Calibration-Free Roadside BEV Perception with V2X-Enabled Vehicle Position Assistance

Roadside bird's eye view (BEV) perception can enhance the comprehensive environmental awareness required for autonomous driving systems. Current approaches typically concentrate on BEV perception from the perspective of the vehicle, requiring precise camera calibration or depth estimation, leading to potential inaccuracies. We introduce a calibration-free roadside BEV perception architecture, which utilizes elevated roadside cameras in conjunction with the vehicle position transmitted via cellular vehicle-to-everything (C-V2X) independently of camera calibration parameters. To enhance robustness against practical issues such as V2X communication delay, packet loss, and positioning noise, we simulate real-world uncertainties by injecting random noise into the coordinate input and varying the proportion of vehicles providing location data. Experiments on the DAIR-V2X dataset demonstrate that the architecture achieves superior performance compared to calibration-based and calibration-free baselines, highlighting its effectiveness in roadside BEV perception.