Feng Zhongxiang, Wei Xinyi, Bi Yu, Zhu Dianchen, Huang Zhipeng
School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei, China.
Traffic Inj Prev. 2025;26(2):198-206. doi: 10.1080/15389588.2024.2399301. Epub 2024 Oct 2.
In recent years, the increase in traffic accidents has emerged as a significant social issue that poses a serious threat to public safety. The objective of this study is to predict risky driving scenarios to improve road safety.
On the basis of data collected from naturalistic driving real-vehicle experiments, a comprehensive framework for identifying and analyzing risky driving scenarios, which combines an integrated lane-changing detection model and an attention-based long short-term memory (LSTM) prediction model, is proposed. The performance of the 4 machine learning methods on the CULane data set is compared in terms of model running time and running speed as evaluation metrics, and the ultrafast network with the best performance is selected as the method for lane line detection. We compared the performance of LSTM and attention-based LSTM on the basis of the prediction accuracy, recall, precision, and F1 value and selected the better model (attention-based LSTM) for risky scenario prediction. Furthermore, Shapley additive explanation analysis (SHAP) is used to understand and interpret the prediction results of the model.
In terms of algorithm efficiency, the running time of the ultrafast lane detection network only requires 4.1 ms, and the average detection speed reaches 131 fps. For prediction performance, the accuracy rate of attention-based LSTM reaches 96%, the precision rate is 98%, the recall rate is 96%, and the F1 value is 97%.
The improved attention-based LSTM model is significantly better than the LSTM model in terms of convergence speed and prediction accuracy and can accurately identify risky scenarios that occur during driving. The importance of factors varies by risky scenario. The characteristics of the yaw rate, speed stability, vehicle speed, acceleration, and lane change significantly influence the driving risk, among which lane change has the greatest impact. This study can provide real-time risky scenario prediction, warnings, and scientific decision guidance for drivers.
近年来，交通事故的增加已成为一个重大社会问题，对公共安全构成严重威胁。本研究的目的是预测危险驾驶场景以提高道路安全。
基于从自然驾驶实车实验收集的数据，提出了一个用于识别和分析危险驾驶场景的综合框架，该框架结合了集成变道检测模型和基于注意力的长短期记忆（LSTM）预测模型。以模型运行时间和运行速度作为评估指标，比较了4种机器学习方法在CULane数据集上的性能，并选择性能最佳的超快速网络作为车道线检测方法。基于预测准确率、召回率、精确率和F1值比较了LSTM和基于注意力的LSTM的性能，并选择更好的模型（基于注意力的LSTM）进行危险场景预测。此外，使用Shapley加法解释分析（SHAP）来理解和解释模型的预测结果。
在算法效率方面，超快速车道检测网络的运行时间仅需4.1毫秒，平均检测速度达到131帧/秒。对于预测性能，基于注意力的LSTM的准确率达到96%，精确率为98%，召回率为96%，F1值为9
