# A Robust Cooperative Vehicle Coordination Framework for Intersection Crossing

arXiv: https://arxiv.org/abs/2508.03417 · HTML: https://mlsanity.com/paper/2508.03417/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [SCORP: Scene-Consistent Multi-agent Diffusion Planning with Stable Online Reinforcement Post-Training for Cooperative Driving](https://mlsanity.com/paper/2604.11734/) (2026)

## Abstract (arXiv)

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.