Near-Sensor LiDAR and Visual Feature Extraction and Communication for Low-Latency Roadside Cooperative Perception

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.