Abstract
Joining worldwide efforts to understand the relationship between driving emotion and behavior, the current study aimed at examining the influence of emotions on driving intention transition. In Study 1, taking a car-following scene as an example, we designed the driving experiments to obtain the driving data in drivers’ natural states, and a driving intention prediction model was constructed based on the HMM. Then, we analyzed the probability distribution and transition probability of driving intentions. In Study 2, we designed a series of emotion-induction experiments for eight typical driving emotions, and the drivers with induced emotion participated in the driving experiments similar to Study 1. Then, we obtained the driving data of the drivers in eight typical emotional states, and the driving intention prediction models adapted to the driver’s different emotional states were constructed based on the HMM severally. Finally, we analyzed the probabilistic differences of driving intention in divers’ natural states and different emotional states, and the findings showed the changing law of driving intention probability distribution and transfer probability caused by emotion evolution. The findings of this study can promote the development of driving behavior prediction technology and an active safety early warning system.
Highlights
Traffic safety continues to come into focus with the ever-increasing use of cars in modern society, and traffic accidents are becoming a major factor causing human injuries [1]
This study focused on the influence of driver’s different emotional states on the generation and transition of driving intention states
By controlling a single variable, we obtained the generation probability of driving intention under the condition that the emotional state changed while the driving environment parameters remained
Summary
Traffic safety continues to come into focus with the ever-increasing use of cars in modern society, and traffic accidents are becoming a major factor causing human injuries [1]. In car driving activities, human drivers have inherent limitations in perception, decision-making, and behavior execution. Even experienced drivers may ignore important information in the driving environment and make wrong judgments about traffic safety situation, and adopt inappropriate or unsafe driving behaviors [5]. It is very important to monitor and predict driving behavior through the on-board intelligence system, and to evaluate whether the driving behavior can keep the car in a safe state in a specific environment [6]. Many researchers in the field of automotive active safety have begun to build driving behavior identification and prediction models [7,8,9].
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