Effects of emotions on driving behavior

Steinhauser, Klara; Leist, Felix; Maier, Kathrin; Michel, Vera; Pärsch, Nikolai; Rigley, Philip; Wurm, Franz; Steinhauser, Marco

doi:10.1016/j.trf.2018.08.012

Cited by 75 publications

(29 citation statements)

References 32 publications

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“…Similar to the aforementioned personality research, higher levels of self-esteem and sensation seeking were also related with higher levels of risk-taking in the simulated driving. Steinhauser et al (2018) investigated how positive and negative emotions impact driving behaviour, and which of these effects is related to emotional effects on attention.…”

Section: Emotion and Drivingmentioning

confidence: 99%

Factors underpinning unsafe driving: A systematic literature review of car drivers

Bowen

Budden

Smith

2020

Transportation Research Part F: Traffic Psychology and Behaviou

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Section: Emotion and Drivingmentioning

confidence: 99%

Factors underpinning unsafe driving: A systematic literature review of car drivers

Bowen

Budden

Smith

2020

Transportation Research Part F: Traffic Psychology and Behaviou

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“…Interestingly, using another emotion induction method, Pêcher et al (2009) asked drivers to listen to music and found that happy music (positive valence) resulted in an unexpected large decrement of speed and a deteriorated lateral control in comparison with sad music (negative valence). The reason for the inconsistent findings could be due to the differences in participants' emotion induction methods (Steinhauser et al, 2018) and participants' base emotions and personal experience.…”

Section: Emotions In Manual and Automated Drivingmentioning

confidence: 99%

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Zhou

Pulver

et al. 2020

SSRN Journal

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In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers' takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers' takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not.

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“…Du et al [24] studied manual driving and the driver's emotions in situations by measuring the time to change vehicle control according to the driver's emotions in semiautonomous driving. A study on the effect of high and low driver emotional states in terms of the control performance for manual driving was also conducted [25,26]. When the driver's emotions were positive, the concentration was high while driving, but the reaction speed was slow.…”

Section: Related Workmentioning

confidence: 99%

Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

2021

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Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver's situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver's situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is confirmed by classifying four driving scenarios and controlling the speed of the vehicle. In the experiment, by using a driving simulator and hardware-in-the-loop simulation equipment, we confirm that the response speed of the driving assistance system is 351.75 ms and the system recognizes four scenarios and eight emotions through bio-signal data.

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Effects of emotions on driving behavior

Cited by 75 publications

References 32 publications

Factors underpinning unsafe driving: A systematic literature review of car drivers

Factors underpinning unsafe driving: A systematic literature review of car drivers

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

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