Improving Intelligent Vehicle Control with a Prediction Model of Passenger Comfort Based on Postural Instability Parameters.

With the development of technology, comfort has gradually developed into the main criterion for evaluating intelligent vehicle performances. In this study, a field test was carried out under five common driving conditions, and 60 participants took part. Passenger posture data, vehicle motion data and passenger subjective comfort data were collected. A paired sample T-test and a ridge regression algorithm were used to explore the relationship between passenger posture swing parameters and subjective comfort. The results show that under the same driving conditions, the speed of posture swing was significantly higher for passengers who experienced discomfort. Furthermore, we found that the change in angular velocity was the main cause of passenger discomfort under different driving conditions. This suggested that the design of intelligent vehicle algorithms should focus on the angular velocity variation among passengers. Finally, based on traditional machine learning algorithms and deep learning algorithms, this paper establishes two models for predicting comfort through passenger posture instability. The accuracy of the machine learning model in predicting passenger comfort was 87.1%, while that for the deep learning model was 89%. The findings are useful in providing a theoretical basis for improving the comfort of intelligent vehicles.