Mitigating In-Transit Vision Noise for Enhanced Vehicle Safety

Software-defined vehicles (SDVs) rely on cameras for intelligent and safety-critical applications but face challenges from dynamic environmental noise, including weather and occlusions. Unlike static sensors, SDV cameras encounter noise patterns influenced by driving speed, a factor often overlooked in prior research. To address this gap, we conduct a quantitative analysis of the in-transit noise impact using data from public datasets, the CARLA simulator, a robotic vehicle, and a real vehicle. Our findings suggest that maintaining a speed below 40km/h may serve as a threshold for ensuring reliable camera-based applications under noisy urban conditions. In addition, we propose TransitNet, a novel model designed to mitigate in-transit camera noise and enhance driving safety, particularly at higher speeds. Compared to multiple baselines, experimental results show that TransitNet improves the F-measure by 5.1%, mAP@50 by 3.6%, and increases FPS by 56.7% across all datasets. We also provide detailed observations and insights from extensive testing.