用视觉技术识别驾驶员分心与失能，无需车联即可分析非联网车辆。
Classification of Driver Behaviour Using External Observation Techniques for Autonomous Vehicles
- 通过视觉算法实时追踪车辆位置与轨迹变化
- 准确识别急转弯、偏移车道等危险驾驶行为
- 适合自动驾驶测试与交通监控场景使用
道路交通事故仍是全球重大问题，人为失误（尤其是分心和受药物影响驾驶）是主要原因。本研究提出一种新型驾驶员行为分类系统，利用外部观测技术检测分心与失能的指标。该框架采用先进计算机视觉方法，包括实时目标追踪、横向位移分析和车道位置监测。通过YOLO目标检测模型与自定义车道估计算法，系统可识别过度横向移动和不规则轨迹等危险驾驶行为。与依赖车际通信的系统不同，此基于视觉的方法能对未联网车辆进行行为分析。在多种视频数据集上的实验表明，该框架在不同道路与环境条件下均具备可靠性和适应性。
原文摘要 · Abstract (English)
Road traffic accidents remain a significant global concern, with human error, particularly distracted and impaired driving, among the leading causes. This study introduces a novel driver behaviour classification system that uses external observation techniques to detect indicators of distraction and impairment. The proposed framework employs advanced computer vision methodologies, including real-time object tracking, lateral displacement analysis, and lane position monitoring. The system identifies unsafe driving behaviours such as excessive lateral movement and erratic trajectory patterns by implementing the YOLO object detection model and custom lane estimation algorithms. Unlike systems reliant on inter-vehicular communication, this vision-based approach enables behavioural analysis of non-connected vehicles. Experimental evaluations on diverse video datasets demonstrate the framework's reliability and adaptability across varying road and environmental conditions.
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