🤖 AI Summary
To address the “zero-speed false braking” issue in commercial vehicle Automatic Emergency Braking (AEB) systems caused by CAN signal inaccuracies under low-speed conditions, this paper proposes a high-precision vehicle motion state recognition method leveraging blind-zone camera video streams. The approach introduces three key innovations: (1) a five-frame sliding-window trajectory displacement statistic; (2) a dual-threshold state decision matrix; and (3) an OBD-II–driven dynamic Region-of-Interest (ROI) configuration mechanism—collectively mitigating environmental interference and false detection of moving objects. Motion estimation employs CLAHE-enhanced image preprocessing, SIFT feature extraction, and KNN-RANSAC matching, achieving real-time processing at 14.2 ms per frame on Jetson AGX Xavier for 704×576-resolution video. Experimental results show F1-scores of 99.96% for stationary and 97.78% for moving state classification. Field deployment reduces false braking incidents by 89%, achieves 100% emergency braking success rate, and maintains system fault rate below 5%.
📝 Abstract
A vision-based trajectory analysis solution is proposed to address the "zero-speed braking" issue caused by inaccurate Controller Area Network (CAN) signals in commercial vehicle Automatic Emergency Braking (AEB) systems during low-speed operation. The algorithm utilizes the NVIDIA Jetson AGX Xavier platform to process sequential video frames from a blind spot camera, employing self-adaptive Contrast Limited Adaptive Histogram Equalization (CLAHE)-enhanced Scale-Invariant Feature Transform (SIFT) feature extraction and K-Nearest Neighbors (KNN)-Random Sample Consensus (RANSAC) matching. This allows for precise classification of the vehicle's motion state (static, vibration, moving). Key innovations include 1) multiframe trajectory displacement statistics (5-frame sliding window), 2) a dual-threshold state decision matrix, and 3) OBD-II driven dynamic Region of Interest (ROI) configuration. The system effectively suppresses environmental interference and false detection of dynamic objects, directly addressing the challenge of low-speed false activation in commercial vehicle safety systems. Evaluation in a real-world dataset (32,454 video segments from 1,852 vehicles) demonstrates an F1-score of 99.96% for static detection, 97.78% for moving state recognition, and a processing delay of 14.2 milliseconds (resolution 704x576). The deployment on-site shows an 89% reduction in false braking events, a 100% success rate in emergency braking, and a fault rate below 5%.