对使用路边LiDAR的外卖送货摩托车进行接近碰撞风险的识别和评估
Ciyun Lin1, Shaoqi Zhang2, Bowen Gong2
1Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China; Jilin Engineering Research Center for Intelligent Transportation System, Changchun 130022, China.
Accident; analysis and prevention
|February 27, 2024
概括
这项研究引入了一种使用路边光检测和距离测量 (LiDAR) 的新方法,以准确评估配送摩托车的近碰撞风险. 该方法增强了轨迹预测和量化风险,改进了城市交通安全分析.
科学领域:
- 交通安全工程 交通安全工程
- 运输系统分析 运输系统分析
- 传感器技术 传感器技术
背景情况:
- 城市摩托车的普及增加了交通安全问题.
- 传统传感器在捕获高分辨率摩托车轨迹数据方面的局限性阻碍了接近碰撞的风险评估.
- 准确识别交货摩托车近碰撞风险对于主动安全措施至关重要.
研究的目的:
- 开发一种使用路边LiDAR的新方法,用于识别和评估外卖送货摩托车的接近撞车风险.
- 为了提高摩托车的轨迹预测准确性和连续性.
- 量化接近碰撞的风险水平,并支持积极的交通安全分析.
主要方法:
- 利用路边光探测和距离测定 (LiDAR) 来获取摩托车轨迹数据.
- 实施了轨迹修改方法,包括速度和转向角度,以改善预测.
- 开发了一种接近碰撞的预测模型,该模型结合了方向盘的意图和排斥力模型.
- 建立了基于最近距离和风险半径的近碰撞识别方法.
- 使用速度,位置和重量来量化风险,构建了接近碰撞的风险场.
主要成果:
- 在预测摩托车转向意图方面获得了92.10%的准确性.
- 获得了低预测误差,平均绝对误差 (MAE) 为0.53m,根平均平方误差 (RMSE) 为0.45m.
- 证明了该方法在准确的近碰撞风险量化和可视化方面的能力.
结论:
- 提出的基于LiDAR的方法显著提高了摩托车轨迹预测的准确性和连续性.
- 该方法有效地识别和量化了接近碰撞的风险,从而实现了积极的交通安全战略.
- 这种新的技术为城市交通安全提供了增强的可视化和分析能力,特别是在配送摩托车方面.
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