## 2 Literature Review  
Recent advancements in V2X communication and artificial intelligence (AI) have significantly propelled AD. VR simulation models are now essential for testing and validating AV systems in diverse and complex scenarios. This section reviews relevant research and positions the contribution of VRDeepSafety within this evolving context. VR has emerged as a powerful tool for simulating complex and diverse driving scenarios, enabling the testing and refinement of AD algorithms in a controlled and scalable environment. VR environments, as demonstrated by [PERSON] et al. [10], allow for high-fidelity traffic simulations that replicate multi-vehicle interactions, pedestrian behaviors, and environmental factors, all of which are essential for evaluating AV safety systems. In particular, VR simulations allow for testing AD systems in high-risk scenarios, such as intersections, where the probability of accidents is higher due to the convergence of multiple vehicles and road users. The ability to simulate these scenarios at scale is a significant advantage of VR, allowing researchers to model numerous traffic scenarios, including rare but high-potential accident scenarios. [PERSON] et al. [11] described using immersive VR simulations to train AD algorithms in edge cases, such as near collisions, sudden lane changes, and pedestrian jaywalking. By providing a realistic yet controlled testing environment, VR simulations help develop more resilient and adaptive AV systems. VRDeepSafety builds upon these foundational VR simulation models by incorporating multiple data sources, including cameras, Light Detection and Ranging (LiDAR), and radar, to create a complete and accurate test for AD. Unlike traditional models using visual data alone, VRDeepSafety integrates multiple data streams to simulate the full spectrum of sensory inputs that an AV would perceive, enhancing the realism and accuracy of accident prediction algorithms with a richer dataset for training and validation. The integration of V2V and V2I communication in the simulation environment makes VRDeepSafety unique, enabling the testing of autonomous systems in a fully connected vehicular environment (see Table 1), addressing a significant limitation in current datasets. Accident prediction is a key aspect of AD, as it allows the vehicle to anticipate potential hazards and take action before an accident occurs. In recent years, there has been a significant shift towards using DL to enhance the accuracy and speed of accident prediction frameworks. DL models, especially those based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can process large amounts of high-dimensional data in real time, enabling them to identify patterns and make predictions more accurately than traditional rule-based systems. This is exemplified by the study of [PERSON] et al. [12], who developed a DL framework that uses V2X data to predict accidents in urban traffic. V2X data integration has transformed accident prediction by allowing real-time information exchange between vehicles and surrounding infrastructure, improving the situational awareness of autonomous systems using context from traffic light status, road conditions, and nearby vehicles. [PERSON] et al. [13] developed a framework to use these data to predict vehicle trajectories, detect potential collisions, and warn the vehicle and surrounding road users, finding that it enhanced accident prediction accuracy and reduced false positives, a common problem in models using only sensor data. [PERSON] et al. [14] proposed a hybrid accident prediction model combining the strengths of CNNs and RNNs. Their model processes sequential traffic data from multiple sources--including LiDAR, radar, and V2X communication--analyzing temporal relationships between different data points (e.g., vehicle speed, inter-vehicle distance, and traffic signal timing) to proactively predict hazards, enabling informed decisions, such as slowing down or changing lanes to avoid accidents.