MDP-based high-level decision-making for combining safety and optimality : autonomous overtaking

Abstract

This paper presents a novel solution for optimal high-level decision-making in autonomous overtaking on two-lane roads, considering both opposite-direction and same-direction traffic. The proposed solution accounts for key factors such as safety and optimality, while also ensuring recursive feasibility and stability. To safely complete overtaking maneuvers, the solution is built on a constrained Markov decision process (MDP) that generates optimal decisions for path planners. By combining MDP with model predictive control (MPC), the approach guarantees recursive feasibility and stability through a baseline control policy that calculates the terminal cost and is incorporated into a constructed Lyapunov function. The proposed solution is validated through five simulated driving scenarios, demonstrating its robustness in handling diverse interactions within dynamic and complex traffic conditions.