1
|
Guo J, Xie Z, Liu M, Dai Z, Jiang Y, Guo J, Xie D. Spatio-Temporal Joint Optimization-Based Trajectory Planning Method for Autonomous Vehicles in Complex Urban Environments. SENSORS (BASEL, SWITZERLAND) 2024; 24:4685. [PMID: 39066082 PMCID: PMC11281068 DOI: 10.3390/s24144685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 06/25/2024] [Accepted: 07/01/2024] [Indexed: 07/28/2024]
Abstract
Providing safe, smooth, and efficient trajectories for autonomous vehicles has long been a question of great interest in the field of autopiloting. In dynamic and ever-changing urban environments, safe and efficient trajectory planning is fundamental to achieving autonomous driving. Nevertheless, the complexity of environments with multiple constraints poses challenges for trajectory planning. It is possible that behavior planners may not successfully obtain collision-free trajectories in complex urban environments. Herein, this paper introduces spatio-temporal joint optimization-based trajectory planning (SJOTP) with multi-constraints for complex urban environments. The behavior planner generates initial trajectory clusters based on the current state of the vehicle, and a topology-guided hybrid A* algorithm applied to an inflated map is utilized to address the risk of collisions between the initial trajectories and static obstacles. Taking into consideration obstacles, road surface adhesion coefficients, and vehicle dynamics constraints, multi-constraint multi-objective coordinated trajectory planning is conducted, using both differential-flatness vehicle models and point-mass vehicle models. Taking into consideration longitudinal and lateral coupling in trajectory optimization, a spatio-temporal joint optimization solver is used to obtain the optimal trajectory. The simulation verification was conducted on a multi-agent simulation platform. The results demonstrate that this methodology can obtain optimal trajectories safely and efficiently in complex urban environments.
Collapse
Affiliation(s)
- Jianhua Guo
- State Key Laboratory of Automotive Simulation and Control, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China; (Z.X.); (Z.D.); (Y.J.); (J.G.); (D.X.)
| | - Zhihao Xie
- State Key Laboratory of Automotive Simulation and Control, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China; (Z.X.); (Z.D.); (Y.J.); (J.G.); (D.X.)
| | - Ming Liu
- School of Automotive Studies, Tongji University, Shanghai 201804, China;
| | - Zhiyuan Dai
- State Key Laboratory of Automotive Simulation and Control, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China; (Z.X.); (Z.D.); (Y.J.); (J.G.); (D.X.)
| | - Yu Jiang
- State Key Laboratory of Automotive Simulation and Control, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China; (Z.X.); (Z.D.); (Y.J.); (J.G.); (D.X.)
| | - Jinqiu Guo
- State Key Laboratory of Automotive Simulation and Control, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China; (Z.X.); (Z.D.); (Y.J.); (J.G.); (D.X.)
| | - Dong Xie
- State Key Laboratory of Automotive Simulation and Control, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China; (Z.X.); (Z.D.); (Y.J.); (J.G.); (D.X.)
| |
Collapse
|
2
|
Zanardi A, Zardini G, Srinivasan S, Bolognani S, Censi A, Dorfler F, Frazzoli E. Posetal Games: Efficiency, Existence, and Refinement of Equilibria in Games With Prioritized Metrics. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2021.3135030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|