Technology Category
Computer Vision: Motion & TrackingIntelligent Robots: State EstimationPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
Improving Functional Reliability of Near-Field Monitoring for Emergency Braking in Autonomous Vehicles
🤖 AI Summary
Near-field obstacle detection for autonomous vehicles suffers from sensor blind spots and false positives, compromising emergency braking safety. To address this, we propose a near-field monitoring framework integrating dynamic spatial modeling, object-scale adaptive analysis, and motion-trend prediction. We design three novel synergistic strategies: (1) vehicle-kinematics-driven dynamic perception region partitioning, (2) size-sensitive confidence-weighting, and (3) short-horizon trajectory–guided false-positive suppression. Validated in a high-fidelity simulation environment with realistic sensor modeling and dynamic threshold optimization, our method reduces false positive rate by 42.7% while maintaining a 98.3% true obstacle recall rate. This work is the first to systematically jointly model spatial dynamics, geometric scale, and motion semantics for real-time near-field detection—significantly enhancing functional safety and reliability under critical driving scenarios.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Enhancing near-field obstacle detection for autonomous vehicles
Reducing false positives in emergency braking systems
Improving reliability using dynamic spatial and motion-aware strategies
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic spatial properties enhance monitoring
Relevant object sizes improve detection
Motion-aware prediction reduces false positives
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