[0019] According to the above solution, step S2 includes the following steps:

[0020] S201. Regard the intelligent connected vehicle v i as an intelligent connected vehicle fleet P with a team leader of 1 i , and form an ungrouped conflict vehicle fleet set P cf_up :

[0021] P cf_up = P cf ∪ P i

[0022] S202. Establish a minimum average vehicle delay model, and the specific expression of the objective function is as follows:

[0023]

[0024] In the formula, O m,n represents the passing order of the m-th vehicle fleet P cf_up and the n-th vehicle fleet P m in the ungrouped conflict vehicle fleet set P n . Taking 1 means that P m has the right of way first, and taking 0 means that the m-th vehicle in P n has the right of way first; d up represents the average vehicle delay of the ungrouped conflict vehicle fleet set P cf_up ; represents the total vehicle delay of the vehicle fleet P m ; represents the team leader (including the number of vehicles) of the vehicle fleet P m ; M represents the total number of vehicle fleets in the ungrouped conflict vehicle fleet set P cf_up ;

[0025] S203. Use a heuristic algorithm to solve the minimum average vehicle delay model in step S202, and store the solution result O m,n in the ungrouped optimal passing order D up .

[0026] According to the above solution, step S3 includes the following steps:

[0027] S301. Obtain the path where the intelligent connected vehicle v is located; i

[0028] S302. Determine whether there is a convoy that has not exited the control area on the path where the intelligent connected vehicle v is located. If so, determine whether the intelligent connected vehicle v can merge into the convoy ahead within the control area at a constant speed at the maximum speed; i i

[0029] S303. If it is not possible to merge into the convoy ahead, or there is no convoy ahead, step S3 ends, and the delay d of all the convoy vehicles is taken as infinite; p

[0030] S304. If it is possible to merge into the convoy ahead then update the information of the convoy ahead according to the following formula:

[0031]

[0032]

[0033] In the formula, represents the leader (the number of vehicles included) of the convoy P ahead; f represents the actual length of the convoy P ahead, l f i represents the actual length of the vehicle v of the intelligent connected vehicle, and δ represents the minimum safety distance; i

[0034] S305. Update the convoy information of the convoy P in the original conflict vehicle set P cf to form a convoy conflict set P f ; cf_p

[0035] S306. Establish a minimum average vehicle delay model. The specific expression of the objective function is as follows:

[0036]

[0037] In the formula, O m,n represents the passing order of the m-th convoy P and the n-th convoy P in the convoy conflict set P cf_p . Taking 1 means that P m has the right of way first, and taking 0 means that the m-th vehicle in P n has the right of way first; d m represents the average vehicle delay of the convoy conflict set P n ; p represents the total vehicle delay of the convoy P cf_p ; represents the convoy P m ; ​​​​​​​​Denote the team leader of team P (including the number of vehicles); M represents the set of conflicting teams P of the team formation m ; M represents the total number of teams in the set of conflicting teams P for team formation cf_p ;

[0038] S307. Use a heuristic algorithm to solve the minimum vehicle average delay model in step S306, and store the solution result O m,n into the optimal passing order D for team formation p .

[0039] According to the above scheme, in step S4: If the intelligent connected vehicle v i does not form a team with the front team, then perform trajectory planning for it according to the optimal passing order in step S2; if the intelligent connected vehicle v i forms a team with the front team, then a correction model needs to be introduced to correct the initial trajectory of traveling at a constant maximum speed so that the intelligent connected vehicle v i can smoothly merge into the front team.

[0040] According to the above scheme, step S4 includes the following steps:

[0041] S401. If the average delay d of the unformed vehicle up is less than the average delay d of the formed vehicle p , then the intelligent connected vehicle v i forms a team with the front team, otherwise the intelligent connected vehicle v i does not form a team with the front team;

[0042] S402. Obtain the current driving information such as the moment, speed, acceleration, and acceleration change rate when the intelligent connected vehicle v i reaches the boundary of the control area, and determine its initial conditions x0: t = t 0 , s0 = (u 0 , a 0 , v 0 , x 0 );

[0043] In the formula, in the formula, t 0 represents the initial moment, s0 represents the initial state, u 0 represents the initial acceleration change rate, a 0 represents the initial acceleration, v 0 represents the initial speed, x 0 represents the initial position;

[0044] S403. If the intelligent connected vehicle v i does not form a team with the front team, then calculate the scheduled arrival moment, speed, acceleration, acceleration change rate and other driving information to be executed according to the unformed optimal passing order D up in step S2, and determine its termination condition x f : t = tf ,s f =(u f ,a f ,v f ,x f );

[0045] In the formula, t f represents the termination time, s f represents the termination state, u f represents the termination acceleration change rate, a f represents the termination acceleration, v f represents the termination velocity, x f represents the termination position;

[0046] S404. Establish an optimal control model for the intelligent connected vehicle v i between the initial conditions and the termination conditions. The specific expression is as follows:

[0047] Objective function:

[0048]

[0049] Constraint conditions:

[0050]

[0051] u min ≤u(t)≤u max

[0052] a min ≤a(t)≤a max

[0053] v min ≤v(t)≤v max

[0054] In the formula, the objective function ensures the highest passenger comfort during the vehicle driving process, and the constraint conditions ensure that the system dynamics equation and vehicle performance constraints are satisfied during the vehicle driving process. Among them, u(t) represents the function of the acceleration change rate with respect to time, a(t) represents the function of the acceleration with respect to time, v(t) represents the function of the velocity with respect to time, x(t) represents the function of the displacement with respect to time, u min , u max represent the lower and upper limits of the acceleration change rate respectively, a min , a max represent the lower and upper limits of the acceleration respectively, v min , v max represent the lower and upper limits of the velocity respectively;

[0055] S405. Solve the optimal control model in step S404 to obtain the intelligent connected vehicle v iTrajectory planning result without forming a convoy with the leading convoy;

[0056] S406. If the intelligent connected vehicle v i forms a convoy with the leading convoy, then solve its initial trajectory that always travels at the maximum speed uniformly after the initial time t = t 0 ;

[0057] S407. Extract the first intersection time t i between the initial trajectory of the intelligent connected vehicle v FC_1 obtained in step S406 and the optimal trajectory of the leading convoy. The segmentation time of the trajectory planning of the intelligent connected vehicle v i is calculated according to the following formula:

[0058] t FC = max(t FC_1 , t 0 + t FCmin )

[0059] In the formula, t FC represents the segmentation time of the trajectory planning of the intelligent connected vehicle v i , t 0 represents the initial time of the intelligent connected vehicle v i , and t FCmin represents the shortest time required for the intelligent connected vehicle v i to form a convoy with the leading convoy;

[0060] S408. Calculate the segmentation state of the intelligent connected vehicle v FC according to the state of the leading convoy at the segmentation time t i :

[0061]

[0062] In the formula, s FC represents the segmentation state of the intelligent connected vehicle v i , u FC represents the segmentation acceleration change rate of the leading convoy, a FC represents the segmentation acceleration of the leading convoy, v FC represents the segmentation speed of the leading convoy, x FC represents the position of the leading vehicle of the leading convoy in the segment, represents the actual convoy length of the leading convoy, and δ represents the minimum safety distance;

[0063] S409. Use steps S404 - S405 to calculate the intelligent connected vehicle v i under the initial conditions x0: t = t 0 , s0 = (u 0 , a 0 , v 0 , x0 ) and the segmentation condition x FC : t = t FC , The trajectory between, the intelligent connected vehicle v i At the segmentation moment t FC After that, the speed, acceleration, and acceleration change rate are consistent with the optimal trajectory of the vehicle platoon ahead. The intelligent connected vehicle v i At the segmentation moment t FC After that, the position satisfies the following conditions with the position of the leading vehicle of the vehicle platoon ahead:

[0064]

[0065] In the formula, x i Represents the position of the intelligent connected vehicle v i The position of, Represents the position of the leading vehicle of the vehicle platoon ahead, Represents the actual vehicle platoon length of the vehicle platoon ahead, and δ represents the minimum safety distance.

[0066] Implementing the intelligent vehicle intersection passing sequence and trajectory planning calculation amount reduction method of the present invention has the following beneficial effects:

[0067] By taking the platooning strategy of the intelligent connected vehicle as a decision variable, the present invention enables the platooning behavior of the intelligent connected vehicle to be solved in advance, and then solves the passing sequence and conducts trajectory planning with the vehicle platoon as the control object, reducing the calculation burden of the central controller for solving the passing sequence and trajectory planning of the intelligent connected vehicle. Brief Description of the Drawings

[0068] Figure 1 Is the flowchart of the intelligent vehicle intersection passing sequence and trajectory planning calculation amount reduction method;

[0069] Figure 2 Is the schematic diagram of the distribution position of the intersection control area and the conflict area;

[0070] Figure 3 Is the schematic diagram of the intersection path distribution;

[0071] Figure 4 Is the schematic diagram of the control object for solving the passing sequence without platooning;

[0072] Figure 5 Is the schematic diagram of the control object for solving the passing sequence with platooning. Detailed Description of the Preferred Embodiments

[0073] In order to have a clearer understanding of the technical features, purposes, and effects of the present invention, the specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0074] Such as Figures 1-5As shown in the figure, the method for reducing the computational complexity of the intelligent vehicle intersection passing sequence and trajectory planning of the present invention includes the following steps:

[0075] S1. When the intelligent connected vehicle v i arrives at the intersection control area, it is determined whether there are other intelligent connected vehicle fleets driving out of the intersection conflict area. If so, they are removed from the conflict fleet set P cf ;

[0076] S101. Obtain the moment when the intelligent connected vehicle v i arrives at the intersection control area;

[0077] S102. Determine whether there is an intelligent connected vehicle fleet driving out of the conflict area within the time range when the previous intelligent connected vehicle v i-1 and the current intelligent connected vehicle v i arrive at the control area:

[0078] t a,i-1 ≤T d ≤t a,i

[0079] In the formula, t a,i-1 represents the moment when the previous intelligent connected vehicle v i-1 arrives at the control area, t a,i represents the moment when the current intelligent connected vehicle v i arrives at the control area, and T d represents the set of moments when the intelligent connected vehicle fleet drives out of the intersection conflict area within the time range of t a,i-1 and t a,i ;

[0080] S103. If the set T d in step S102 is an empty set, it means that there is no fleet driving out of the intersection conflict area within the time range of t a,i-1 and t a,i ;

[0081] S104. If the set T d in step S102 is non-empty, the corresponding fleet P d in T d needs to be removed from the conflict vehicle set P cf as follows:

[0082]

[0083] In this embodiment, the moment when the intelligent connected vehicle v 10 arrives at the intersection control area is 20 s, and the moment when the previous intelligent connected vehicle v9 arrives at the control area is 19 s. Within the time range when v9 and v 10 arrive at the control area, no fleet drives out of the intersection conflict area.

[0084] S2. Consider v i as an intelligent connected vehicle fleet with a team leader of 1, and move v i into the conflict vehicle fleet set P cf to form an ungrouped conflict vehicle fleet set P cf_up . Use a heuristic algorithm to calculate the optimal passing order D cf_up among the vehicle fleets in the ungrouped conflict vehicle fleet set P up and the average delay d of the ungrouped vehicles up ;

[0085] S201. Consider the intelligent connected vehicle v 10 as an intelligent connected vehicle fleet P with a team leader of 1 10 to form an ungrouped conflict vehicle fleet set P cf_up :

[0086] P cf_up = P cf ∪P i

[0087] S202. Establish a minimum average vehicle delay model, and the specific expression of the objective function is as follows:

[0088]

[0089] In the formula, O m,n represents the passing order of the m-th vehicle fleet P cf_up in the ungrouped conflict vehicle fleet set P m and the n-th vehicle fleet P n . Taking 1 means that P m has the right of way first, and taking 0 means that the m-th vehicle in P n has the right of way first; d up represents the average vehicle delay of the ungrouped conflict vehicle fleet set P cf_up ; represents the total vehicle delay of the vehicle fleet P m ; represents the team leader (including the number of vehicles) of the vehicle fleet P m ; M represents the total number of vehicle fleets in the ungrouped conflict vehicle fleet set P cf_up ;

[0090] S203. Use a heuristic algorithm to solve the minimum average vehicle delay model in step S202, and store the solution result O m,n into the ungrouped optimal passing order D up ;

[0091] In this embodiment, the original conflict vehicle fleet set P cf has 3 vehicle fleets. After adding v 10 , the formed ungrouped conflict vehicle fleet set P cf_up contains 4 vehicle fleets, and the ungrouped conflict vehicle fleet set Pcf_up The minimum average vehicle delay d up = 14.8 s;

[0092] S3. Obtain the path where the connected and automated vehicle v i is located, and determine whether the connected and automated vehicle v i can merge into the vehicle platoon in front of its path within the intersection control area at a constant speed at the maximum speed. If it can, update the information of the vehicle platoon in front and update the conflict vehicle platoon set P cf to form the grouped conflict vehicle platoon set P cf_p and use a heuristic algorithm to calculate the optimal passing order D cf_p among the vehicle platoons in the grouped conflict vehicle platoon set P p and its grouped average vehicle delay d p ;

[0093] S301. Obtain the path where the connected and automated vehicle v i is located;

[0094] In this embodiment, the path number where v 10 is located is 1;

[0095] S302. Determine whether there is a vehicle platoon that has not exited the control area on the path where the connected and automated vehicle v i is located. If so, determine whether the connected and automated vehicle can merge into the vehicle platoon in front within the control area at a constant speed at the maximum speed;

[0096] S303. If it cannot merge into the vehicle platoon in front, or there is no vehicle platoon in front, then step S3 ends, and the grouped average vehicle delay d i takes infinity; p In this embodiment, there is a vehicle platoon that has not exited the control area on the path where v

[0097] is located, and v 10 can merge into the vehicle platoon in front; 10 ;

[0098] S304. If it can merge into the vehicle platoon in front then update the information of the vehicle platoon in front according to the following formula:

[0099]

[0100]

[0101] In the formula, represents the leader (the number of vehicles included) of the vehicle platoon P f in front; represents the actual length of the vehicle platoon P f in front, l i represents the actual length of the connected and automated vehicle v i and δ represents the minimum safety distance;

[0102] In this embodiment, the actual length of vehicle v 10 is 3.75 m, the minimum safety distance is taken as 1.5 m, and the length of the leading vehicle fleet P f is updated from 1 to 2, and the actual length of the leading vehicle fleet P f is updated from 3.75 m to 9 m;

[0103] S305. Update the fleet information of the original conflict vehicle set P cf to form a conflict set P of grouped vehicle fleets f ; cf_p

[0104] In this embodiment, update the leader and the actual length of the fleet of vehicle v cf in the original conflict vehicle set P 10 ; f

[0105] S306. Establish a minimum average vehicle delay model, and the specific expression of the objective function is as follows:

[0106]

[0107] In the formula, O m,n represents the passing order of the m-th fleet P cf_p and the n-th fleet P m in the conflict set P of grouped vehicle fleets. Taking 1 means that P n has the right of way first, and taking 0 means that the m-th vehicle in P m has the right of way first; d n represents the average vehicle delay of the conflict set P of grouped vehicle fleets p ; cf_p represents the total vehicle delay of fleet P m ; represents the leader of fleet P m (including the number of vehicles); M represents the total number of fleets in the conflict set P of grouped vehicle fleets cf_p ;

[0108] S307. Use a heuristic algorithm to solve the minimum average vehicle delay model in step S306, and store the solution result O m,n into the optimal passing order D of grouped vehicles p ;

[0109] In this embodiment, there are 3 fleets in the conflict fleet set P cf_p , and the minimum average vehicle delay d cf_p of the conflict set P of grouped vehicle fleets is p = 11.3 s;

[0110] S4. According to the average vehicle delay d of ungrouped vehicles​​​up and the average delay d of the platooning vehicle p to determine the platooning strategy of the connected and automated vehicle v i ; if the connected and automated vehicle v i does not platoon with the leading vehicle platoon, then perform trajectory planning for it according to the optimal passing order of step S2; if the connected and automated vehicle v i platoons with the leading vehicle platoon, then a correction model needs to be introduced to correct the initial trajectory of traveling at a constant maximum speed so that the connected and automated vehicle v i can smoothly merge into the leading vehicle platoon;

[0111] S401. If the average delay d of the un-platooned vehicle up is less than the average delay d of the platooning vehicle p , then the connected and automated vehicle v 10 platoons with the leading vehicle platoon, otherwise the connected and automated vehicle v 10 does not platoon with the leading vehicle platoon;

[0112] In this embodiment, the average delay d of the un-platooned vehicle up = 14.8 s > the average delay d of the platooning vehicle p = 11.3 s, and the connected and automated vehicle v 10 platoons with the leading vehicle platoon;

[0113] S402. Obtain the current driving information such as the time, speed, acceleration, and acceleration change rate when the connected and automated vehicle v 10 reaches the boundary of the control area, and determine its initial conditions x0: t = 20, s0 = (0, 0, 14, 0);

[0114] S403. If the connected and automated vehicle v 10 does not platoon with the leading vehicle platoon, then calculate the waiting execution driving information such as the scheduled arrival time, speed, acceleration, and acceleration change rate of its warning line according to the un-platooned optimal passing order D up of step S2, and determine its termination conditions x f : t = t f , s f = (u f , a f , v f , x f );

[0115] In the formula, t f represents the termination time, s f represents the termination state, u f represents the termination acceleration change rate, a f represents the termination acceleration, v f represents the termination speed, and x f represents the termination position;

[0116] S404: Establish the optimal control model of the intelligent connected vehicle v 10 between the initial condition and the termination condition, and the specific expression is as follows:

[0117] Objective function:

[0118]

[0119] Constraint conditions:

[0120]

[0121] u min ≤u(t)≤u max

[0122] a min ≤a(t)≤a max

[0123] v min ≤v(t)≤v max

[0124] In the formula, the objective function ensures the highest passenger comfort during the vehicle driving process, and the constraint conditions ensure that the system dynamics equation and vehicle performance constraints are satisfied during the vehicle driving process. Among them, u(t) represents the function of the acceleration change rate with respect to time, a(t) represents the function of the acceleration with respect to time, v(t) represents the function of the speed with respect to time, x(t) represents the function of the displacement with respect to time, u min and u max respectively represent the lower limit and upper limit of the acceleration change rate, a min and a max respectively represent the lower limit and upper limit of the acceleration, v min and v max respectively represent the lower limit and upper limit of the speed.

[0125] S405. Solve the optimal control model in step S404 to obtain the trajectory planning result of the intelligent connected vehicle v 10 not forming a convoy with the vehicle fleet ahead;

[0126] In this embodiment, if the intelligent connected vehicle v 10 forms a convoy with the vehicle fleet ahead, then skip steps S403 - S405;

[0127] S406. If the intelligent connected vehicle v 10 forms a convoy with the vehicle fleet ahead, then solve its initial trajectory that keeps moving at a constant maximum speed after the initial time t = 20;

[0128] S407. Extract the first intersection time t 10 of the initial trajectory of the intelligent connected vehicle v obtained in step S406 and the optimal trajectory of the vehicle fleet aheadFC_1 = 25, the intelligent connected vehicle v 10 The segmentation time of the trajectory planning is calculated according to the following formula:

[0129] t FC = max(25, 20 + 12) = 32

[0130] S408: Calculate the segmentation state of the intelligent connected vehicle v according to the state of the front vehicle fleet at the segmentation time t FC : i The segmentation state of v:

[0131] s FC = (0, 0, 0, 92 - 3.75 - 1.5) = (0, 0, 0, 86.75)

[0132] S409: Use Steps S404 - S405 to calculate the trajectory of the intelligent connected vehicle v 10 Under the initial conditions x0:t = 20, s0 = (0, 0, 14, 0) and the segmentation conditions x FC :t = 32, s FC = (0, 0, 0, 86.75), the speed, acceleration, and acceleration change rate of the intelligent connected vehicle v 10 after the segmentation time t FC = 32 are consistent with the optimal trajectory of the front vehicle fleet. The position of the intelligent connected vehicle v 10 after the segmentation time t FC satisfies the following conditions with the position of the leading vehicle of the front vehicle fleet:

[0133]

[0134] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present invention, without departing from the spirit and scope protected by the present invention and the claims, can also make many forms, and these all fall within the protection scope of the present invention.

Claims

1. A method for reducing the computational complexity of intelligent vehicle intersection passing sequence and trajectory planning, characterized in that Including the following steps: S1. Intelligent and Connected Vehicle When it arrives at the intersection control area, determine whether there are other intelligent and connected vehicle fleets driving out of the intersection conflict area. If so, remove them from the conflict fleet set ; S2. Regard as an intelligent connected vehicle fleet with a team leader of 1, and move into the conflict fleet set to form an ungrouped conflict fleet set . Then, use a heuristic algorithm to calculate the optimal passing order among the fleets in the ungrouped conflict fleet set and the average delay of ungrouped vehicles ; The step S2 includes the following steps: S201. Consider the intelligent connected vehicle as an intelligent connected vehicle fleet with a team leader of 1 to form a set of ungrouped conflict vehicle fleets : S202. Establish a minimum average vehicle delay model, and the specific expression of the objective function is as follows: In the formula, represents the set of ungrouped conflict vehicle platoons the th vehicle platoon and the th vehicle platoon in terms of the passing order, taking 1 indicates priority passing, and taking 0 indicates the th vehicle has priority to pass; represents the average delay per vehicle of the set of ungrouped conflict vehicle platoons ; represents the total delay of the vehicles in the vehicle platoon ; represents the length of the vehicle platoon ; the total number of vehicle platoons in the set of ungrouped conflict vehicle platoons ; S203. Use a heuristic algorithm to solve the minimum average vehicle delay model in step S202, and store the solution result into the ungrouped optimal passing order ; S3. Can it merge into the front vehicle platoon within the intersection control area? If yes, update the information of the front vehicle platoon and update the conflict vehicle platoon set , and form a platooning conflict vehicle platoon set , and use a heuristic algorithm to calculate the optimal passing order among the vehicle platoons in the platooning conflict vehicle platoon set and their average platooning vehicle delay ; S4. Determine the platooning strategy of the intelligent connected vehicle according to the comparison result of the average delay of unplatooned vehicles and the average delay of platooned vehicles ; ​ S5. When each intelligent connected vehicle arrives at the intersection control area, execute steps S1 - S4 to realize the dynamic update of the passing order and trajectory planning of the intelligent connected vehicles at the intersection.

2. The method for reducing the computational complexity of intelligent vehicle intersection passing sequence and trajectory planning according to claim 1, wherein, The step S1 includes the following steps: S101. Obtain the intelligent connected vehicle The moment of arriving at the intersection control area, where the intersection control area is the area enclosed by the warning lines of the four approach roads and the signal transmission boundary; S102. Determine whether there is an intelligent connected vehicle fleet driving out of the conflict area within the time range when the previous intelligent connected vehicle and the current intelligent connected vehicle reach the control area, where the conflict area is the area enclosed by the warning lines of the four approach roads; Wherein, represents the moment when the previous intelligent connected vehicle arrives at the control area, represents the moment when the current intelligent connected vehicle arrives at the control area, represents and the set of moments when the intelligent connected vehicle fleet exits the intersection conflict area within the time range; S103. If the set in step S102 is an empty set, it means that and there is no vehicle fleet leaving the conflict area of the intersection within the time range; S104. If the set in step S102 is non-empty, then the corresponding vehicle fleet in needs to be removed from the conflict vehicle set according to the following formula: : 。 3. A method for reducing the computational complexity of intelligent vehicle intersection passing sequence and trajectory planning according to claim 1, characterized in that, The step S3 includes the following steps: S301. Obtain the intelligent connected vehicle where it is located; S302. Determine whether there is a vehicle fleet that has not exited the control area on the path where the intelligent connected vehicle is located. If so, determine whether the intelligent connected vehicle can merge into the vehicle fleet ahead within the control area at a constant speed at the maximum speed; ​ S303. If it is impossible to merge into the leading vehicle platoon or there is no leading vehicle platoon ahead, step S3 ends and all the platooning vehicles are delayed. Take infinity; S304. If it is possible to merge into the leading vehicle platoon , update the information of the leading vehicle platoon according to the following formula: In the formula, represents the leader of the front vehicle fleet ; represents the actual length of the front vehicle fleet ; represents the actual length of the intelligent connected vehicle ; represents the minimum safety distance. S305. Update the original conflict vehicle set Middle vehicle convoy of the vehicle convoy information to form a conflict set of grouped vehicle convoys ; S306. Establish a minimum average vehicle delay model, and the specific expression of the objective function is as follows: In the formula, represents the set of platooning conflict vehicle fleets the th vehicle fleet and the th vehicle fleet in terms of the passing order, taking 1 means priority passing, taking 0 means the th vehicle has priority to pass; represents the average delay per vehicle of the set of platooning conflict vehicle fleets ; represents the total delay of the vehicles in the vehicle fleet represents the length of the vehicle fleet ; the total number of vehicle fleets in the set of platooning conflict vehicle fleets ; S307. Use a heuristic algorithm to solve the minimum average vehicle delay model in step S306, and store the solution result into the optimal passing order of the platoon .

4. A method for reducing the computational complexity of intelligent vehicle intersection passing sequence and trajectory planning according to claim 1, characterized in that In the step S4: If the intelligent connected vehicle does not form a team with the vehicle fleet ahead, then trajectory planning is performed for it according to the optimal passing order in step S2; if the intelligent connected vehicle forms a team with the vehicle fleet ahead, then a correction model needs to be introduced to correct the initial trajectory of traveling at a constant speed at the maximum speed, so that the intelligent connected vehicle can smoothly merge into the vehicle fleet ahead.

5. A method for reducing the computational complexity of intelligent vehicle intersection passing sequence and trajectory planning according to claim 4, characterized in that The step S4 includes the following steps: S401. If the average delay of ungrouped vehicles is less than the average delay of grouped vehicles , then the intelligent connected vehicle will form a group with the vehicle fleet ahead; otherwise, the intelligent connected vehicle will not form a group with the vehicle fleet ahead; S402. Obtain the intelligent connected vehicle Obtain the current driving information such as the moment, speed, acceleration, and acceleration change rate when it reaches the boundary of the control area, and determine its initial conditions ; In the formula, in the formula, represents the initial moment, represents the initial state, represents the initial acceleration change rate, represents the initial acceleration, represents the initial velocity, represents the initial position; S403. If the intelligent connected vehicle does not form a team with the vehicle convoy ahead, then according to the optimal non-team driving order in step S2 calculate the scheduled arrival time, speed, acceleration, acceleration change rate and other driving information to be executed of its warning line, and determine its termination condition ; In the formula, represents the termination time, represents the termination state, represents the termination acceleration change rate, represents the termination acceleration, represents the termination velocity, represents the termination position; S404. Establish an intelligent connected vehicle An optimal control model between the initial condition and the termination condition, and the specific expression is as follows: Objective function: Constraint conditions: In the formula, the objective function ensures the highest passenger comfort during vehicle driving, and the constraint conditions ensure that the system dynamics equation and vehicle performance constraints are satisfied during vehicle driving, where represents the function of the acceleration change rate with respect to time, represents the function of the acceleration with respect to time, represents the function of the speed with respect to time, represents the function of the displacement with respect to time, and represent the lower and upper limits of the acceleration change rate respectively, represent the lower and upper limits of the acceleration respectively, represent the lower and upper limits of the speed respectively; S405. Solve the optimal control model in step S404 to obtain the trajectory planning result of the intelligent connected vehicle that does not form a platoon with the vehicle fleet ahead; S406. If the intelligent connected vehicle forms a team with the vehicle fleet ahead, then solve its initial trajectory that will always travel at a constant maximum speed after the initial moment ; S407. Extract the first intersection time of the initial trajectory of the intelligent connected vehicle obtained in step S406 and the optimal trajectory of the vehicle platoon ahead The piecewise time of the trajectory planning of the intelligent connected vehicle is calculated according to the following formula for the intelligent connected vehicle ​ In the formula, represents the segmentation time of the trajectory planning of the intelligent connected vehicle , represents the initial time of the intelligent connected vehicle , represents the shortest time required for the intelligent connected vehicle to form a team with the vehicle fleet ahead. S408. Calculate the segmented state of the intelligent connected vehicle according to the state of the vehicle platoon at the segmentation moment ​​ In the formula, represents the segmentation state of the intelligent connected vehicle ; represents the change rate of the segmented acceleration of the vehicle platoon ahead, represents the segmented acceleration of the vehicle platoon ahead, represents the segmented speed of the vehicle platoon ahead, represents the position of the leading vehicle of the vehicle platoon ahead, represents the actual length of the vehicle platoon ahead, represents the minimum safety distance; S409. Calculate the intelligent connected vehicle using steps S404 - S405 Under the initial conditions and the piecewise conditions of the trajectory between them, the intelligent connected vehicle at the piecewise moment after that, the speed, acceleration, and acceleration change rate are consistent with the optimal trajectory of the leading vehicle in front, and the intelligent connected vehicle at the piecewise moment after that, the position satisfies the following conditions with the position of the leading vehicle in front of the leading vehicle In the formula, represents the position of the intelligent connected vehicle ; represents the position of the leading vehicle of the vehicle platoon ahead; represents the actual length of the vehicle platoon ahead; represents the minimum safety distance.

Images