REACT: Runtime-Enabled Active Collision-avoidance Technique for Autonomous Driving (2505.11474v1)

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.

• The paper presents REACT, a novel framework for runtime collision avoidance in autonomous driving that integrates kinetic energy transfer and human-vehicle-road interaction modeling to quantify dynamic risk.
• Numerical simulations show that the REACT system achieved 100% effective collision avoidance with zero false alarms and a response latency below 50 ms across various high-risk scenarios.
• REACT's approach merges runtime risk assessment with real-time closed-loop control, representing a significant contribution to enhancing the safety, interpretability, and adaptability of autonomous vehicle systems.

Runtime-Enabled Active Collision-avoidance Technique for Autonomous Driving (REACT)

The research paper "Runtime-Enabled Active Collision-avoidance Technique for Autonomous Driving" presents the development of a novel framework named REACT. This system is designed to enhance the dynamic and real-time collision avoidance capabilities of autonomous vehicles in complex traffic scenarios. The paper focuses on a closing gap in existing autonomous driving technologies: the ability to anticipate and respond to unpredictable high-risk situations in real-time.

Overview of REACT

REACT integrates the principles of kinetic energy transfer and human-vehicle-road interaction modeling into a cohesive, lightweight system aimed at improving the safety and efficiency of autonomous driving. By constructing behavior-aware risk fields, the system dynamically quantifies risk through the continuous assessment of potential hazards. This framework combines runtime risk assessment with an active avoidance control strategy, enabling vehicles to navigate complex environments by identifying high-risk zones and proposing feasible avoidance maneuvers.

The methodology involves a grid-based risk mapping approach that discretizes the risk field into a two-dimensional grid centered on the ego vehicle. This grid enables directional risk attribution, facilitating the adaptation of safety strategies in response to dynamically shifting high-risk interactions. The hierarchical warning trigger strategy employed by REACT classifies risks into three levels—safe, warning, and emergency—based on dynamically adjusted risk thresholds.

Numerical Results and Performance

The REACT system's performance was evaluated through simulations that mimicked four typical high-risk driving scenarios: car-following braking, cut-in, rear-approaching, and intersection conflict. The framework demonstrated a notable ability to achieve 100% effective collision avoidance, with zero recorded false alarms or missed detections. Impressively, the system's warning lead time aligns closely with human driver cognition at a sub-0.4-second mark, an essential attribute for application in real-world driving. Furthermore, REACT's computational architecture achieved a response latency of less than 50 ms, underlining its potential for onboard deployment in autonomous vehicles.

Theoretical Implications and Future Directions

The introduction of a runtime-enabled, multi-source risk field model marks a significant contribution to the field of autonomous driving safety, offering an alternative to conventional rule-based and offline risk evaluation methods. REACT shifts the paradigm by merging risk quantification with real-time closed-loop control, thereby enhancing the interpretability and adaptability of autonomous vehicle safety systems.

The prospects for future development focus on extending the applicability of REACT to fully autonomous control environments and augmenting its capabilities in unstructured and diverse driving conditions. An enhancement in adaptability to uncharted domains could address current limitations related to parameter sensitivity and modeling uncertainties inherent in existing field-based methods.

Practical Implications

The deployment of REACT in autonomous vehicle systems can fundamentally elevate operational safety by enhancing foresight and maneuverability in complex traffic environments. This framework supports intelligent decision-making that mirrors human anticipatory behavior, a pivotal step towards achieving level 5 autonomy.

In summary, REACT exemplifies a significant advancement in the active collision avoidance domain within autonomous driving research. Its real-time, computationally efficient design holds promise for immediate implementation in safety-critical scenarios, bolstering the prospects for widespread adoption of autonomous vehicles in varied vehicular ecosystems. Continued research and development could broaden the impact of REACT, facilitating safer and more reliable autonomous driving on a global scale.