Improving Functional Reliability of Near-Field Monitoring for Emergency Braking in Autonomous Vehicles

TL;DR

This paper presents three innovative monitoring strategies to enhance the reliability of near-field hazard detection in autonomous vehicles, reducing false positives and improving safety in emergency braking scenarios.

Contribution

The paper introduces three novel monitoring strategies based on spatial properties, object sizes, and motion prediction to improve near-field hazard detection reliability.

Findings

Significant reduction in false positives with new strategies

Improved hazard detection accuracy in simulation

Enhanced safety performance in emergency braking

Abstract

Autonomous vehicles require reliable hazard detection. However, primary sensor systems may miss near-field obstacles, resulting in safety risks. Although a dedicated fast-reacting near-field monitoring system can mitigate this, it typically suffers from false positives. To mitigate these, in this paper, we introduce three monitoring strategies based on dynamic spatial properties, relevant object sizes, and motion-aware prediction. In experiments in a validated simulation, we compare the initial monitoring strategy against the proposed improvements. The results demonstrate that the proposed strategies can significantly improve the reliability of near-field monitoring systems.