The proliferation of motorcycles in urban areas has raised concerns regarding traffic safety. However, traditional sensors struggle to obtain precise high-resolution trajectory data, which hinder the accurate identification and quantification of near-crash risks for takeout delivery motorcycles. To fill this gap, this study presents a novel approach utilizing roadside light detection and ranging (LiDAR) to identify and evaluate the risk of near crashes of takeout delivery motorcycles. First, a trajectory amendment method incorporating speed and steering angle was introduced to enhance the accuracy and continuity of the trajectory prediction. Second, a trajectory prediction method combining the steering intention and a repulsive force model was proposed for near-crash risk prediction. Subsequently, a near-crash identification method was developed that relied on the closest distance and risk radius. Finally, near-crash risk fields were constructed to quantify risk levels by leveraging velocity, position, and weight. The experimental results demonstrated 92.10 % accuracy in intention prediction, with mean absolute error (MAE) and root mean square error (RMSE) values of 0.53 m and 0.45 m, respectively. In addition to its higher accuracy, the proposed method makes it easier to quantify near-crash risk and supports a proactive approach for visualizing and analyzing traffic safety.

中文翻译：

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利用路边激光雷达进行外卖摩托车的近碰撞风险识别与评估

摩托车在城市地区的泛滥引起了人们对交通安全的担忧。然而，传统传感器难以获得精确的高分辨率轨迹数据，这阻碍了外卖摩托车险些碰撞风险的准确识别和量化。为了填补这一空白，本研究提出了一种利用路边光检测和测距（LiDAR）的新方法来识别和评估外卖摩托车险些发生碰撞的风险。首先，引入了结合速度和转向角的轨迹修正方法，以增强轨迹预测的准确性和连续性。其次，提出了一种结合转向意图和斥力模型的轨迹预测方法，用于近碰撞风险预测。随后，开发了一种依赖最近距离和风险半径的临近碰撞识别方法。最后，构建了近乎崩溃的风险场，通过利用速度、位置和重量来量化风险水平。实验结果表明，意图预测的准确度为 92.10%，平均绝对误差 (MAE) 和均方根误差 (RMSE) 值分别为 0.53 m 和 0.45 m。除了具有更高的准确性外，所提出的方法还可以更轻松地量化接近碰撞的风险，并支持用于可视化和分析交通安全的主动方法。