Interaction-aware motion planning and control based on game theory with human-in-the-loop validation

Abstract

An interaction-aware control and motion planning framework is proposed and experimentally verified for time-critical merging scenarios. The framework considers interaction between the automated driving system and other vehicles, including human-driven vehicles, by monitoring lateral and longitudinal response of the neighbor vehicle without communication or having access to their control and safety objective functions. This has not been accounted for safe motion planning and controls in existing merging solutions in mixed traffic. The framework includes a novel inverse differential game based on a long short-term memory network for estimation of the possible path tracking objective function of the human-driven vehicle in real-time. Then, a game-theoretic receding horizon controller is devised for the automated driving system by predicting the trajectory of the human-driven vehicle. The developed framework is validated in several merging scenarios and road surface conditions using CarSim high-fidelity simulations including human-in-the-loop case studies with different test subjects.