Game-Theoretic Reinforcement Learning Framework for Local Multi-Vehicle Interactive Guided Trajectory Generation in Representative Traffic Scenarios

The generation of guided trajectories for autonomous vehicles in multi-vehicle interaction scenarios remains challenging because surrounding vehicles continuously adapt their actions under uncertainty. This paper proposes a game-theoretic reinforcement learning (GT-RL) framework that combines a posterior-weighted rolling-horizon local game with proximal policy optimization (PPO) trajectory refinement. The revision makes the incomplete-information cost explicit through a normalized Bayesian posterior, a posterior expected cost, and a certainty-equivalent numerical approximation used by the SQP best-response solver. Using the supplied run-level logs (500 runs per method and scenario), GT-RL achieved mean safety scores of 94.1% (95% bootstrap CI: 93.91–94.28) in the intersection scenario and 91.5% (91.33–91.66) in the highway-merging scenario, with zero collisions in both sets of 500 runs. Paired comparisons with DQN, PPO, MPC-only, and potential-field baselines were significant after Holm correction (p < 0.001) for safety, traversal time, and comfort; DQN and PPO were faster in some cases but had lower safety and comfort. At the intersection, the rule-based method had higher safety but required 4.2 s more traversal time and had a 7.8-point lower comfort score than GT-RL. The findings support game-theoretic reasoning as a strategic prior for local interaction-aware trajectory generation. Claims are restricted to the tested low-to-moderate-speed, non-limit-handling simulations; high-fidelity vehicle dynamics and empirical large-scale timings remain areas of future study.