Personalized Driving Data-Based Bump Prediction Using a Cloud-Based Continuous Learning for Preview Electronically Controlled Suspension

An electronically controlled suspension with road preview enhances ride comfort and driving stability. For detecting road irregularities such as speed bump, look-ahead sensors such as camera and Light Detection And Ranging (LiDAR) sensors have been used. However, these preview systems perform poorly in night and adverse weather conditions. To overcome these limitations, this paper proposes a novel machine learning-based speed bump prediction system that predicts remaining distance to and height of speed bumps using in-vehicle data, which is robust to external environment and represent driving pattern of driver. The proposed system consists of three components: machine learning-based bump predictor, automatic training dataset generator, and cloud-based continuous learning system. The effectiveness of the proposed system was confirmed through tests with varied datasets in different drivers and locations. The prediction of the distance to the speed bump demonstrated a Root Mean Square Error (RMSE) of 6.37 meters, while the height prediction showed an RMSE of 0.9 cm. Additionally, after applying cloud-based continuous learning for driver-specific personalization, the RMSE for the distance prediction improved to 5.96 meters. Furthermore, experiments conducted under challenging conditions showed that our system outperformed existing systems.