A Robust Cooperative Vehicle Coordination Framework for Intersection Crossing

2025-08-05

AI Summary

Safety risks in cooperative control at signal-free intersections arise from vehicle state uncertainty and communication constraints. Method: This paper proposes a robust cooperative framework that innovatively integrates trajectory distribution evolution control with a context-aware state update mechanism based on driving urgency, enabling cooperative trajectory planning under probabilistic safety constraints and bandwidth-sensitive dynamic priority scheduling. Contribution/Results: It is the first work to jointly model state update scheduling and trajectory distribution control, significantly reducing collision probability while maintaining coordination efficiency and wireless resource utilization. The framework outperforms state-of-the-art methods under limited bandwidth and high uncertainty, and its theoretical reliability is guaranteed through formal probabilistic safety verification.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Cooperative vehicle coordination at unsignalized intersections has garnered significant interest from both academia and industry in recent years, highlighting its notable advantages in improving traffic throughput and fuel efficiency. However, most existing studies oversimplify the coordination system, assuming accurate vehicle state information and ideal state update process. The oversights pose driving risks in the presence of state uncertainty and communication constraint. To address this gap, we propose a robust and comprehensive intersection coordination framework consisting of a robust cooperative trajectory planner and a context-aware status update scheduler. The trajectory planner directly controls the evolution of the trajectory distributions during frequent vehicle interactions, thereby offering probabilistic safety guarantees. To further align with coordination safety in practical bandwidth-limited conditions, we propose a context-aware status update scheduler that dynamically prioritizes the state updating order of vehicles based on their driving urgency. Simulation results validate the robustness and effectiveness of the proposed coordination framework, showing that the collision probability can be significantly reduced while maintaining comparable coordination efficiency to state-of-theart strategies. Moreover, our proposed framework demonstrates superior effectiveness in utilizing wireless resources in practical uncertain and bandwidth-limited conditions.

Problem

• Addresses vehicle coordination at unsignalized intersections under uncertainty
• Ensures safety despite state inaccuracies and communication constraints
• Optimizes wireless resource use in bandwidth-limited, uncertain conditions

Innovation

• Robust cooperative trajectory planner for safety
• Context-aware status update scheduler for urgency
• Probabilistic safety guarantees in vehicle interactions