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

arXiv: https://arxiv.org/abs/2505.11474 · HTML: https://mlsanity.com/paper/2505.11474/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 16 May 2025.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 4 of 4)

- [Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification](https://mlsanity.com/paper/2404.18573/) (2024)
- [Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planning](https://mlsanity.com/paper/1609.05399/) (2016)
- [Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving](https://mlsanity.com/paper/2302.07109/) (2023)
- [SafeCast: Risk-Responsive Motion Forecasting for Autonomous Vehicles](https://mlsanity.com/paper/2503.22541/) (2025)

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

- [A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems](https://mlsanity.com/paper/2509.22379/) (2025)

## Abstract (arXiv)

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.