# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 6 Conclusions  
This study presents a novel approach to identify near-crash events on mountainous roads, employing naturalistic driving data from Yunnan. It introduces a CNN-LSTM-Attention model, which stands out for its innovative integration of the convolutional neural networks (CNN), the long short-term memory networks (LSTM), and the attention mechanism. This model demonstrates unparalleled accuracy and efficiency in detecting near-crash scenarios compared to traditional models.  
In the feature analysis, we employ the random forest algorithm for feature selection of the most important vehicle motion features, including x-axis acceleration, y-axis acceleration, y-axis angular velocity, heading angle, and speed. These features are instrumental in the superior performance of the CNN-LSTM-Attention model, which achieved impressive metrics: a recall rate of 98.5% and an F1 score of 0.941 for near-crash events, and for normal events, an even higher recall rate of 98.9% and an F1 score of 0.993. Comparative analyses underscore the CNN-LSTM-Attention model's supremacy, which achieves an overall accuracy of 98.8% and an AUC of 0.997, significantly outperforming the CNN, LSTM, Attention, CNN-LSTM, CNN-Attention, and LSTM-Attention models. Its precision rate of 90.1% in identifying near-crash events represented a remarkable improvement over the other models. It highlights the critical importance of the CNN, LSTM, and attention mechanisms in achieving high performance.  
In the future, this study could be significantly enhanced by incorporating a broader spectrum of input variables into the model, including driver physiological data, leading vehicle dynamics obtained from in-vehicle cameras, and millimeter-wave radar. Additionally, an sensitivity analysis of the model performance would be valuable. Such enhancements hold promise to further refine the model's precision on mountainous roads with complex driving environments.  
Conceptualization, J.Z., W.Y. and F.Z.; Methodology, J.Z., W.Y. and F.Z.; Validation, J.Z.; Formal analysis, J.Z., W.Y. and F.Z.; Investigation, J.Z., W.Y. and F.Z.; Resources, W.Y.; Data curation, J.Z. and W.Y.; Writing--original draft, J.Z.; Writing--review & editing, W.Y. and F.Z.; Visualization, J.Z. and F.Z.; Supervision, W.Y. and F.Z.; Project administration, W.Y.; Funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was jointly funded by the National Key Research and Development Program of China (2022 YFC3002601), and the Science and Technology Innovation Program of the Department of Transportation, Yunnan Province, China (2022-107).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The code of the study could be accessed at: [[https://ldrv.ms/f/s](https://ldrv.ms/f/s)]([https://ldrv.ms/f/s](https://ldrv.ms/f/s))!AjiNnfVhflakrrnlaw30 xsGIIU4 Jn?e=%fGyM3 (accessed on 2 June 2024).  
**Acknowledgments:** The authors would like to thank [PERSON] for the invaluable assistance with data processing in this study.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Broadvision Engineering Consultants Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.