Open AccessTransition characteristics of driver's intentions triggered by emotional evolution in two‐lane urban roads
Author(s) -
Guo Yongqing,
Wang Xiaoyuan,
Yuan Quan,
Liu Shanliang,
Liu Shijie
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2020.0037
Subject(s) - hidden markov model , driving simulator , advanced driver assistance systems , transition (genetics) , reliability (semiconductor) , action (physics) , road traffic safety , computer science , human–computer interaction , engineering , simulation , transport engineering , road traffic , artificial intelligence , biochemistry , chemistry , power (physics) , physics , quantum mechanics , gene
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.