An Experimental Study on Physiological Parameters Toward Driver Emotion Recognition

Leng, Hongjie; Lin, Yingzi; Zanzi, L. A.

doi:10.1007/978-3-540-73333-1_30

Cited by 34 publications

(13 citation statements)

References 16 publications

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“…Compared with the control scenarios, the participants' BVP, SC, %, and % appear to have significant difference in the anger induced experiments. A similar pattern has also been demonstrated in the part of introduction, drivers' heart rate would be faster and the %, % would be significantly different when they were in a negative emotion [28,29]. So the evidences from the participants' physiological and brain signals also indicate the scenarios of encountering the adjacent vehicle interfere, red light frequently, and traffic congestion easily lead to the driver becoming angry during driving.…”

Section: Discussionsupporting

confidence: 64%

The Induction and Detection Method of Angry Driving: Evidences from EEG and Physiological Signals

Yan

Park

Qin

et al. 2018

Discrete Dynamics in Nature and Society

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Introduction. Angry driving has been a significant road safety issue worldwide. This study focuses on the problem of inducing and detecting driving anger based on the simulation and on-road experiments. Methods. First, three typical scenarios (including waiting for the red light frequently, traffic congestion, and the surrounding vehicle interference) which could cause driving anger were developed and applied in a driving simulator experimental study. The self-reported, biosignals, and brain signals of driving anger data were collected from the driving anger induction experiment. Second, in order to examine the difference of driving anger between simulation driving and real-life driving, 22 groups of on-road experiments were conducted. The typical scenes and self-reported data were recorded to distinguish normal driving from angry driving. Finally, a Hidden Naïve Bayes classifier was employed to detect angry driving during the on-road driving according to the four features (namely, BVP, SC, %, and %) from driver's biosignals and brain signals. Results. The evaluation of emotional differentiation degrees and emotional intensity indicates that the developed scenarios based on virtual reality were useful and effective in inducing driving anger. Meanwhile, the proposed angry driving detection approach achieves an accuracy of 85.0%. Conclusions and Applications. Due to possible crash and injury from the on-road experiments, the proposed approach of driving anger induction using a driving simulator is effective in exploring the causal relationship between angry driving, unsafe driving behavior, and traffic accident. In addition, angry driving detection approach can provide theoretical foundation for the development of driving anger warning products.

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Section: Discussionsupporting

confidence: 64%

The Induction and Detection Method of Angry Driving: Evidences from EEG and Physiological Signals

Yan

Park

Qin

et al. 2018

Discrete Dynamics in Nature and Society

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“…To list a few, some people have a poker face (Picard 1997). Because some affective states share similar facial expressions (e.g., anger and disgust, fear and surprise), they are commonly confusing (Leng, Lin, and Zanzi 2007). Moreover, detecting facial expressions at night may not work well with a webcam (without additional light or specifications that, for example, an infrared camera might offer).…”

Section: Affect Detection Methods and Modalitiesmentioning

confidence: 99%

Towards affect-integrated driving behaviour research

Jeon

2015

Theoretical Issues in Ergonomics Science

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This paper aims to systematically explore the relationship between affect, cognition, and driving behaviours. Whereas some driving models partially address drivers' affective factors, the majority of them have focused on cognitive aspects. Thus, it would be helpful to construct an affect-integrated driving behaviour research model. First, I briefly assessed the current driving models and identified critical cognitive elements commonly found among those models. Next, the mechanisms of affective effects on those cognitive processes were described. Then, I showed the effects of affective states on driving performance. Finally, a conceptual model for affect-related driving behaviour research was proposed and followed by discussions from the theoretical and practical perspectives. This review helps researchers construct a more naturalistic and generic driving behaviour model, conceptualise affect detection research with a clearer idea of what affect needs to be detected and why, and develop an affect regulation model and adaptive system for multiple drivers. Relevance to human factors/ergonomics theoryThe present paper reviews affect-related driving research and proposes an affectintegrated driving model based on theoretical and practical evidence. This paper will contribute to conduct more comprehensive driving behaviour research and design an adaptive in-vehicle affect detection and mitigation system.

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“…Campagne et al [3] studied the relationship of driver's age difference to traffic accidence numbers using EEG recorder, and the result showed that driver's vigilance would decrease and the number of operation mistakes would grow as the age increases. Brundell-Freij and Ericsson [4], Leng et al [5], Pêcher [6], Di Stasi [7], Taubman-Ben-Ari [8], Cai and Lin [9], Parker et al [10], Trick et al [11], Lansdown [12], Daffy [13], Wang et al [14][15][16], Weng and Meng [17], Verschuur and Hurts [18], Zhang et al [19], Gosselin et al [20], and Hu et al [21], Danciu et al [22] have studied driver behavior characteristics from different angles. However, in the field of vehicle active safety, there are few researches on driver's affective characteristics from microcosmic and dynamic perspective.…”

Section: Forewordmentioning

confidence: 99%

Prediction Method of Driver's Propensity Adapted to Driver's Dynamic Feature Extraction of Affection

Zhang

Wang

Ban

et al. 2013

Advances in Mechanical Engineering

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Driver's propensity is a dynamic measurement of driver's characteristics, such as affection and preference. In the vehicle driverassistance system, especially its collision warning subsystem, it is also an important parameter of computing driver's intention. The prediction of driver's propensity from relative static and macroscopic perspective is an essential precondition for further researching and extracting dynamic characteristics. Physiology and psychology tests are designed to measure driver's character and calculate physiological rhythm. Changing data of driver's psychology and emotion during driving are obtained by real vehicle test. Then driver's propensity values of different types are calculated by weighting method according to the contribution rate of standard features. Results show that this method is better than the traditional psychology test, and it provides a basis for further studying dynamic characteristics of driver's affection.

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An Experimental Study on Physiological Parameters Toward Driver Emotion Recognition

Cited by 34 publications

References 16 publications

The Induction and Detection Method of Angry Driving: Evidences from EEG and Physiological Signals

The Induction and Detection Method of Angry Driving: Evidences from EEG and Physiological Signals

Towards affect-integrated driving behaviour research

Prediction Method of Driver's Propensity Adapted to Driver's Dynamic Feature Extraction of Affection

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