L-RadSet: A Long-range Multimodal Dataset with 4D Radar for Autonomous Driving and Its Application

A long-range and robust perception system plays a crucial role in advancing research and deployment of autonomous driving. 4D radar, as an emerging range sensor, offers superior resilience to adverse weather conditions than lidar and provides elevation measurement compared to 3D radar. Existing 4D radar datasets, emphasizing robust and multimodal perception, typically combine camera, lidar, and 4D radar. However, they often lack long-range capability due to limited annotations. Furthermore, the configuration of their single shortfocus camera fails to effectively match a long-range 4D radar. To overcome these limitations, we present a novel long-range multimodal dataset. It encompasses high-resolution and longrange sensors, including forward-facing cameras, a 360° lidar, and a front-mounted 4D radar, along with detailed annotations for 3D objects across 11.2K key frames in different scenarios and weather conditions. Particularly, our dataset introduces, for the first time, three cameras with different focal lengths, enabling simultaneous capture of images with varying perception ranges. It serves as a valuable resource for developing accurate longrange perception algorithms. Remarkably, our dataset achieves the longest annotation ranges among comparable 4D radar datasets, spanning up to 220 meters. It supports applications such as 3D object detection and tracking, as well as facilitates the study of multimodal tasks. Through rigorous experiments, we validate the efficacy of our dataset and offer valuable insights into long-range 3D object detection.