Radar-Based Road Surface Classification System for Personal Mobility Devices

As personal mobility devices (PMDs) have been widely adopted, the accident rate also increased. For safe driving, the speed of the PMDs should be controlled by recognizing the condition of the road surface. In recent advances in artificial intelligence, various studies have been conducted to recognize the type of material based on radar sensors and machine learning. However, there are very limited studies addressing radar-based material recognition in noisy radar signal environments such as PMDs. In this article, we proposed a road surface classification scheme based on the 60-GHz pulsed radar, which has low cost and low power usage to be applied to various PMDs. We also developed a lightweight deep neural network (DNN) model to recognize the type of road surface from the radar data after removing unnecessary features using principal component analysis (PCA). The proposed mechanism is robust against ambient noise and can recognize surface types on roads of variable shapes with high accuracy and low computational complexity. The results of experiments showed that the proposed lightweight surface classification system improved accuracy with only about 40% of the parameters used in the previous study.