Citation

Zhou J, Zheng H, Wang J, Wang Y, Zhang B, Shao Q. IEEE Trans. Vehicular Tech. 2020; 69(2): 1291-1308.

Copyright

(Copyright © 2020, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TVT.2019.2956504

PMID

unavailable

Abstract

This paper describes an optimal lane-changing strategy for intelligent vehicles under the constraints of collision avoidance in complex driving environments. The key technique is optimization in a collision-free lane-changing trajectory cluster. To achieve this cluster, a tuning factor is first derived by optimizing a cubic polynomial. Then, a feasible trajectory cluster is generated by adjusting the tuning factor in a stable handling envelope defined from vehicle dynamics limits. Furthermore, considering the motions of surrounding vehicles, a collision avoidance algorithm is employed in the feasible cluster to select the collision-free trajectory cluster. To extract the optimal trajectory from this cluster, the TOPSIS algorithm is utilized to solve a multiobjective optimization problem that is subject to lane change performance indices, i.e., trajectory following, comfort, lateral slip and lane-changing efficiency. In this way, the collision risk is eliminated, and the lane change performance is improved. Simulation results show that the strategy is able to plan suitable lane-changing trajectories while avoiding collisions in complex environments.

Language: en

Keywords

Clustering algorithms; collision avoidance; Collision avoidance; Heuristic algorithms; Intelligent vehicle; lane-changing strategy optimization; Optimization; path planning; TOPSIS algorithm; Trajectory; Tuning; Vehicle dynamics