Dynamic Game-Informed Lane Changing: Leveraging Stochastic Search for Autonomous Vehicle Decision-Making

Safe and efficient lane-changing remains a core challenge in autonomous driving, due to complex and uncertain inter-vehicle interactions in dynamic multi-agent environments. Existing prediction and rule-based methods often struggle to capture bidirectional strategic coupling and reason under behavioral uncertainty. In this work, we propose Dynamic Game-Informed Stochastic Search (DGSS), a novel lane changing decision-making algorithm that integrates multi-tree Monte Carlo Tree Search (MCTS) with game-theoretic modeling to capture adaptive interagent interactions. DGSS constructs a dedicated game tree for each relevant surrounding vehicle, enabling parallel turn-based strategic simulations and mutual behavioral adaptation over time. Candidate actions are evaluated through a multi-objective reward function that balances safety, spatial advantage, and driving efficiency. We validate DGSS through extensive experiments in both a custom local simulator and the large-scale real-world autonomous planning benchmark nuPlan. Results show that DGSS significantly improves safety and interaction-awareness compared to existing baselines, providing a safe, interpretable solution for autonomous lane-changing. Video is available at: https://sites.google.com/view/plan-dgss.