Are happy drivers safer drivers? Evidence from hazard response times and eye tracking data

Zimasa, Tatjana; Jamson, Samantha; Henson, Brian

doi:10.1016/j.trf.2016.12.005

Cited by 40 publications

(19 citation statements)

References 48 publications

(63 reference statements)

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“…The emotion factor of the seafarers in watchkeeping is relevant to working space conditions, inaccessible information sources, and communication. Although there are some studies focused on the road or railway (Lucidi et al, 2010, Read et al, 2012, Morales et al, 2017, Scott-Parker, 2017, Zimasa et al, 2017 emotional factors and human errors quantification, relatively rare researchers study this in maritime operations. In order to identify the negative emotions, Liu and Sourina (2014) started to use an EEG (Electroencephalogram) system in bridge simulators to monitor officers' workload and pressure.…”

Section: Seafarers' Emotion Identificationmentioning

confidence: 99%

Effects of seafarers’ emotion on human performance using bridge simulation

et al. 2018

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75-96% of maritime accidents are caused by human and organisational factors. Seafarers' emotion may degrade the effectivity of human behaviour when tasks in onboard environment are complex and demanding. This study was concerned with the relationship between seafarers' emotion and occurring events in navigation. The Electroencephalogram (EEG) and Self-Assessment Manikin (SAM) scale rating are used to investigate the occurrence and impact of seafarers' emotions on their performance using a bridge simulator. The study was conducted and described in two sections: emotion calibration and test recognition. In the first section, two types of emotions are induced by the sound clips of the International Affective Digitized Sounds (IADS), developed by the National Institute of Mental Health Center for the Study of Human Emotions. In the second section, emotion is recognised by the Support Vector Machine (SVM) classifier, as well as self-rated after the crew-qualified test in a bridge simulator. The results indicate that SVM can identify the emotions by EEG feature extraction, with an accuracy of 77.55%. The results concerning officers' emotion in a bridge simulator test reveal that seafarers' emotion in maritime operations, relating to events exposure, affects their behaviour and decision-making. In addition, negative emotion has a higher likelihood of contributing to human errors than positive emotion. Less negative emotion is the most dangerous emotion state during navigation, followed by extreme positive emotion.

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Section: Seafarers' Emotion Identificationmentioning

confidence: 99%

Effects of seafarers’ emotion on human performance using bridge simulation

et al. 2018

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“…Affective computing in the car, i.e., recognizing and reacting upon the driver's emotional state, is a topic of interest: Both negative [5][6][7] but also exuberantly positive emotional states [8] have been identified to strongly impact driving performance and to support unsafe driving. This topic has been identified as an upcoming research area already more than a decade ago [5] but compared to other areas of emotion-related sciences or HCI, the automotive sector rather neglected this topic in the past.…”

Section: Introductionmentioning

confidence: 99%

A Survey to Understand Emotional Situations on the Road and What They Mean for Affective Automotive UIs

Braun

Pfleging

Alt

2018

MTI

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In this paper, we present the results of an online survey (N = 170) on emotional situations on the road. In particular, we asked potential early adopters to remember a situation where they felt either an intense positive or negative emotion while driving. Our research is motivated by imminent disruptions in the automotive sector due to automated driving and the accompanying switch to selling driving experiences over horsepower. This creates a need to focus on the driver’s emotion when designing in-car interfaces. As a result of our research, we present a set of propositions for affective car interfaces based on real-life experiences. With our work we aim to support the design of affective car interfaces and give designers a foundation to build upon. We find respondents often connect positive emotions with enjoying their independence, while negative experiences are associated mostly with traffic behavior. Participants who experienced negative situations wished for better information management and a higher degree of automation. Drivers with positive emotions generally wanted to experience the situation more genuinely, for example, by switching to a “back-to-basic” mode. We explore these statements and discuss recommendations for the design of affective interfaces in future cars.

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“…Other researchers have noticed that their stimuli can produce different effects in participants depending on the nature of the hazards. Zimasa, Jamson and Henson (2017) found that their clips containing vulnerable road users elicited quicker response times than those involving cars. This distinction appears particularly relevant when comparing typical drivers to individuals on the autistic spectrum, with the latter showing reduced sensitivity for such 'social' hazards (Bishop-Johnson, Biasini, and Stavrinos, 2017;Sheppard, Ropar, Underwood, and van Loon, 2010).…”

Section: General Introductionmentioning

confidence: 97%

The hazard prediction test: A comparison of free-response and multiple-choice formats

Ventsislavova

2018

Safety Science

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Hazard perception skill is often related to lower crash risk, and the hazard perception test has been widely employed to measure this ability in drivers. An increasingly popular test-variant is the hazard prediction test: driving videos are occluded immediately prior to a hazard and participants are asked to predict how the situation will develop. Early versions of this test asked participants to provide a free-response answer which was subsequently coded. Later versions, however, have used a multiple-choice format where participants are provided with four options presented on screen. While the benefits of a multiple-choice format are obvious in terms of providing immediate feedback without relying on subjective coding, it is unclear whether this change in format affects the discriminative validity of the test. For the current study, a free-response test and a multiple-choice test were created using the same video clips. The free-response test (experiment 1) was found to successfully discriminate between novice and experienced drivers, with the latter predicting more hazards correctly. The answers provided by participants in Experiment 1 were then used to generate the options for a multiple-choice test (experiment 2). This second test was also found to discriminate between novice and experienced drivers, and a comparison between the two tests failed to reveal an advantage for one over the other. Despite this, correlations between prediction accuracy and both years of post-license driving, and annual mileage, were only significant for the multiple-choice test. The results suggest that the multiple-choice format is not only time-and cost-efficient, but is ostensibly as good as the free-response test in discriminating between driver groups.

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Are happy drivers safer drivers? Evidence from hazard response times and eye tracking data

Cited by 40 publications

References 48 publications

Effects of seafarers’ emotion on human performance using bridge simulation

Effects of seafarers’ emotion on human performance using bridge simulation

A Survey to Understand Emotional Situations on the Road and What They Mean for Affective Automotive UIs

The hazard prediction test: A comparison of free-response and multiple-choice formats

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