Artificial Intelligence for Perception Using 4D Radar

Autonomous vehicles (AVs) rely on accurate perception systems to interpret their surroundings and make real-time driving decisions. While cameras and lidars have been primarily used for perception, they are vulnerable to adverse weather conditions. In contrast, radar that uses microwave signals shows very robust performance under challenging weather conditions. Recently commercialized, 4D radar simultaneously delivers range, azimuth, elevation, and Doppler measurements of surrounding objects, proving that it can be a useful alternative to lidar and a strong complement to camera sensors. This tutorial article presents a comprehensive overview of 4D radar for AVs, from data processing to artificial intelligence (AI)-driven techniques, such as object detection, drivable area detection, odometry, and sensor fusion. In addition, we survey publicly available 4D radar datasets and analyze current research trends, offering insights into how AI techniques can enhance the use of emerging 4D radar technology. To facilitate practical learning, we provide hands-on materials, including code implementations and example datasets, at https://github.com/kaist-avelab/radar-tutorial. As a result, this article provides researchers and practitioners with a clear understanding of the unique advantages of 4D radar and the state-of-the-art methods being developed to harness its capabilities for safer and more reliable autonomous driving.