## 2 Literature Review
The KITTI dataset, while valuable, is limited by its focus on urban driving scenarios and its comparatively small size (15,000 annotated samples), making it inadequate for capturing the full range of real-world driving complexities. VRDeepSafety overcomes this by simulating urban and rural scenarios. Similarly, nuScenes is restricted by its geographic focus on Boston and Singapore, its lower temporal resolution of 2 Hz, and its limited number of driving scenes, restricting its applicability to broader scenarios. VRDeepSafety addresses this limitation by providing a diverse range of environmental and geographical settings, using a high temporal resolution of 10 Hz. The Waymo Open Dataset, while large, has limited weather conditions and geographic locations, focusing primarily on clear skies and daylight in specific US cities. This lack of diverse environmental conditions and geographic coverage means that models trained on such datasets may perform poorly in other contexts. VRDeepSafety addresses these issues by simulating a variety of environmental factors, such as rain, snow, fog, and diverse illumination conditions. Furthermore, these datasets lack integrated V2X communication, making them unsuitable for testing advanced multi-agent  
autonomous systems that rely on shared information. VRDeepSafety fully integrates V2X, modeling real-world constraints and behaviors. Moreover, datasets focusing on collaborative perception, such as OPV2V [26], V2X-Sim [27], and V2X-seq [28], also have limitations. The OPV2V dataset is fully synthetic, using CARLA and SUMO simulators, thus lacking real-world complexity. VRDeepSafety addresses this by leveraging a realistic VR simulation engine. The V2X-Sim dataset, also simulator-based, makes assumptions of a perfect V2X communication environment, which does not reflect real-world network challenges. VRDeepSafety solves this by modeling real-world signal loss, latency, and message prioritization. Similarly, V2X-seq focuses on idealized V2X scenarios, lacking real-world noise and sensor imprecision, which prevents robust testing for real-world deployments. VRDeepSafety simulates these conditions using realistic V2X protocol models to assess the impact of these challenges. In Table 1, \(\checkmark\) indicates the presence of a feature, and \(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\bmbm }}}}}}}}}}}}}}}\) indicates its absence. For example, \(\checkmark\) in \"LiDAR\" means the dataset contains LiDAR data, while \(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}}\) in \"V2X Data\" means it lacks V2X data. In contrast, VRDeepSafety offers a comprehensive and scalable platform for simulating large-scale accident scenarios, incorporating Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication with realistic signal loss, latency, and interference. This simulation-based dataset was developed to overcome the limitations of real-world data collection and existing simulated datasets by providing a rich environment for modeling complex multi-vehicle interactions, diverse weather conditions, and dynamic traffic behaviors crucial for testing advanced autonomous systems. As evidenced by Table 2, VRDeepSafety provides a significantly larger scale, with more annotated samples and V2X frames, enabling the training of robust, accurate, and generalizable accident prediction models vital for AD safety.