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%.

翻译：本文提出了一种基于视觉的轨迹分析解决方案，以解决商用车自动紧急制动系统在低速运行时，因控制器局域网信号不准确而导致的"零速制动"问题。该算法利用NVIDIA Jetson AGX Xavier平台处理来自盲点摄像头的连续视频帧，采用自适应对比度受限自适应直方图均衡增强的尺度不变特征变换特征提取和K最近邻-随机抽样一致匹配方法，从而实现对车辆运动状态的精确分类。关键创新包括：1）多帧轨迹位移统计，2）双阈值状态决策矩阵，以及3）车载诊断系统驱动的动态感兴趣区域配置。该系统有效抑制了环境干扰和动态物体的误检测，直接解决了商用车安全系统中低速误触发的难题。在真实数据集上的评估结果表明，静态检测的F1分数达到99.96%，运动状态识别的F1分数为97.78%，处理延迟为14.2毫秒。现场部署显示，误制动事件减少了89%，紧急制动成功率达到100%，故障率低于5%。