Our paper: 'Safety-Critical Multi-Agent MCTS for Mixed Traffic Coordination at Unsignalized Intersections' has been accepted in IEEE Transactions on Intelligent Transportation Systems
Published: 30 September 2025
Our paper: 'Safety-Critical Multi-Agent MCTS for Mixed Traffic Coordination at Unsignalized Intersections' has been accepted in IEEE Transactions on Intelligent Transportation Systems! Great work by: Zhihao Lin; Jianglin Lan; Christos Anagnostopoulos; Zhen Tian; David Flynn
Decision making at unsignalized intersections presents significant challenges for autonomous vehicles (AVs), particularly in mixed traffic scenarios where both AVs and human-driven vehicles (HDVs) must safely coordinate their movements. This paper proposes a safety-critical multi-agent Monte Carlo tree search (MCTS) framework that integrates deterministic and probabilistic predictions to enable cooperative decision making in complex intersection scenarios. The framework incorporates three main innovations: 1) a safety assessment mechanism that systematically handles AV-to-AV (V2V), AV-to-HDV (V2H), and Vehicle-to-Road (V2R) interactions using dynamic safety thresholds and spatiotemporal risk metrics, 2)an adaptive HDV behavior awareness by combining the Intelligent Driver Model (IDM) with probabilistic distributions, and 3)a multi-objective reward function optimization approach that balances safety, efficiency, and cooperation. Extensive simulations demonstrate our framework’s efficacy and superior capability in ensuring safe and efficient intersection navigation across the fully-autonomous scenario (100% AVs) and challenging mixed traffic scenario (50% AVs +50% HDVs). Compared to benchmarks, our method reduces trajectory deviations by up to 37.56% in the fully-autonomous scenario and 62.43% in the mixed traffic scenario, while maintaining significantly lower Post-Encroachment Time (PET) violations (0% and 2.8%, respectively).
https://ieeexplore.ieee.org/abstract/document/11130528
First published: 30 September 2025