Autonomous driving technologies are swiftly evolving, characterized by two main strategies: 1) single-vehicle autonomous driving (SVAD) and 2) vehicle-infrastructure cooperative autonomous driving (VICAD). SVAD depends entirely on the vehicle’s internal sensors and processing capabilities, whereas VICAD benefits from a synergistic network combining roadside infrastructure, connected vehicles, and cloud services to boost safety and efficiency. Nevertheless, VICAD encounters challenges with high-bandwidth data transmission and perception latency. To mitigate these concerns, we introduce an innovative intelligent roadside unit (I-RSU) platform integrating perception, computing, and communication into one cohesive system. The platform features dual neural processing units (NPUs) for the effective extraction of images and LiDAR features, alongside a Cellular-V2X (C-V2X) communication module, all realized on a field-programmable gate array (FPGA). This setup minimizes latency and expenses by enabling computation near the sensors and facilitating selective data transmission. Our system also supports multimodal fusion, enhancing overall perception and safety. Through extensive real-world trials and simulations, our system demonstrates a substantial reduction in end-to-end latency, providing a scalable solution for VICAD scenarios.

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.