Citation

Lin C, Zhang S, Gong B, Liu H. Accid. Anal. Prev. 2024; 199: e107520.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.aap.2024.107520

PMID

38412766

Abstract

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.

Language: en

Keywords

Near crash; Risk identification and quantization; Roadside LiDAR; Takeout motorcycle; Trajectory prediction