2D-Action Asynchronous Cooperative Lane Change Trajectory Planning Method for Connected and Automated Vehicles
The ability to change lanes safely, efficiently, and comfortably is an important prerequisite for the application of Connected-Automated Vehicles (CAVs). Based on the five-order polynomial trajectory planning for CAVs, the 2D-Action Asynchronous Lane Change (AALC) trajectory planning model is constr...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/5540444 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832544161188806656 |
|---|---|
| author | Liyang Wei Weihua Zhang Haijian Bai Jingyu Li |
| author_facet | Liyang Wei Weihua Zhang Haijian Bai Jingyu Li |
| author_sort | Liyang Wei |
| collection | DOAJ |
| description | The ability to change lanes safely, efficiently, and comfortably is an important prerequisite for the application of Connected-Automated Vehicles (CAVs). Based on the five-order polynomial trajectory planning for CAVs, the 2D-Action Asynchronous Lane Change (AALC) trajectory planning model is constructed by further considering the longitudinal and lateral driving action execution time parameters. This is done to improve the applicability of the lane change model and increase the CAV lane change success rate. The continuous collision space algorithm is constructed by determining the continuity condition of collision trajectory parameter solution space through the monotonicity of trajectory curve parameters and collision form classification. AALC trajectory safety judgment is realized through this algorithm. A cooperative lane change trajectory evaluation objective function is constructed, considering multivehicle comfort and efficiency. Finally, the AALC model is solved in the continuous collision space according to the optimal objective function, and the lane change is divided into free, cooperative, and refused according to the optimization. The results indicate that the AALC model achieves the transfer of collision space between lanes through asynchronous process of behavior execution time window, thereby reducing the possibility of vehicle collision. The AALC model reduces the degree of change of cooperative lane change parameters by asynchronous process of behavior, increasing the number of free lane change trajectories by about 17%, effectively reducing the occurrence of lane change refusal, improving the successful rate of lane change, and enhancing the overall evaluation of the lane change. The AALC model realizes the reallocation of collision space between different lanes through asynchronous process, making it more suitable for environments with large differences in vehicle gaps such as ramp merging. The collision-based trajectory optimization algorithm can quickly obtain the corresponding safety space and optimal trajectory. The maximum calculation time for a single cooperative lane change is 0.073 s, thus enabling real-time trajectory planning. |
| format | Article |
| id | doaj-art-d9267ed1b3f3425699f05a59e5a0c7e1 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-d9267ed1b3f3425699f05a59e5a0c7e12025-02-03T10:58:00ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/55404442D-Action Asynchronous Cooperative Lane Change Trajectory Planning Method for Connected and Automated VehiclesLiyang Wei0Weihua Zhang1Haijian Bai2Jingyu Li3College of Civil EngineeringSchool of Automobile and Transportation EngineeringSchool of Automobile and Transportation EngineeringCollege of Civil EngineeringThe ability to change lanes safely, efficiently, and comfortably is an important prerequisite for the application of Connected-Automated Vehicles (CAVs). Based on the five-order polynomial trajectory planning for CAVs, the 2D-Action Asynchronous Lane Change (AALC) trajectory planning model is constructed by further considering the longitudinal and lateral driving action execution time parameters. This is done to improve the applicability of the lane change model and increase the CAV lane change success rate. The continuous collision space algorithm is constructed by determining the continuity condition of collision trajectory parameter solution space through the monotonicity of trajectory curve parameters and collision form classification. AALC trajectory safety judgment is realized through this algorithm. A cooperative lane change trajectory evaluation objective function is constructed, considering multivehicle comfort and efficiency. Finally, the AALC model is solved in the continuous collision space according to the optimal objective function, and the lane change is divided into free, cooperative, and refused according to the optimization. The results indicate that the AALC model achieves the transfer of collision space between lanes through asynchronous process of behavior execution time window, thereby reducing the possibility of vehicle collision. The AALC model reduces the degree of change of cooperative lane change parameters by asynchronous process of behavior, increasing the number of free lane change trajectories by about 17%, effectively reducing the occurrence of lane change refusal, improving the successful rate of lane change, and enhancing the overall evaluation of the lane change. The AALC model realizes the reallocation of collision space between different lanes through asynchronous process, making it more suitable for environments with large differences in vehicle gaps such as ramp merging. The collision-based trajectory optimization algorithm can quickly obtain the corresponding safety space and optimal trajectory. The maximum calculation time for a single cooperative lane change is 0.073 s, thus enabling real-time trajectory planning.http://dx.doi.org/10.1155/2024/5540444 |
| spellingShingle | Liyang Wei Weihua Zhang Haijian Bai Jingyu Li 2D-Action Asynchronous Cooperative Lane Change Trajectory Planning Method for Connected and Automated Vehicles Journal of Advanced Transportation |
| title | 2D-Action Asynchronous Cooperative Lane Change Trajectory Planning Method for Connected and Automated Vehicles |
| title_full | 2D-Action Asynchronous Cooperative Lane Change Trajectory Planning Method for Connected and Automated Vehicles |
| title_fullStr | 2D-Action Asynchronous Cooperative Lane Change Trajectory Planning Method for Connected and Automated Vehicles |
| title_full_unstemmed | 2D-Action Asynchronous Cooperative Lane Change Trajectory Planning Method for Connected and Automated Vehicles |
| title_short | 2D-Action Asynchronous Cooperative Lane Change Trajectory Planning Method for Connected and Automated Vehicles |
| title_sort | 2d action asynchronous cooperative lane change trajectory planning method for connected and automated vehicles |
| url | http://dx.doi.org/10.1155/2024/5540444 |
| work_keys_str_mv | AT liyangwei 2dactionasynchronouscooperativelanechangetrajectoryplanningmethodforconnectedandautomatedvehicles AT weihuazhang 2dactionasynchronouscooperativelanechangetrajectoryplanningmethodforconnectedandautomatedvehicles AT haijianbai 2dactionasynchronouscooperativelanechangetrajectoryplanningmethodforconnectedandautomatedvehicles AT jingyuli 2dactionasynchronouscooperativelanechangetrajectoryplanningmethodforconnectedandautomatedvehicles |