🤖 AI Summary
This work addresses the safety and efficiency challenges faced by autonomous vehicles in mixed traffic, where the unpredictable behavior of human drivers complicates interaction. To tackle this issue, the paper formulates driving interactions as a generalized Nash equilibrium problem—a framework introduced here for the first time in a real-time autonomous driving system. By jointly optimizing vehicle strategies while explicitly coupling safety and geometric constraints, the approach enables dynamic coordination between autonomous policies and human behaviors. To solve the resulting non-convex equilibrium problem efficiently, the authors develop a dedicated particle swarm optimization (PSO)-based solver capable of millisecond-level decision-making. Real-world vehicle tests demonstrate that the system converges within 50 milliseconds, generating smooth, human-like trajectories that effectively handle critical interactive scenarios.
📝 Abstract
Safe and efficient navigation in mixed-traffic environments remains a critical challenge for Autonomous Vehicles (AVs), primarily due to the complex interdependence between the AV's decisions and the unpredictable reactions of human drivers. This paper introduces a comprehensive decision-making framework that formulates the driving interaction as a Generalized Nash Equilibrium Problem (GNEP). Unlike decoupled optimization approaches, this framework explicitly models shared safety and geometric constraints, ensuring that the feasibility of the AV's strategy is dynamically linked to the opponent's actions. To solve this non-convex problem in real-time, we propose a dedicated solver based on Particle Swarm Optimization (PSO). The complete architecture was validated on a test track using a real autonomous Renault Zoé interacting with a human driver. Experimental results demonstrate the system's ability to handle critical scenarios by generating comfortable, human-like trajectories. Benchmarks confirm the solver's operational feasibility, achieving convergence in under 50 ms.