Commercial Vehicle Braking Optimization: A Robust SIFT-Trajectory Approach

• URL: http://arxiv.org/abs/2512.18597v1
• Date: Sun, 21 Dec 2025 05:06:16 GMT
• Title: Commercial Vehicle Braking Optimization: A Robust SIFT-Trajectory Approach
• 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,
• Abstract summary: A vision-based trajectory analysis solution is proposed to address the "zero-speed braking" issue caused by inaccurate Controller Area Network (CAN) signals.<n>The algorithm utilizes the NVIDIA Jetson AGX Xavier platform to process sequential video frames from a blind spot camera.<n>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%.
• Score: 6.751326589596112
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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%.

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