Pedestrian road-crossing willingness as a function of vehicle automation, external appearance, and driving behaviour

This paper discusses whether the knowledge of the driving mode of an approaching vehicle (manual vs. automated) influences pedestrians’ decisions while crossing a street. Additionally, the paper explores how different appearances and driving behaviours of vehicles interact with driving mode in affecting pedestrians’ road-crossing behaviours. In a video-based experiment with sixty participants, two vehicles with different appearances (a BMW 3 and a Renault Twizy) were presented as either manually-driven or automated vehicles. Both vehicles displayed either yielding or non-yielding behaviour on a straight road devoid of other traffic. Participants were asked to indicate whether they would cross the street in front of the approaching vehicle, at different distances ranging from 45 m to 1.5 m. The results showed that there was no significant influence of the knowledge of the driving mode (manually-driven vs automated) on pedestrians’ willingness to cross the street at any distance. The vehicle’s behaviour (whether it is maintaining speed or yielding) played a dominant role in pedestrians’ decision to cross a road, and this was similar for both modes and both vehicles, at all distances. However, results suggested that in situations and at distances when the intent of the vehicle was not fully clear by the behaviour of the car alone, there were differences between the two vehicles at certain distances, which could be attributed to the differences in their appearance such as size, aggressiveness and novelty. A futuristic-looking vehicle inspired less confidence in road-crossing situations compared to an ordinary-looking vehicle. Additionally, a novel and futuristic-looking vehicle appeared to make it easier for people to believe that it is an automated vehicle. The authors conclude by discussing design implications for the development of external HMIs automated vehicles.

Language

  • English

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  • Accession Number: 01717547
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 14 2019 3:08PM