REACT: Runtime-Enabled Active Collision-avoidance Technique for Autonomous Driving

TL;DR

REACT is a real-time collision avoidance framework for autonomous vehicles that integrates risk assessment with active control, ensuring safety and efficiency in dynamic traffic scenarios.

Contribution

It introduces a novel closed-loop system combining risk quantification, safety constraints, and efficient avoidance strategies for autonomous driving.

Findings

Achieves 100% safe avoidance with zero false alarms.

Maintains real-time response under 50 ms latency.

Effectively predicts critical risks aligning with human cognition.

Abstract

Achieving rapid and effective active collision avoidance in dynamic interactive traffic remains a core challenge for autonomous driving. This paper proposes REACT (Runtime-Enabled Active Collision-avoidance Technique), a closed-loop framework that integrates risk assessment with active avoidance control. By leveraging energy transfer principles and human-vehicle-road interaction modeling, REACT dynamically quantifies runtime risk and constructs a continuous spatial risk field. The system incorporates physically grounded safety constraints such as directional risk and traffic rules to identify high-risk zones and generate feasible, interpretable avoidance behaviors. A hierarchical warning trigger strategy and lightweight system design enhance runtime efficiency while ensuring real-time responsiveness. Evaluations across four representative high-risk scenarios including car-following braking, cut-in, rear-approaching, and intersection conflict demonstrate REACT's capability to accurately identify critical risks and execute proactive avoidance. Its risk estimation aligns closely with human driver cognition (i.e., warning lead time < 0.4 s), achieving 100% safe avoidance with zero false alarms or missed detections. Furthermore, it exhibits superior real-time performance (< 50 ms latency), strong foresight, and generalization. The lightweight architecture achieves state-of-the-art accuracy, highlighting its potential for real-time deployment in safety-critical autonomous systems.