Hazard anticipation of young novice drivers : assessing and enhancing the capabilities of young novice drivers to anticipate latent hazards in road and traffic situations

Uit het onderzoek blijkt dat ervaren bestuurders deze toets beter maken dan beginners. Ook bleken jonge beginners die een ongeval hebben gehad de toets slechter te maken dan jonge beginners die géén ongeval hebben gehad. Van een ongevalservaring alleen leert men kennelijk niet, zo concludeert Vlakveld. Hij zette zijn trainingsmodule zodanig op dat men er wél van leert. Vlakveld concludeert voorts dat gevaaranticipatie beter is te toetsen aan de hand van bewegende beelden. Ook is het bij een filmtoets moeilijker om te slagen op basis van alleen examentraining (het maken van oefenopgaven) en zullen kandidaten genoodzaakt zijn zich daadwerkelijk te bekwamen in gevaaranticipatie. Behalve voor een CBR-toets met bewegend beeld, pleit Vlakveld dan ook voor de opname van een gevaaranticipatietraining in de rijopleidingen. Vlakveld ontwikkelde, als onderdeel van zijn onderzoek, de training. Als is aangetoond dat de training ook in de praktijk beklijft, kan deze in de rijopleiding worden opgenomen. The research shows that experienced drivers perform better in this test than novice drivers. But young novice drivers who have already had an accident do worse in the test than young novice drivers who have not been involved in an accident. Vlakveld concludes from this that not all drivers learn from the experience of an accident. His own training module has been devised in a way that helps drivers to learn from their experience. Vlakveld also concludes that it is easier to assess hazard anticipation using moving images. Practising solely with theoretical exercises makes it more difficult to pass a film assessment and so it would be better if candidates were obliged to practise anticipating hazards in a practical situation. So apart from practising for a CBR assessment with moving images, Vlakveld would also like to see a hazard anticipation training course included alongside standard driving lessons. Vlakveld devised the training course as part of his research. If the course proves to leave a lasting impression, it can be included for learner drivers alongside standard driving lessons.

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

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 × 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.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin

Corrigendum to “Speed characteristics of speed pedelecs, pedelecs and conventional bicycles in naturalistic urban and rural traffic conditions” [Accid. Anal. Prev. 150 (2021) 105940] (Accident Analysis and Prevention (2021) 150, (S0001457520317607), (10.1016/j.aap.2020.105940))

The authors regret that a mistake was made in Table 3 of the article. This table should read: [Table presented] The figures in bold, italic and red differ from the figures in the published article. As a result of these different values, the sixth sentence of the abstract should read: “S-pedelecs were much faster than conventional bicycles, amounting to a speed difference with conventional bicycles of 9.6 km/h in urban areas (M = 26.9 km/h vs. 17.3 km/h) and of 13.1 km/h in rural areas (M = 31.4 km/h vs. 18.3 km/h).” and the seventh sentence of the abstract should read: “The speed differences between pedelecs and conventional bicycles were much smaller: 2.8 km/h in urban areas (20.1 km/h vs 17.3 km/h) and 3.9 km/h in rural areas (22.2 km/h vs. 18.3 km/h). In the Discussion, Section 4.1. Comparison with previous studies on cycling speed characteristics, the second sentence of the second section should read: “Our study found similar patterns among Dutch riders. S-pedelecs were much faster than conventional bicycles, amounting to a speed difference in mean speeds in urban areas of 9.6 km/h (M = 26.9 km/h vs. 17.3 km/h) and in rural areas of 13.1 km/h (M = 31.4 km/h vs. 18.3 km/h). Values different from those in the published text are in bold, italic, and red. The authors would like to apologise for any inconvenience caused.</p