A Reference Model for Driver Attention in Automation: Glance Behavior Changes During Lateral and Longitudinal Assistance
Journal article, 2019

This paper introduces a reference model of glance behavior for driving safety assessment. This model can improve the design of automated and assistive systems. Technological limitations have previously hindered the use of unobtrusive eye trackers to measure glance behavior in naturalistic conditions. This paper presents a comprehensive analysis of eye-tracking data collected in a naturalistic field operation test, using an eye tracker that proved to be robust in real-world driving scenarios. We describe a post-processing technique to enhance the quality of naturalistic eye-tracker data, propose a data-analysis procedure that captures the important features of glance behavior, and develop a model of glance behavior (based on distribution fitting), which was lacking in the literature. The model and its metrics capture key defining characteristics of, and differences between, on- and off-path glance distributions, and during manual driving and driving with adaptive cruise control and lane keeping aid active. The results show that drivers' visual response is tightly coupled to the driving context (vehicle automation, car-following, and illumination).

Author

Alberto Morando

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Trent Victor

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Marco Dozza

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN) 1558-0016 (eISSN)

Vol. 20 8 2999-3009 8482505

Human Factors of Automated Driving (HFAUTO)

European Commission (EC) (EC/FP7/605817), 2013-11-01 -- 2017-10-31.

Quantitative Driver Behaviour Modelling for Active Safety Assessment Expansion (QUADRAE)

VINNOVA (2015-04863), 2016-01-01 -- 2019-12-31.

Areas of Advance

Transport

Subject Categories

Transport Systems and Logistics

Other Engineering and Technologies not elsewhere specified

Vehicle Engineering

DOI

10.1109/TITS.2018.2870909

More information

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4/6/2022 5