Pan, Junnan (University of the Bundeswehr Munich), Sotiriadis, Prodromos (University of the Bundeswehr Munich), Nenchev, Vladislav (University of the Bundeswehr Munich), Englberger, Ferdinand (University of the Bundeswehr Munich)

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

Scheduled for presentation during the Regular Session "S37a-Reliable Perception and Robust Sensing for Intelligent Vehicles" (FR-LM-T37), Friday, November 21, 2025, 11:10−11:30, Coolangata 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

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 strategies against the proposed improvements. The results demonstrate that the proposed strategies can significantly improve the reliability of near-field monitoring systems.