## 1 Introduction  
Road traffic accidents represent one of the most critical public health issues of the modern era, exacting a devastating toll on human life and global economies. According to the World Health Organization (WHO), approximately 1.19 million lives are lost annually due to road collisions, with an additional 50 million individuals sustaining severe injuries [1]. These road accidents also impact the GDP of countries, as countries allocate nearly 3% of their annual GDP to address medical expenses, infrastructure repairs, and productivity losses linked to traffic incidents. These numbers rise even more in developing countries, where traffic safety rules are sometimes lenient and emergency response systems are inadequate, which fuels poverty and public health inequality in developing countries [2]. Moreover, according to the National Safety Council (NSC) [3], in 2023, car collisions claimed 19,400 lives out of the 44,762 total motor vehicle deaths in the United States, making up over 43% of the fatalities. The inherent unpredictability of road accidents poses a difficult challenge to efforts to mitigate them. Collisions frequently arise from dynamic, non-linear interactions among human error, environmental conditions, and mechanical failures--factors that defy straightforward modeling [4; 5]. Traditional reactive approaches, such as post-accident crash analysis or manual surveillance, suffer from critical limitations: they are labor-intensive, prone to human error, and incapable of enabling real-time intervention [6]. For instance, delayed accident detection on highways or in construction zones increases secondary risks, including prolonged congestion and delayed emergency response, which collectively increase economic losses and public frustration. The advancement in computer vision and deep learning offers transformative potential to address these gaps. Automated systems capable of real-time collision detection could revolutionize road safety by enabling proactive measures, from rerouting traffic to dispatching first-aid teams within seconds of an incident [7]. Recent research has leveraged deep learning for traffic-related applications, including vehicle detection [8; 9; 10], pedestrian tracking [11], and accident severity assessment [12; 13; 14]. [PERSON] [15] proposed an attention-based Stack ResNet for Citywide Traffic Accident Prediction framework which combines static spatial data with dynamic spatio-temporal information using a ResNet-based model and an attention mechanism to prioritize temporally relevant features and incorporates a speed inference module to address missing speed data. [PERSON] et al. [16] proposed an automated, real-time Accident Detection and Severity Classification System that leverages YOLOv5 to identify accidents in video images and a machine learning classifier to categorize them into mild, moderate, or severe levels, triggering rapid alerts to authorities. To reduce latency and network usage in emergency detection, [PERSON] et al. [17] proposed a Deep Learning-Based Car Accident Detection Framework which employs a 2D CNN on edge nodes for local video processing while offloading essential data to the cloud for further analysis. [PERSON] et al. [18] proposed the challenge of detecting near-accidents at traffic intersections in real-time, such as sudden lane changes or pedestrian crossings. They introduce a two-stream (spatial and temporal) convolutional network architecture designed for real-time detection, tracking, and near-accident prediction. The spatial stream uses the YOLO algorithm to detect vehicles and potential accident regions in individual video frames, while the temporal stream analyzes motion features across multiple frames to generate vehicle trajectories. By combining these spatial and temporal features, the system computes collision probabilities, flagging high-risk regions when trajectories intersect or come too close. [PERSON] et al.