# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 2 Preliminary of Near-Crash Event Identification  
### Related Work  
Existing methods of near-crash event identification can be divided into three categories, i.e., statistical methods, threshold methods, and data-driven approaches. Statisticalmethods treat the results of near-crash event identification as a categorical dependent variable and construct statistical models to establish relationships between this dependent variable and observable independent variables. The data from near-crash events is used to calibrate the parameters in these statistical models, enabling the calibrated models to identify potential near-crash events. In addition to identifying near-crash events, statistical models can also reveal the relationships between each independent variable and the near-crash events. For example, [PERSON] et al. [10] utilized statistical methods to analyze the micro fluctuations in vehicle motion states before a collision, suggesting that nine types of pre-collision driving fluctuations, such as speed and deceleration, can be used to measure the severity of a collision. [PERSON] and [PERSON] [11] constructed a logistic regression model to analyze the impact of distracted driving duration on the probability of collision/near-collision events, concluding that the duration of various types of distraction is one of the main indicators for the occurrence of collision/near-collision events, with longer durations correlating with higher collision risk. [PERSON] et al. [12] used a mixed logistic regression model to analyze the relationship between fluctuations and the occurrence of hazardous events, finding a positive correlation between fluctuations and hazardous events, with near-end collisions and lane departure incidents being most affected by fluctuations. Statistical methods focus on analyzing the impact of specific factors on event occurrence. However, when it comes to identifying hazardous events, relying solely on these factors often results in underreporting the number of hazardous events. Threshold methods involve researchers setting specific indicators that represent the occurrence of a hazardous event and defining corresponding threshold values. When an indicator exceeds its threshold, it signifies a near-crash event. Various strategies based on naturalistic driving data have been devised for this purpose. [PERSON] et al. [13] observed that abnormal longitudinal vehicle acceleration, often resulting from emergency braking, effectively contributes to detecting various near-crash events in the 100-Car naturalistic driving study. Some studies have adapted [PERSON]'s thresholds for identifying near-crash events [14; 15]. Based on [PERSON]'s study, [PERSON] [16] developed a functional yaw rate classifier capable of identifying 92% of crash events and 81% of near-crash events, while reducing 42% of invalid or erroneous event identifications. [PERSON] et al. [17] evaluated multi-kinematic parameter thresholds to detect near-crash events from naturalistic driving data in the US and Canada, suggesting that some established thresholds in the literature may be overly sensitive. [PERSON] et al. [18] proposed threshold methods and [PERSON]'s test to filter near-crash events, which employed survival analysis and ROC curves to fine-tune the optimal detection thresholds. Although threshold methods might generate false positives, they are suitable for preliminary screening due to their efficiency in filtering a significant portion of near-crash events, thereby facilitating the early stages of causation analysis. To address the high false-positive rate issue of threshold methods, researchers have turned to machine learning techniques, which excel at identifying complex patterns, for identifying safety-critical events in naturalistic driving data. [PERSON] et al. [19] experimented with various machine learning methods, including K-nearest neighbors (KNN), random forest, and support vector machine (SVM), to predict safety-critical events based on vehicular kinematic data. [PERSON] et al.