Investigating Drivers’ Awareness of Automated Vehicle Modes Using the State Diagram Method

Driving automation introduces multiple driving modes to maximize the system’s benefits, but drivers must monitor and stay aware of these modes, which can sometimes lead to mode confusion. We modified Degani and Heymann’s state diagram method to assess the mode structure of a hypothetical driving automation system and the likelihood of discrepancies between drivers’ mode awareness and the system’s actual mode. We used the modified method and driving simulation data from participants who weren’t fully informed about all automation modes. The modified method visualized all possible combinations of automation modes and drivers’ mode awareness, highlighting where they diverge and estimating the frequency of the divergence. The diagram identified areas where human-machine interface (HMI) design can help drivers maintain accurate mode awareness. The modified state diagram method provides actionable insights for designing HMIs to reduce mode confusion and can be used to develop computational models of automation mode structures.

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Publication Details

Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2024-09-01
DOI
https://doi.org/10.1177/10711813241275986
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00
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article

Investigating Drivers’ Awareness of Automated Vehicle Modes Using the State Diagram Method

John D. Lee, Hansol Rheem, Joseph Szczerba, Akilesh Rajavenkatanarayanan et al.
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Human-Automation Interaction and Safety
article

Investigating Drivers’ Awareness of Automated Vehicle Modes Using the State Diagram Method

John D. Lee, Hansol Rheem, Joseph Szczerba, Akilesh Rajavenkatanarayanan, Joonbum Lee, Roy Mathieu
article en

Abstract

Driving automation introduces multiple driving modes to maximize the system’s benefits, but drivers must monitor and stay aware of these modes, which can sometimes lead to mode confusion. We modified Degani and Heymann’s state diagram method to assess the mode structure of a hypothetical driving automation system and the likelihood of discrepancies between drivers’ mode awareness and the system’s actual mode. We used the modified method and driving simulation data from participants who weren’t fully informed about all automation modes. The modified method visualized all possible combinations of automation modes and drivers’ mode awareness, highlighting where they diverge and estimating the frequency of the divergence. The diagram identified areas where human-machine interface (HMI) design can help drivers maintain accurate mode awareness. The modified state diagram method provides actionable insights for designing HMIs to reduce mode confusion and can be used to develop computational models of automation mode structures.

Proceedings of the Human Factors and Ergonomics Society Annual MeetingVol. 68(1)
General Motors (United States) (US), University of Wisconsin–Madison (US)
Openalex Percentile: Top 81%
Human-Automation Interaction and Safety
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Investigating Drivers’ Awareness of Automated Vehicle Modes Using the State Diagram Method — John D. Lee, Hansol Rheem, et al. · Proceedings of the Human Factors and Ergonomics Society Annual Meeting (2024) | TGRS Research Map | TGRS