Indirect Shared Control Strategy for Human-Machine Cooperative Driving on Hazardous Curvy Roads

作者：Xiaobin Zhao, Zhishuai Yin, Zhiwei He, Linzhen Nie, Kang Li, Yuanhao Kuang, Chunyuan Lei · 发表于：IEEE Transactions on Intelligent Vehicles · 年份：2023 · DOI：10.1109/tiv.2023.3242762 · 被引用次数：42 · 研究领域：Human-Automation Interaction and Safety、Autonomous Vehicle Technology and Safety、Traffic control and management

Effective and friendly human-machine cooperative driving is considered as a feasible solution to filling the gap between assisted driving and highly automated driving. This paper proposes an indirect shared control strategy, in both longitudinal and lateral directions, for safe cooperative driving on curvy roads, which often pose hazards in real-world driving. At the tactical decision level of the proposed strategy, driving authorities are dynamically allocated between human and machine based on the risk assessed with a data-driven gaussian processes regression (GPR) model, which is trained to generate dynamic safety envelopes of longitudinal and lateral control commands, namely vehicle speed and front wheel steering angle, from observed road conditions and vehicle states. At the maneuver execution level, a multi-objective hierarchical MPC controller is built to combine human-machine control commands smoothly for collision-free driving while simultaneously maintaining vehicle stability and mitigating human-machine conflicts. Both matching the driver's commands and tracking the reference trajectories are formulated into objectives of the optimization-based controller. The weight factor of each objective is adjusted according to the authorities allocated at the decision level. The results of experiments conducted on a 3 degree of freedom motion-base driving simulator show that the proposed strategy accomplishes effective and friendly cooperative driving on curvy roads. It's als...