Xiang, Tan (BeiHang University), Wang, Ziyan (buaa), Li, Ding (Beihang University), Chen, Peng (Beihang University)

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

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Abstract

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 decision-making framework that integrates multi-tree Monte Carlo Tree Search (MCTS) with game-theoretic modeling to capture recursive inter-agent 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 nuPlan dataset. Results show that DGSS significantly improves safety and interaction-awareness compared to existing baselines, providing a safe, interpretable, and robust solution for autonomous lane-changing.