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access icon openaccess Transition characteristics of driver's intentions triggered by emotional evolution in two-lane urban roads

  • Author(s): Yongqing Guo 1, 2 ; Xiaoyuan Wang 2, 3 ; Quan Yuan 4 ; Shanliang Liu 3 ; Shijie Liu 1
    • View affiliations
  • Source: Volume 14, Issue 13, 15 December 2020, p. 1788 – 1798
    DOI:  10.1049/iet-its.2020.0037 , Print ISSN 1751-956X, Online ISSN 1751-9578
This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 30/01/2020, Accepted 09/10/2020, Revised 24/07/2020, Published 26/11/2020

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.

Inspec keywords: driver information systems; road traffic; behavioural sciences computing; hidden Markov models; road vehicles; road safety; road accidents

Other keywords: personalized driving warning system; olfactory stimuli; virtual driving experiments; human-vehicle-environment dynamic data; driver intention recognition model; driver intention transition; visual stimuli; road traffic safety; hidden Markov model; emotional evolution; intelligent human-machine interaction; two-lane urban roads; auditory stimuli; driver emotion; transition characteristics

Subjects: Markov processes; Social and behavioural sciences computing; Traffic engineering computing

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