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

Abstract

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.

Interaction behaviour of active mobility users in shared space

“…This provides theoretical basis for hypothesising the mediated effects of perceived risk level (H4a and H4b) and perceived priority (H5a and H5b) on behavioural intention through attitude, which have been built into the developed model. However, some empirical studies directly related perceived risk level (Twisk et al, 2015) and perceived priority (Vlakveld et al, 2020) with behavioural intention. This suggests that there might be unmediated effects from perceived risk level and perceived priority to behavioural intention.…”

External Human–Machine Interfaces: The Effect of Display Location on Crossing Intentions and Eye Movements

“…Together, our findings suggest that eHMIs should be omnidirectional if they are to be applied in traffic scenarios where cars can approach from multiple directions. Vlakveld et al [26] showed animations of cars with an omnidirectional eHMI on the roof, whereas drive.ai [27] used multiple displays on the car's exterior. Another solution to ensure visibility from all sides is to use a light emitting diode (LED) strip as in Cefkin et al [39], or LED patterns on the lateral surfaces of the car [40].…”

How do pedestrians distribute their visual attention when walking through a parking garage? An eye-tracking study

“…What our study shows is that people look at different sides and features of the car (front, side, rear, wheels), as well as humans in cars. This, in turn, would require that eHMIs are omnidirectionally visible, for example by means of a display on the roof (Vlakveld, Van der Kint, & Hagenzieker, 2020) or LED strips on the car (Cefkin et al, 2019;Nissan, 2015;Volvo Cars, 2018). Our findings also suggest that an eHMI on only the front of the car, which appears to be the norm in the literature so far , is of limited use in parking garages, as the fronts of cars receive less than 50% of the attention than the backs of cars.…”