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

2025-05-16

AI Summary

Real-time proactive collision avoidance remains challenging in dynamic, interactive traffic environments. Method: This paper proposes a closed-loop collision avoidance framework integrating physics-based safety constraints with human-vehicle-road cooperative modeling. It constructs a continuous-space risk field grounded in energy transfer principles, incorporates directional risk assessment and traffic regulation constraints to enable interpretable behavior generation, and designs a hierarchical warning-triggering mechanism with a lightweight architecture to ensure human cognitive alignment. Contribution/Results: To our knowledge, this is the first work to embed physics-informed safety priors into end-to-end collision avoidance decision-making. It achieves 100% safe avoidance across four high-risk scenarios. The system attains warning latency < 0.4 s, total system latency < 50 ms, and zero false positives or missed detections. It demonstrates strong foresight, cross-scenario generalizability, and feasibility for edge-device deployment.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Low Level & Physics-based Vision
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• Achieving rapid active collision avoidance in dynamic traffic
• Integrating risk assessment with real-time avoidance control
• Ensuring real-time responsiveness and safety in autonomous driving

Innovation

• Closed-loop framework integrating risk assessment and avoidance control
• Dynamic risk quantification using energy transfer principles
• Hierarchical warning strategy for real-time responsiveness