TY - GEN
T1 - Driver-automation collaborative steering control for intelligent vehicles under unexpected emergency conditions
AU - Yang, Lu
AU - Liu, Ke
AU - Huang, Heye
AU - Liu, Qiaobin
AU - Gao, Ming
AU - Wang, Jianqiang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Most of fatal traffic accidents occur in unexpected emergency conditions, such as, post-impact, tire blowout etc, in which vehicle attitudes are immediately changed due to external disturbances and internal perturbations. It is an extremely challenging task for human driver to effectively and timely stop or control such a vehicle, especially for the inexperienced driver. Towards this end, this paper proposes a driver-automation collaborative control scheme for vehicles subjected to unexpected emergency conditions by assisting human driver's steering manipulation. To begin with, a model predictive lateral controller is constructed to enhance dynamical stability and collision avoidance capability considering model uncertainty and external disturbances. After that, a collaborative steering control authority allocator is designed for adaptively allocating control weighting of respective steering angles, in which the parameterized human driver activation is formulated considering driving action and state. As well, an optimal preview acceleration driver model combined with neuromuscular dynamics is developed for imitating human driver's steering manipulation while harmonizing with the controller. Lastly, simulation examples with different experienced human drivers validated the effectiveness and superiority of proposed control scheme and approaches in lateral stability enhancement and collision avoidance capability of intelligent vehicles subjected to unexpected emergency conditions.
AB - Most of fatal traffic accidents occur in unexpected emergency conditions, such as, post-impact, tire blowout etc, in which vehicle attitudes are immediately changed due to external disturbances and internal perturbations. It is an extremely challenging task for human driver to effectively and timely stop or control such a vehicle, especially for the inexperienced driver. Towards this end, this paper proposes a driver-automation collaborative control scheme for vehicles subjected to unexpected emergency conditions by assisting human driver's steering manipulation. To begin with, a model predictive lateral controller is constructed to enhance dynamical stability and collision avoidance capability considering model uncertainty and external disturbances. After that, a collaborative steering control authority allocator is designed for adaptively allocating control weighting of respective steering angles, in which the parameterized human driver activation is formulated considering driving action and state. As well, an optimal preview acceleration driver model combined with neuromuscular dynamics is developed for imitating human driver's steering manipulation while harmonizing with the controller. Lastly, simulation examples with different experienced human drivers validated the effectiveness and superiority of proposed control scheme and approaches in lateral stability enhancement and collision avoidance capability of intelligent vehicles subjected to unexpected emergency conditions.
KW - Collaborative steering control
KW - Control authority allocation
KW - Unexpected emergency conditions
UR - http://www.scopus.com/inward/record.url?scp=85135376409&partnerID=8YFLogxK
U2 - 10.1109/IV51971.2022.9827330
DO - 10.1109/IV51971.2022.9827330
M3 - Conference contribution
AN - SCOPUS:85135376409
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 1623
EP - 1630
BT - 2022 IEEE Intelligent Vehicles Symposium, IV 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Intelligent Vehicles Symposium, IV 2022
Y2 - 5 June 2022 through 9 June 2022
ER -
