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

-- 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. T o 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. Advanced Driver Assistance Systems (ADASs) are rapidly advancing toward improved automation and safety in transportation; however, ensuring robust and reliable safety of self-driving vehicles remains a critical challenge. While Autonomous Emergency Braking Systems (AEBSs) have demonstrated potential in reducing collision risks, these systems often rely on the same sensor setup as the high-level driving system, which can miss hazards outside the sensors' Field-of-View (FOV). Especially in complex urban environments, such blind spots underscore the need for dedicated near-field monitoring systems as an additional safety layer.