Human-Like Autopilot: Proactively Acquiring Right-of-Way

Current Autopilot systems struggle in highly dynamic traffic scenarios due to their lack of human-like competitive capability. This research proposes a human-like Autopilot planner with right-of-way priority acquisition capability. It bears the following features: i) human-like Autopilot with proactive right-of-way acquisition capability; ii) with enhanced maneuvering optimality; iii) aggressive but not unsafe; iv) with real-time implementation capability. The proposed approach can identify surrounding vehicles' driving styles in real time and plan motions based on Stackelberg competition. Simulation results show that the proposed approach acquires right-of-way priority proactively. The proposed approach enhances driving efficiency by up to 3.57% across different congestion levels and improves driving safety by up to 13.52% compared to the conventional Autopilot planner. The proposed approach shows remarkable flexibility in dense traffic. Additionally, the proposed approach enables real-time implementation by maintaining less than 100 milliseconds of computation time.