Citation

Souders DJ, Baringer K, King SL, Mintz A. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2022; 66(1): 346-350.

Copyright

(Copyright © 2022, Human Factors and Ergonomics Society, Publisher SAGE Publishing)

DOI

10.1177/1071181322661323

PMID

unavailable

Abstract

Advanced driver assistance systems (ADAS) have been increasingly incorporated in cars for nearly four decades and have changed the relationship of the driver to the driving task substantially. Over this period, original equipment manufacturers (OEMs) have developed similar ADAS functions (e.g., adaptive cruise control, lane keeping assist, forward collision warning), but these functions lack uniformity in their implementation such that there is the possibility of negative transfer of learning across different implementations of the same ADAS function. This brief theoretical paper aims to highlight issues around using some existing human-automation interaction (HAI) frameworks that have been used to classify vehicle automation and discuss considerations for better ADAS classification to inform their design and support safe and satisfactory use.

Language: en