Latest revision as of 04:46, 23 March 2021

Abstract

With self-driving vehicles (SDVs), pedestrians can no longer rely on a human driver. Previous research suggests that pedestrians may benefit from an external Human–Machine Interface (eHMI) displaying information to surrounding traffic participants. This paper introduces a natural methodology to compare eHMI concepts from a pedestrian’s viewpoint. To measure eHMI effects on traffic flow, previous video-based studies instructed participants to indicate their crossing decision with interfering data collection devices, such as pressing a button or slider. We developed a quantifiable concept that allows participants to naturally step off a sidewalk to cross the street. Hidden force-sensitive resistor sensors recorded their crossing onset time (COT) in response to real-life videos of approaching vehicles in an immersive crosswalk simulation environment. We validated our method with an initial study of N = 34 pedestrians by showing (1) that it is able to detect significant eHMI effects on COT as well as subjective measures of perceived safety and user experience. The approach is further validated by (2) replicating the findings of a test track study and (3) participants’ reports that it felt natural to take a step forward to indicate their street crossing decision. We discuss the benefits and limitations of our method with regard to related approaches.

Document type: Article

Full document

Original document

The different versions of the original document can be found in:

• https://www.mdpi.com/2078-2489/11/7/360/pdf under the license https://creativecommons.org/licenses/by

• https://www.mdpi.com/2078-2489/11/7/360,

https://dblp.uni-trier.de/db/journals/information/information11.html#FaasMKB20,

https://doi.org/10.3390/info11070360,

https://academic.microsoft.com/#/detail/3041859452 under the license cc-by

• https://www.mdpi.com/2078-2489/11/7/360,

https://doaj.org/toc/2078-2489

• https://www.mdpi.com/2078-2489/11/7/360/pdf,

http://dx.doi.org/10.3390/info11070360

under the license https://creativecommons.org/licenses/by/4.0/