## 2 Literature Review
VRDeepSafety provides a large-scale, multi-modal dataset for training and testing accident prediction models. Unlike existing datasets, which are often limited in scope, VRDeepSafety simulates a wide range of accident scenarios, including multi-vehicle collisions, pedestrian incidents, and accidents related to infrastructure and, by including V2X data in the simulation, allows researchers to build and test models that are more accurate, generalizable, and capable of handling the unpredictable nature of real-world traffic. The adoption of V2X communication technologies is widely regarded as a promising development for improving road safety and reducing traffic accidents. V2X protocols enable vehicles to exchange information with each other (V2V), with roadside infrastructure (V2I), with pedestrians (V2P), and with other elements of the traffic ecosystem and, by enabling real-time data exchange, enhances situational awareness and provides quicker and more effective reactions to potential threats. [PERSON] et al. [15] describe how V2X improves safety by enabling vehicles to share vital information like position, speed, and heading with other vehicles and nearby infrastructure, improving reaction time and reducing the likelihood of accidents. The literature well documents the capabilities of V2X communication. For instance, [PERSON] et al. [16] reviewed the impact of the V2X protocol, specifically the IEEE 802.11p standard for Wireless Access in Vehicular Environments (WAVE), on accident prevention systems. Their study shows how these protocols reduce latency and improve data transmission, both critical for real-time safety applications. By enabling vehicles to share their position, speed, and trajectory with other vehicles and infrastructure, V2X communication facilitates more coordinated and informed decision-making, particularly in high-traffic areas where accidents are more likely. [PERSON] et al. [17] and [PERSON] et al. [18] recently showed how V2X protocols can improve the safety of VRUs like pedestrians and cyclists through better collision prediction models. [PERSON] et al. [19] explore integrating ML in V2X environments for better collision avoidance, while [PERSON] et al. [20] emphasize using DL techniques to predict traffic conditions and improve V2X network effectiveness in accident prevention [21]. Recent advancements in cellular V2X (C-V2X) technology have further expanded V2X capabilities with faster data transmission and wider network coverage. [PERSON] et al. [22] demonstrated how C-V2X can support more sophisticated safety applications like cooperative perception and coordinated maneuvering, important for preventing accidents in complex traffic scenarios. Their study emphasizes the importance of low latency in enabling vehicles to share real-time information about road conditions, traffic lights, and nearby pedestrians, which all reduce the risk of accidents. VRDeepSafety integrates V2X communication protocols into its simulation platform. By simulating accident scenarios that involve V2V and V2I communication, VRDeepSafety provides a comprehensive dataset to evaluate V2X-based safety applications. This dataset includes detailed information on vehicle movements, traffic light timings, and environmental conditions, allowing researchers to assess how different V2X protocols and message types--such as Basic Safety Messages (BSMs), Cooperative Awareness Messages (CAMs), and Decentralized Environmental Notification Messages (DENMs)--affect accident prediction and prevention. Table 1 shows the limitations of current AD datasets for evaluating accident prediction and safety, especially in V2X contexts. While datasets like KITTI [23], nuScenes [24], and Waymo [25] have been crucial for advancing single-vehicle perception, their limitations make them unsuitable for addressing the challenges of V2X-enabled autonomous systems.