TY - GEN
T1 - Interactive-Teb
T2 - 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
AU - Wang, Jiacheng
AU - Fu, Jiawei
AU - Chen, Shitao
AU - Xin, Jingmin
AU - Zheng, Nanning
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Trajectory planning in mixed traffic scenarios is a critical and demanding task in autonomous driving, since it necessitates addressing intricate interactions among traffic agents while conforming to kinodynamic constraints of the vehicle. Most existing methods struggle to simultaneously meet the above-mentioned requirements, resulting in unsafe driving behavior. To take this issue, we propose Interactive-TEB algorithm, which utilizes a time-optimal optimization framework to generate a safe and comfortable motion trajectory, considering kinodynamic constraints of the vehicle and obstacle avoidance. Moreover, Interactive-TEB incorporates interactive predicted trajectories of other traffic agents into trajectory optimization to consider the inevitable interactivity in mixed traffic scenarios. Extensive experimental evaluations across diverse mixed traffic scenarios demonstrate that the proposed algorithm outperforms existing methods in terms of safety, comfort, and effectiveness.
AB - Trajectory planning in mixed traffic scenarios is a critical and demanding task in autonomous driving, since it necessitates addressing intricate interactions among traffic agents while conforming to kinodynamic constraints of the vehicle. Most existing methods struggle to simultaneously meet the above-mentioned requirements, resulting in unsafe driving behavior. To take this issue, we propose Interactive-TEB algorithm, which utilizes a time-optimal optimization framework to generate a safe and comfortable motion trajectory, considering kinodynamic constraints of the vehicle and obstacle avoidance. Moreover, Interactive-TEB incorporates interactive predicted trajectories of other traffic agents into trajectory optimization to consider the inevitable interactivity in mixed traffic scenarios. Extensive experimental evaluations across diverse mixed traffic scenarios demonstrate that the proposed algorithm outperforms existing methods in terms of safety, comfort, and effectiveness.
UR - https://www.scopus.com/pages/publications/85186498984
U2 - 10.1109/ITSC57777.2023.10422169
DO - 10.1109/ITSC57777.2023.10422169
M3 - 会议稿件
AN - SCOPUS:85186498984
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 3977
EP - 3984
BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 September 2023 through 28 September 2023
ER -