Real-time prediction of road adhesion conditions ahead of the vehicle is critical for emergency braking, stability control, and trajectory planning in advanced driver-assistance systems (ADAS). To overcome the limitations of conventional vehicle-dynamics-based methods, which mainly rely on tire–road contact responses and lack preview perception capability, this article proposes a real-time road adhesion prediction method based on onboard lidar point clouds. First, point cloud data were collected from four typical road surface conditions, including dry asphalt, wet asphalt, dry concrete, and wet concrete. Random sample consensus (RANSAC)-based ground segmentation and reflectivity filtering were then applied to remove nonroad points and highly reflective outliers. Subsequently, a speed-adaptive dynamic region of interest (ROI) was constructed by incorporating a vehicle safety distance model, enabling preview perception of the target road surface ahead. On this basis, the processed point clouds were converted into multichannel pseudoimages, and a convolutional neural network (CNN)-vision transformer classification model was developed to enhance local reflectance texture feature extraction and global context modeling. Finally, based on the empirical mapping relationship between road surface type and friction coefficient, the road surface recognition results were converted into road adhesion prediction results. Experimental results show that the proposed method achieves a better tradeoff between recognition accuracy and real-time performance. Compared with conventional vehicle-dynamics-based estimation methods, the proposed method significantly reduces the mean absolute error and mean absolute percentage error in both homogeneous road and transition road scenarios, verifying its effectiveness for real-time forward road adhesion prediction in ADAS.
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.
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