Decision-making for Autonomous Vehicles in Unprotected Left-Turn Scenarios Considering Conflict-Aware Nash Equilibrium Selection

Autonomous vehicles (AVs) face significant decision-making challenges in unprotected left-turn scenarios due to complex game-theoretic interactions with human-driven vehicles. The coexistence of velocity constraints and multiple Nash equilibria (NEs) exacerbates the dilemma between decisiveness and safety. This paper proposes a game-theoretic framework addressing three key aspects: 1) generating velocity-constrained trajectories to simplify closed-loop equilibrium derivation, 2) resolving equilibrium multiplicity through spatiotemporal safety analysis, and 3) developing a conflict-aware selection mechanism for safety-critical decisions. Extensive simulations demonstrate 10.46% higher traffic efficiency and 15.24% safety rate compared to conventional game-theoretic baselines, effectively balancing interaction ambiguity resolution with operational constraints.