Feature extraction and dynamic identification of driving intention adapting to multi-mode emotions

Abstract

Accurate identification of driving intention and reasonable control of driver’s behavior is seen as an important mean to reduce man-made traffic accidents for the intelligent vehicle. However, the intention identification processes associated with driving emotion-related impact have received very little attention. With the aim of uncovering the emotional impact on driving intention identification, the car-following condition was taken as an example, and multi-source and dynamic data of human–vehicle–environment under different driving emotional states were obtained through the experiments of emotions induction, actual driving, and virtual driving in this study. The feature extraction and dynamic identification models based on rough set theory and back-propagation artificial neural network were built to recognize driving intentions. The results showed that there were some differences in driving intention identification under different emotional modes. The differences were mainly manifested in the complexity and accuracy of intention feature vectors. The rationality and validity of the intentions feature extraction and identification models were verified by the actual driving experiments, virtual driving experiments, and interactive simulation experiments. The research results can provide theoretic basis for the emotional intelligence research of advanced vehicle, driving assist systems, and unmanned vehicles.