Analysis of Real-World Driver's Frustration

Malta, Lucas; Miyajima, Chiyomi; Kitaoka, Norihide

doi:10.1109/tits.2010.2070839

Cited by 69 publications

(45 citation statements)

References 17 publications

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“…The Bayesian rule-based method excels in application. It can be naturally integrated into the stateof-the-art method based on Bayesian networks using hybrid measures [30].…”

Section: Discussionmentioning

confidence: 99%

Analysis of Temporal Relationships between Eye Gaze and Peripheral Vehicle Behavior for Detecting Driver Distraction

Hirayama

Mase

Takeda

2013

International Journal of Vehicular Technology

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A car driver's cognitive distraction is a main factor behind car accidents. One's state of mind is subconsciously exposed as a reaction reflecting it by external stimuli. A visual event that occurs in front of the driver when a peripheral vehicle overtakes the driver's vehicle is regarded as the external stimulus. We focus on temporal relationships between the driver's eye gaze and the peripheral vehicle behavior. The analysis result showed that the temporal relationships depend on the driver's state. In particular we confirmed that the timing of the gaze toward the stimulus under the distracted state induced by a music retrieval task using an automatic speech recognition system is later than that under a neutral state while only driving without the secondary cognitive task. This temporal feature can contribute to detecting the cognitive distraction automatically. A detector based on a Bayesian framework using this feature achieves better accuracy than one based on the percentage road center method.

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“…The Bayesian rule-based method excels in application. It can be naturally integrated into the stateof-the-art method based on Bayesian networks using hybrid measures [30].…”

Section: Discussionmentioning

confidence: 99%

Analysis of Temporal Relationships between Eye Gaze and Peripheral Vehicle Behavior for Detecting Driver Distraction

Hirayama

Mase

Takeda

2013

International Journal of Vehicular Technology

Self Cite

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“…The PMT-based messages were found to be more effective than jurisdiction-based messages in specific gender groups under specific conditions. Aggressive driving may be induced by drivers' frustration (Joint, 1995;Malta et al, 2011), and driving at high speeds may be affected by drivers' anger (Mesken et al, 2007;Roidl et al, 2013;Vallières et al, 2014).…”

Section: Emotional Arousalmentioning

confidence: 99%

Seemingly irrational driving behavior model: The effect of habit strength and anticipated affective reactions

Chung

2015

Accident Analysis & Prevention

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“…And there are studies using Bayes' Rule to infer drivers' behavior and innate emotional status (e.g. Malta et al, 2011). The application of rule-based approach in en-route diversion modeling is coarse except one research to model and recalibrate the if-then diversion rules based on fuzzy logics and weight factors (Paz and Peeta, 2009).…”

Section: Behavior Modelingmentioning

confidence: 99%

Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

Xiong

Chen

et al. 2016

Transportation Research Part C: Emerging Technologies

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Analysis of Real-World Driver's Frustration

Cited by 69 publications

References 17 publications

Analysis of Temporal Relationships between Eye Gaze and Peripheral Vehicle Behavior for Detecting Driver Distraction

Analysis of Temporal Relationships between Eye Gaze and Peripheral Vehicle Behavior for Detecting Driver Distraction

Seemingly irrational driving behavior model: The effect of habit strength and anticipated affective reactions

Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

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