Location

Santa Fe, New Mexico, USA

Date

27-6-2019

Session

Session 8 – Automation

Abstract

Higher level cognitive processes such as learning and mental models play a fundamental role in the success of automated driving, as technology can only be as good as our understanding and expectations of it. The present study investigated the development of these processes during interactions with driving automation. In a driving simulator study, N=52 participants completed several transitions between manual and Society of Automotive Engineers (SAE) levels 2 and 3 automated driving. Self-reported learning progress and mental model development were assessed via questionnaires. In parallel, eye-tracking data were collected as a behavioral measure of higher level cognitive functions. The results demonstrated that self-reported learning and gaze behavior followed a power-law function; the power-law functions showed task specific parameter manifestations. The evolution of the mental models of the level 2 and level 3 human-machine interface continued up to the fifth contact, indicating a long lasting process. For researchers and practitioners, the present study implies that accurate mental models require up to 5 repeated interactions. Furthermore, learning progress with driving automation can be captured through gaze behavior.

Rights

Copyright © 2019 the author(s)

DC Citation

Proceedings of the Tenth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 24-27 June 2019, Santa Fe, New Mexico. Iowa City, IA: Public Policy Center, of Iowa, 2019: 398-404.

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Jun 27th, 12:00 AM

Learning and Development of Mental Models during Interactions with Driving Automation: A Simulator Study

Santa Fe, New Mexico, USA

Higher level cognitive processes such as learning and mental models play a fundamental role in the success of automated driving, as technology can only be as good as our understanding and expectations of it. The present study investigated the development of these processes during interactions with driving automation. In a driving simulator study, N=52 participants completed several transitions between manual and Society of Automotive Engineers (SAE) levels 2 and 3 automated driving. Self-reported learning progress and mental model development were assessed via questionnaires. In parallel, eye-tracking data were collected as a behavioral measure of higher level cognitive functions. The results demonstrated that self-reported learning and gaze behavior followed a power-law function; the power-law functions showed task specific parameter manifestations. The evolution of the mental models of the level 2 and level 3 human-machine interface continued up to the fifth contact, indicating a long lasting process. For researchers and practitioners, the present study implies that accurate mental models require up to 5 repeated interactions. Furthermore, learning progress with driving automation can be captured through gaze behavior.