Personalized Adaptive Cruise Control Based on Passenger's Subjective Risk Evaluation and Model Predictive Control

In this study, a personalized adaptive cruise control system founded on driving risk field (DRF) is proposed, which aims to make the control system more consistent with the driver's driving habits and reduce discomfort. Firstly, founded on the collected driver's operation data, the common characteristics of different drivers' operation behavior under different conditions are analyzed, and the spatial-temporal coupling DRF model under the event of car-following is constructed. Secondly, the data processing and clustering analysis of the High D natural driving data set are carried out to obtain the individual driving preferences of aggressive, normal and conservative drivers, and the DRF is calibrated based on the clustering results. Finally, based on the calibrated DRF, an intelligent vehicle adaptive cruise controller with minimum driving risk is designed, and compared with the traditional optimal velocity model (OVM), the advantages of the constructed DRF in personalized cruise following are verified.