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

## TL;DR

This paper introduces a distributed optimization framework for real-time, safe coordination of connected autonomous vehicles under uncertainty, combining robust planning, parallel algorithms, and attention mechanisms.

## Contribution

It proposes a novel robust cooperative planning method with adaptive safety constraints, a parallel ADMM-based negotiation algorithm, and an interactive attention mechanism for efficiency.

## Key findings

- Reduced collision rates by up to 40.79% in simulations.
- Achieved 15.4% reduction in computational demand.
- Demonstrated robustness and real-time feasibility in real-world tests.

## Abstract

Achieving both safety guarantees and real-time performance in cooperative vehicle coordination remains a fundamental challenge, particularly in dynamic and uncertain environments. Existing methods often suffer from insufficient uncertainty treatment in safety modeling, which intertwines with the heavy computational burden under complex multi-vehicle coupling. 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. Simulation results 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 representative benchmarks, while maintaining strong scalability with increasing vehicle numbers. The proposed interactive attention mechanism further reduces the computational demand by 15.4\%. Real-world experiments further validate robustness and real-time feasibility with unexpected dynamic obstacles, demonstrating reliable coordination in complex traffic scenes. The experiment demo could be found at https://youtu.be/4PZwBnCsb6Q.