## 5 Results and Discussion
### Practical Implications for Real-World Deployment  
The successful implementation of VRFormer and V2X communication has practical implications for real-world deployment, demonstrating how such systems can perform  
Figure 13: Trade-off between prediction horizon and accuracy for VRFormer.  
in everyday driving situations in difficult weather or heavy traffic. In clear weather, the VRFormer demonstrated a high level of reliability. All the sensors provided the most accurate data, enabling high accuracy in motion prediction, accident detection, and 3D object recognition. V2X communications increase overall performance through real-time updates from vehicles and infrastructure, thereby increasing situational awareness.  
In Figure 14, a comparison of APA demonstrates that VRFormer, integrated with V2X communication, significantly outperforms a single-vehicle model (85% \(\pm\) 3% vs. \(70\%\pm 5\%\)), achieving a 15% increase in APA. This illustrates the effectiveness of V2X integration on the improved VRFormer model.  
Complex real-world driving involves challenges, such as heavy traffic, intersections, and pedestrian zones. VRFormer's multi-agent approach and V2X capabilities are suitable for these environments. In urban or highway traffic, VRFormer's V2X capabilities improve its ability to predict vehicle and pedestrian movements and support safer navigation, particularly in crowded intersections and when overtaking. Figure 15 illustrates VRFormer's enhanced pedestrian detection using V2X communication.  
Figure 14: Accident prediction accuracy comparison.  
Figure 15: V2X-enhanced pedestrian detection in VRDeepSafety using VRFormer.  
Figure 15 demonstrates how our novel VR simulation platform, VRDeepSafety, utilizes V2X technology to enhance pedestrian detection. The top row provides VRDeepSafety simulation views of a scenario where a pedestrian is occluded by a truck from the ego vehicle's perspective (left). An elevated infrastructure camera, however, clearly detects the pedestrian (center). The rightmost image shows an example of V2X detection between vehicles. Simplified diagrams below illustrate the process: (left) VRFormer's initial limited view due to occlusion; (center) the infrastructure camera detecting and broadcasting the pedestrian's location via V2X within VRDeepSafety; and (right) VRFormer processing the V2X data to gain awareness of the occluded pedestrian, demonstrating enhanced perception. This integration showcases how V2X in VRDeepSafety overcomes single-vehicle sensor limitations.  
Figure 16 shows the impact of (a) pose noise (meters) and (b) communication latency (seconds) on VRFormer's APA. Pose noise was simulated using a Gaussian distribution (standard deviation 0.02 m). VRFormer's performance was evaluated in a single-vehicle scenario (no V2X) and three multi-agent V2X scenarios (three, four, and four vehicles + infrastructure). Increasing noise or latency decreases APA. However, V2X consistently improves VRFormer's robustness (higher APA). Adding infrastructure to the four-vehicle scenario consistently yields the best results. Especially, at 0.1 m pose noise, VRFormer with four vehicles + infrastructure achieves 43.6% APA--1.5% and 3% better than three vehicles and the single-vehicle baseline, respectively. VRFormer exhibits similar APA gains at 0.1 s latency (approximately 1.1%, 1.8%, and 2.5% improvement for the three-vehicle, four-vehicle, and four-vehicle + infrastructure scenarios, respectively, compared to the single-vehicle baseline). This increased robustness, however, comes at the cost of increased latency. Compared to the single-vehicle case, the three-vehicle, four-vehicle, and four-vehicle + infrastructure scenarios add approximately 0.05, 0.1, and 0.15 s of latency, respectively, at 0.1 m pose noise. Therefore, balancing robustness, accuracy, and responsiveness is critical when designing V2X systems with deep learning.