Literature DB >> 31731093

Driving automation state-of-mind: Using training to instigate rapid mental model development.

Martin Krampell1, Ignacio Solís-Marcos2, Magnus Hjälmdahl2.   

Abstract

The automotive industry is chugging along towards full autonomy, with a yet unknown time of arrival. The next call, however, is partial driving automation. At this interim station lurks many dangers, there-among them issues surrounding the partial performance of the driving task. Despite their potential for increased safety, these systems come with many inherent limitations and caveats, and their safe use depend on drivers correctly understanding their new role. Training is proposed as a potentially effective method of introducing drivers to the central aspects in this human-automation interaction. A proof-of-concept training program designed to introduce drivers to a partial automation system was developed. The effects of training were then evaluated through a between-group mixed-methods simulator experiment. Results indicate that trained drivers both self-report and exhibit an improved understanding of the automation system. They also report a significantly higher inclination to retake control in critical situation, than do their untrained counterparts.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Driver training; Driving experience; Human-automation interaction

Mesh:

Year:  2019        PMID: 31731093     DOI: 10.1016/j.apergo.2019.102986

Source DB:  PubMed          Journal:  Appl Ergon        ISSN: 0003-6870            Impact factor:   3.661


  1 in total

1.  Think Aloud Protocol Applied in Naturalistic Driving for Driving Rules Generation.

Authors:  Borja Monsalve; Nourdine Aliane; Enrique Puertas; Javier Fernández Andrés
Journal:  Sensors (Basel)       Date:  2020-12-03       Impact factor: 3.576

  1 in total

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