Driver's intention is a self‐internal state that represents a commitment to carrying out driving action at the next moment, which could be affected by driver's emotion. Therefore, understanding driver's emotion is an important basis for developing driver intention recognition models. This study aims to gain a better insight of the characteristics of driver intention transition trigged by driver's emotion. The Hidden Markov model was used to develop a driver intention recognition model with the involvement of driver's emotions. Assorted materials including visual, auditory and olfactory stimuli were used to evoke driver's emotions before the driving experiments, as well as keep and increase the emotional level during driving. Real and virtual driving experiments were conducted to collect human‐vehicle‐environment dynamic data in two‐lane roads. The results show that the proposed model can achieve high accuracy and reliability in estimating driver's intention transition with the evolution of driver emotion. Our findings of this study can be used to develop the personalized driving warning system and intelligent human‐machine interaction in vehicles. This study would be of great theoretical significance for improving road traffic safety.
Transition characteristics of driver's intentions triggered by emotional evolution in two‐lane urban roads
IET Intelligent Transport Systems ; 14 , 13 ; 1788-1798
2020-12-01
11 pages
Article (Journal)
Electronic Resource
English
hidden Markov models , two‐lane urban roads , hidden Markov model , olfactory stimuli , driver information systems , driver intention transition , road safety , transition characteristics , behavioural sciences computing , visual stimuli , human‐vehicle‐environment dynamic data , road traffic , auditory stimuli , intelligent human‐machine interaction , driver emotion , road accidents , emotional evolution , road traffic safety , personalized driving warning system , road vehicles , driver intention recognition model , virtual driving experiments
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