This study addresses the challenge of cooperative decision-making in Connected Autonomous Vehicles (CAVs) across diverse road topologies. Current methodologies are limited to specific scenarios, but the authors propose a universal optimization approach that can handle a wide range of traffic scenarios. The approach uses Directed Acyclic Graphs (DAGs) to represent various traffic scenarios and solves the decision-making problem as a mixed-integer linear programming problem. The approach considers factors like velocities, accelerations, conflict resolutions, and overall traffic efficiency. Case studies show the effectiveness of the proposed methodology.
Publication date: 12 Jan 2024
Project Page: Unavailable
Paper: https://arxiv.org/pdf/2401.04968
