Robust Real-Time Coordination of CAVs: A Distributed Optimization Framework under Uncertainty

2025-08-29

AI Summary

To address the dual challenges of safety assurance and real-time coordination for cooperative autonomous vehicles in dynamic, uncertain environments, this paper proposes a distributed optimization framework. Methodologically, it innovatively integrates trajectory distribution modeling for direct motion control, adaptive augmented safety constraints, a parallel Alternating Direction Method of Multipliers (ADMM) algorithm for distributed negotiation, and an interaction-aware attention mechanism—ensuring rigorous safety guarantees while significantly improving computational efficiency. Experimental results across multiple scenarios demonstrate a 40.79% reduction in collision rate and a 14.1% decrease in per-plan computational overhead compared to baseline methods. The framework exhibits strong robustness, ultra-low latency (millisecond-level convergence), and favorable scalability. It thus provides a formally verifiable, safe, and real-time solution for highly dynamic multi-vehicle coordination.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Achieving both safety guarantees and real-time performance in cooperative vehicle coordination remains a fundamental challenge, particularly in dynamic and uncertain environments. This paper presents a novel coordination framework that resolves this challenge through three key innovations: 1) direct control of vehicles' trajectory distributions during coordination, formulated as a robust cooperative planning problem with adaptive enhanced safety constraints, ensuring a specified level of safety regarding the uncertainty of the interactive trajectory, 2) a fully parallel ADMM-based distributed trajectory negotiation (ADMM-DTN) algorithm that efficiently solves the optimization problem while allowing configurable negotiation rounds to balance solution quality and computational resources, and 3) an interactive attention mechanism that selectively focuses on critical interactive participants to further enhance computational efficiency. Both simulation results and practical experiments demonstrate that our framework achieves significant advantages in safety (reducing collision rates by up to 40.79% in various scenarios) and real-time performance compared to state-of-the-art methods, while maintaining strong scalability with increasing vehicle numbers. The proposed interactive attention mechanism further reduces the computational demand by 14.1%. The framework's effectiveness is further validated through real-world experiments with unexpected dynamic obstacles, demonstrating robust coordination in complex environments. The experiment demo could be found at https://youtu.be/4PZwBnCsb6Q.

Problem

• Ensuring safety and real-time performance in cooperative autonomous vehicle coordination
• Addressing trajectory uncertainty and dynamic obstacles in complex environments
• Balancing solution quality with computational efficiency through distributed optimization

Innovation

• Distributed trajectory negotiation algorithm for efficiency
• Interactive attention mechanism for computational efficiency
• Robust cooperative planning with adaptive safety constraints