@article{ref1,
title="Multiobjective optimization of lane-changing strategy for intelligent vehicles in complex driving environments",
journal="IEEE transactions on vehicular technology",
year="2020",
author="Zhou, Jian and Zheng, Hongyu and Wang, Junmin and Wang, Yulei and Zhang, Bing and Shao, Qian",
volume="69",
number="2",
pages="1291-1308",
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.<p /> <p>Language: en</p>",
language="en",
issn="0018-9545",
doi="10.1109/TVT.2019.2956504",
url="http://dx.doi.org/10.1109/TVT.2019.2956504"
}