Potential Hazard-Aware Adaptive Shared Control for Human-Robot Cooperative Driving in Unstructured Environment

Research on the shared control system for human-in-the-loop cooperative driving has grown steadily in the past decade. However, most proposed methodologies were focused on structural roads such as highway rather than the unstructured environment. This paper presents a novel potential hazard-aware shared steering approach for human-robot cooperative driving in unstructured environment. First, we propose a hierarchical Gaussian risk field (HGRF) to evaluate the potential hazard of the predicted path. Then an adaptive control authority allocation module is developed to engage the control in real-time. The control authority will be fully owned by the human driver when the vehicle drives in a safe manner. However, in the situation where the hazard level is predicted to be high, the authority of the human driver decrease, and the automation actively assists the driver by dynamically sharing the control authority to enhance safety and performance. The proposed methodology is experimentally verified with a steer-by-wire car-like mobile robot. The results show that our proposed approach can effectively engage and cooperatively control the vehicle in hazard cases, ensuring driving safety.