{"ID":2826481,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.18597","arxiv_id":"2512.18597","title":"Commercial Vehicle Braking Optimization: A Robust SIFT-Trajectory Approach","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%.","short_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...","url_abs":"https://arxiv.org/abs/2512.18597","url_pdf":"https://arxiv.org/pdf/2512.18597v1","authors":"[\"Zhe Li\",\"Kun Cheng\",\"Hanyue Mo\",\"Jintao Lu\",\"Ziwen Kuang\",\"Jianwen Ye\",\"Lixu Xu\",\"Xinya Meng\",\"Jiahui Zhao\",\"Shengda Ji\",\"Shuyuan Liu\",\"Mengyu Wang\"]","published":"2025-12-21T05:06:16Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.GR\"]","methods":"[]","has_code":false}