###### Abstract  
Safe real-world navigation for autonomous vehicles (AVs) requires robust perception and decision-making, especially in complex, multi-agent scenarios. Existing AV datasets are limited by their inability to capture diverse V2X communication scenarios, lack of synchronized multi-sensor data, and insufficient coverage of critical edge cases in multi-vehicle interactions. This paper introduces VRDeepSafety, a novel and scalable VR simulation platform that overcomes these limitations by integrating Vehicle-to-Everything (V2X) communication, including realistic latency, packet loss, and signal prioritization, to enhance AV accident prediction and mitigation. VRDeepSafety generates comprehensive datasets featuring synchronized multi-vehicle interactions, coordinated V2X scenarios, and diverse sensor data, including visual, LiDAR, radar, and V2X streams. Evaluated with our novel deep-learning model, VRFormer, which uniquely fuses VR sensor data with V2X using a probabilistic Bayesian inference, as well as a hierarchical Kalman and particle filter structure, VRDeepSafety achieved an 85% accident prediction accuracy (APA) at a 2 s horizon, a 17% increase in 3D object detection precision (mAP), and a 0.3 s reduction in response time, outperforming a single-vehicle baseline. Furthermore, V2X integration increased APA by 15%. Extending the prediction horizon to 3-4 s reduced APA to 70%, highlighting the trade-off between prediction time and accuracy. The VRDeepSafety high-fidelity simulation and integrated V2X provide a valuable and rigorous tool for developing safer and more responsive AVs. V2X communication; autonomous driving; accident prediction; motion prediction +
Footnote †: journal: J. 2025, 16, 82  
0000-0001-5002-488X/00/0000-0001-5002-488X/00/00000-0000-00000-0000-0000-0  
learning (ML) and predictive analytics enhances traffic safety by identifying potential collisions and optimizing traffic flow. While ML, including deep learning (DL), has improved collision prediction and analysis with V2X data [2; 3; 4; 5; 6; 7; 8; 9], these models often rely on datasets insufficient for robust real-world applications. Existing AD datasets, such as KITTI and nuScenes, while useful for perception tasks, are fundamentally limited in their capacity to model the complexities of multi-vehicle interactions and realistic V2X communication. Specifically, these datasets lack the ability to simulate scenarios where network latency, signal loss, and message prioritization significantly affect the performance of predictive algorithms, making them ill-equipped for the development or validation of real-world V2X-enabled autonomous driving systems. To address the need for more complex, scalable datasets that faithfully reproduce real-world communication scenarios, we introduce VRDeepSafety, a novel virtual reality (VR) simulation platform integrated with VRFormer, a novel DL model for accident prediction. While the platform's performance is evaluated using a fixed prediction horizon, we recognize that adapting the horizon dynamically based on driving topology is essential and will be explored further in future research. The presented results indicate that such a platform is critical for training accurate and safe AV control systems. Our approach provides a comprehensive solution to AD safety research by (1) simulating high-fidelity V2X using a custom middleware within Unity; (2) implementing dynamic sensor fusion and adaptation using a unique hierarchical approach; (3) providing a scalable framework with a flexible configuration for generating diverse accident scenarios; (4) using probabilistic data fusion with Bayesian inference for resilient environmental understanding; and (5) introducing VRFormer, optimized for accident prediction through a multi-modal convolutional neural network-long short-term memory (CNN-LSTM) architecture with real-time V2X inputs. This innovative combination of a realistic simulation environment and a predictive model makes it ideal for improving the safety of autonomous vehicles.