Differences in risk experience between sensation avoiders and sensation seekers

Heino, A.; Molen, H H Van Der; Wilde, Gerald J. S.

doi:10.1016/0191-8869(95)00152-v

Cited by 57 publications

(21 citation statements)

References 20 publications

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“…For lower speed there would seem to be an inverse relationship between SSSV and following distance-arguably the sensation seekers are seeking more excitement and risk by driving closer. In previous studies the SSSV scale has been shown to be correlated to self-reported risky driving behaviour (Jonah et al, 2001) but not to be a significant indicator of self-reported tailgating, a finding borne out for higher speeds in our analysis but not for lower, while Heino et al (1996) has demonstrated a clear difference (p=0.01) between sensation seekers and avoiders, driving with average headways of 1.2 and 1.8 sec respectively at 110 kph (36.1 vs 55.6 m), a finding not confirmed in our study and potentially at odds with those of Jonah (2001).…”

Section: Discussioncontrasting

confidence: 63%

Driver Psychological Types and Car Following: Is there a Correlation? Results of a Pilot Study

Brackstone

2005

Proceedings of the 2nd International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design: Driv

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Many studies have attempted to measure driver behaviour or to classify drivers' attributes according to questionnaires based on psychological indicators. Although such studies have met with success, for example, correlating behavioural types with accident risk, few attempts have been made to correlate these attributes with direct, dynamically measurable quantities such as desired following distance or its responsiveness to speed changes. In this paper we will examine whether such a correlation is possible by examining results from a pilot study using an instrumented vehicle and a group of eleven subjects. In particular we will focus on how following distances are correlated to the Sensation Seeking and Internality-Externality Scales BACKGROUND Two areas of work that have seen extensive examination in the last decade are the characterization of drivers according to a number of social/psychological measures, and the examination of the dynamic behaviour of drivers, for example the speed dependant following distance that drivers adopt behind each other while driving. Although many have speculated that 'aggressive' or 'passive' drivers will act differently in such circumstances, and indeed have formulated rule of thumb relationships, little work has been done to examine the applicability of such measures to describing dynamic behaviour.

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Section: Discussioncontrasting

confidence: 63%

Driver Psychological Types and Car Following: Is there a Correlation? Results of a Pilot Study

Brackstone

2005

Proceedings of the 2nd International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design: Driv

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“…The influence of personality traits on individual time headway thresholds was also not controlled for in this study. Research has shown that following behavior can be influenced by individual factors in self-driving (Heino, van der Molen, & Wilde, 1996;Ohta, 1993), this could also influence time headway thresholds in automated driving. Since there are no differences in velocity between the lead and the participant's vehicle in this study, the influence of differences in velocity between two vehicles and the resulting occurrence of non-zero time to collision should be researched.…”

Section: Discussionmentioning

confidence: 99%

The exact determination of subjective risk and comfort thresholds in car following

Siebert¹,

Oehl

Bersch

et al. 2017

Transportation Research Part F: Traffic Psychology and Behaviou

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In this study the location of vehicle to vehicle distance thresholds for self-reported subjective risk and comfort was researched. Participants were presented with ascending and descending time headway sequences in a driving simulator. This so called method of limits of ascending and descending stimuli (Gouy, Diels, Reed, Stevens, & Burnett, 2012) was refined to efficiently determine individual thresholds for stable time headways with a granularity of 0.1 seconds. Time headway thresholds were researched for 50, 100, and 150 km/h in a city, rural, and highway setting. Furthermore, thresholds for self-driving (level 0 automation: NHTSA, 2013) were compared with thresholds for the experience of subjective risk and comfort in assisted driving, similar to adaptive cruise control (level 1 automation). Results show that preferred individual time headways vary between subjects. Within subjects however, time headway thresholds do not significantly differ for different speeds. Furthermore we found that there was no significant difference between time headways of self-driving and distanceassisted driving. The relevance of these findings for the development of adaptive cruise control systems, autonomous driving and driver behavior modelling is discussed.

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“…While studying risk taking in driving, Heino et al (1996) noted differences in how individuals manage risks. They suggested the differentiation of individuals, along the lines of Zuckerman (1979), between those that engage in risky activities, known as sensation seekers, and those that tend to avoid such situations, known as sensation avoiders.…”

Section: Field Dependence/independence and Task/resource Commitmentmentioning

confidence: 97%

Cognitive styles as an explanation of experts’ individual differences: A case study in computer-assisted troubleshooting diagnosis

Cegarra¹,

Hoc²

2006

International Journal of Human-Computer Studies

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Differences in risk experience between sensation avoiders and sensation seekers

Cited by 57 publications

References 20 publications

Driver Psychological Types and Car Following: Is there a Correlation? Results of a Pilot Study

Driver Psychological Types and Car Following: Is there a Correlation? Results of a Pilot Study

The exact determination of subjective risk and comfort thresholds in car following

Cognitive styles as an explanation of experts’ individual differences: A case study in computer-assisted troubleshooting diagnosis

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