Citation

Domeyer JE, Lee JD, Toyoda H, Mehler B, Reimer B. IEEE Trans. Intel. Transp. Syst. 2022; 23(5): 4122-4134.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2020.3041562

PMID

unavailable

Abstract

Communication among road users smooths interactions, improves efficiency, and mitigates risk. Eye contact and waving may be the most salient of this communication, but more often road users use their movement or position as implicit signals. Vehicle automation may disrupt these signals by introducing unfamiliar or unclear interactions that may not align with other road user expectations. This creates a need to evaluate how effectively vehicle automation communicates and how this affects safety and efficiency. Vehicle automation with effective communication can enter the roadway ecology more naturally and facilitate acceptance across society. We modeled the outcomes of vehicle-pedestrian encounters where drivers yielded for pedestrians. Models of the initial conditions revealed that drivers and pedestrians jointly contribute to safety and efficiency; however, the initial conditions were generally poor predictors, suggesting that dynamic interaction may be an important determinant of those outcomes. An interdependence model of wait times revealed that driver and pedestrian influence on one another varied across traffic control devices. During the nonintersection encounters, pedestrian wait time depended on their own speed and distance when entering the encounter, indicating that they may linger away from the road and choose the encounter conditions. The stop sign encounters showed a division of influence, with pedestrian and driver initial conditions influencing their own wait time. The unprotected encounters showed negotiation, with pedestrian initial conditions influencing the driver wait time. We demonstrate the need and methods to understand interdependence between road users and the implications for vehicle automation communication.

Language: en

Keywords

Accidents; Automation; Human factors; Measurement; Roads; Safety; Vehicles; autonomous vehicles; pedestrian; automation; human-robot interaction; Instruments