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 solutionaccounts 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.