What will the car driver do? A video-based questionnaire study on cyclists' anticipation during safety-critical situations

Kovacsova, N.; Winter, Joost de; Hagenzieker, Marjan

doi:10.1016/j.jsr.2019.01.002

Cited by 15 publications

(10 citation statements)

References 35 publications

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“…Finally, another 0.5 s was cut from the videos with the car going straight on and 1 s from the videos with the turning car, to be used in the 'early Decision moment' condition. Kovácsová, de Winter, and Hagenzieker (2019) also conducted a video-based test in which cyclists at the end of each video were asked what they would do in conflict situations with a car. They also removed frames from the end of the videos to create versions in which a crash was less imminent than in the most critical version.…”

Section: Stimuli and Materialsmentioning

confidence: 99%

Cyclists’ intentions to yield for automated cars at intersections when they have right of way: Results of an experiment using high-quality video animations

Vlakveld

Kint

Hagenzieker

2020

Transportation Research Part F: Traffic Psychology and Behaviou

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What will cyclists do in future conflict situations with automated cars at intersections when the cyclist has the right of way? In order to explore this, short high-quality animation videos of conflicts between a car and a cyclist at five different intersections were developed. These videos were 'shot' from the perspective of the cyclist and ended when a collision was imminent should the car or the bicyclist not slow down. After each video participants indicated whether they would slow down or continue cycling, how confident they were about this decision, what they thought the car would do, and how confident they were about what the car would do. The appearance of the approaching car was varied as within-subjects variable with 3 levels (Car type): automated car, automated car displaying its intentions to the cyclists, and traditional car. In all situations the cyclist had right of way. Of each conflict, three versions were made that differed in the moment that the video ended by cutting off fractions from the longest version, thus creating videos with an early, mid, and late moment for the cyclist to decide to continue cycling or to slow down (Decision moment). Before the video experiment started the participants watched an introductory video about automated vehicles that served as prime. This video was either positive, negative, or neutral about automated vehicles (Prime type). Both Decision moment and Prime type were between subject variables. After the experiment participants completed a short questionnaire about trust in technology and trust in automated vehicles. 1009 participants divided in nine groups (one per Decision moment and Prime) completed the online experiment in which they watched fifteen videos (5 conflicts Â 3 car types). The results show that participants more often yielded when the approaching car was an automated car than when it was a traditional car. However, when the approaching car was an automated car that could communicate its intentions, they yielded less often than for a traditional car. The earlier the Decision moment, the more often participants yielded but this increase in yielding did not differ between the three car types. Participants yielded more often for automated cars (both types) after they watched the negative prime video before the experiment than when they watched the positive video. The less participants trusted technology, and the capabilities of automated vehicles in particular, the more they were inclined to slow down in the conflict situations with automated cars. The association between trust and yielding was stronger for trust in the capabilities of automated vehicles than for trust in technology in general.

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Section: Stimuli and Materialsmentioning

confidence: 99%

Cyclists’ intentions to yield for automated cars at intersections when they have right of way: Results of an experiment using high-quality video animations

Vlakveld

Kint

Hagenzieker

2020

Transportation Research Part F: Traffic Psychology and Behaviou

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“…A 7-item DBQ was also completed. S8 (Kovácsová, De Winter, & Hagenzieker, 2019) Feb 27, 2017-Feb 28, 2017…”

Section: Analysis At the Individual Levelmentioning

confidence: 99%

“…Respondents participated in a reaction-time measurement task and completed the DBQ. S10 (Kovácsová et al, 2019) Mar 4 External human-machine interfaces for automated driving.…”

Section: Analysis At the Individual Levelmentioning

confidence: 99%

When will most cars be able to drive fully automatically? Projections of 18,970 survey respondents

Bazilinskyy

Kyriakidis

Dodou

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

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“…In recent years, scientific research has investigated a series of factors that may influence and reduce the risk of accidents. For instance, separation of bike flows from motor vehicles has represented a strategical step towards safer infrastructures, even if critical issues remain in connection areas, where the various flows dangerously converge in extremely complex scenarios [7][8][9][10]. From a functional perspective, one of the best solutions is to introduce roundabouts, solving most of the safety problems of the various traffic components.…”

Section: Introductionmentioning

confidence: 99%

Road Safety for Cyclists Based on the Calories Needed

et al. 2021

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Cyclists are a vulnerable group of road users, especially when no separate infrastructure for cyclists is provided. Then, road factors such as distance and altitude differences can indirectly affect cyclists' safety. Therefore, the authors proposed a procedure based on the geometric characteristics of the road that can determine riding difficulties for cyclists. The proposed procedure can be used both by the public authorities who manage cyclists' safety and as a method of classifying the road network for cycling. The proposed procedure, based on the use of pattern recognition techniques, analyses data from a sample of nine riders who travelled on rural roads within the Municipality of Messina (Italy) to classify the roads according to their cycling difficulty. For each rider, duration, distance, road slope, altitude difference and spent calories have been measured and analysed. The collected data were used for the development of a model capable of predicting the cyclist's physical effort as a function of the road alignment itself. Knowing the effort required to cycle along a route can contribute to a more complete assessment of road classification, commonly defined according to motor vehicles. Moreover, it may constitute a measure determining the safety of cycling by encouraging cyclists to travel along roads compatible with their physical abilities.

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What will the car driver do? A video-based questionnaire study on cyclists' anticipation during safety-critical situations

Cited by 15 publications

References 35 publications

Cyclists’ intentions to yield for automated cars at intersections when they have right of way: Results of an experiment using high-quality video animations

Cyclists’ intentions to yield for automated cars at intersections when they have right of way: Results of an experiment using high-quality video animations

When will most cars be able to drive fully automatically? Projections of 18,970 survey respondents

Road Safety for Cyclists Based on the Calories Needed

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