aiqbench analysis
This research proposes a novel algorithm for automated vehicle safety channels that uses predicted motion corridors to filter LiDAR data and optimize emergency braking decisions under computational constraints. The approach combines geometric occupancy scoring with temporal urgency analysis to ensure reliable hazard detection in both straight-line and curved driving scenarios. While the application involves autonomous vehicles, the core subject is the development of specific machine learning and systems control algorithms for safety-critical decision-making.
Corridor-Based Monitoring and Emergency Braking Decision for Automated Vehicle Safety
arXiv:2611.06287v1 Announce Type: new Abstract: This paper addresses emergency-braking decisions for a redundant safety channel in automated vehicles. The channel is responsible for identifying hazards from sensor data and determining whether braking is necessary, given the constrained computational resources of embedded hardware. We present an emergency-braking approach that uses a predicted motion corridor to filter \ac{lidar} measurements and then decide whether the ego vehicle must brake. \ac{pmcm} constructs a curvature-aware boundary from the planned path and vehicle geometry so that the monitored region contains the full swept body on both straight and curved paths. \ac{gdeta} then decides whether the ego vehicle must command an emergency brake for objects inside this boundary, scoring geometric occupancy and temporal urgency independently. Hard gates permit braking when either source is conclusive; soft-OR arbitration combines partial evidence when neither gate is active. Consecutive-scan confirmation reduces sensitivity to transient measurement noise. Corridor coverage is evaluated in controlled collision simulations and recorded campus driving. Braking decisions are evaluated in Monte Carlo simulations of lead-vehicle braking and transverse-crossing encounters.
Abstract:This paper addresses emergency-braking decisions for a redundant safety channel in automated vehicles. The channel is responsible for identifying hazards from sensor data and determining whether braking is necessary, given the constrained computational resources of embedded hardware. We present an emergency-braking approach that uses a predicted motion corridor to filter \ac{lidar} measurements and then decide whether the ego vehicle must brake. \ac{pmcm} constructs a curvature-aware boundary from the planned path and vehicle geometry so that the monitored region contains the full swept body on both straight and curved paths. \ac{gdeta} then decides whether the ego vehicle must command an emergency brake for objects inside this boundary, scoring geometric occupancy and temporal urgency independently. Hard gates permit braking when either source is conclusive; soft-OR arbitration combines partial evidence when neither gate is active. Consecutive-scan confirmation reduces sensitivity to transient measurement noise. Corridor coverage is evaluated in controlled collision simulations and recorded campus driving. Braking decisions are evaluated in Monte Carlo simulations of lead-vehicle braking and transverse-crossing encounters.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2611.06287 [cs.RO]
(or arXiv:2611.06287v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.06287 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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