Article
Version 1
Preserved in Portico This version is not peer-reviewed
Individual Differences in Signal Perception for the Take-over Request in Autonomous Driving
Version 1
: Received: 27 June 2023 / Approved: 28 June 2023 / Online: 28 June 2023 (12:41:54 CEST)
A peer-reviewed article of this Preprint also exists.
Lee, O.; Kang, H. Individual Differences in Signal Perception for Takeover Request in Autonomous Driving. Appl. Sci. 2023, 13, 8162. Lee, O.; Kang, H. Individual Differences in Signal Perception for Takeover Request in Autonomous Driving. Appl. Sci. 2023, 13, 8162.
Abstract
In the context of partial autonomy, where autonomous vehicles and humans share control of the vehicle, bringing out-of-the-loop drivers back into the loop is a significant challenge. While warning signal design guidelines are commonly used to provide alerts, few studies have examined each signal in depth with an emphasis on the autonomous environment. This study aims to fill this gap by investigating visual, auditory, and tactile stimuli and modifying their sub-attributes to explore variations related to age, gender, and other individual backgrounds. A driving simulator was utilized to create a realistic driving environment and measure participants' reaction times in takeover request situations. Analysis of the data revealed correlations between age and reaction times for auditory and tactile signals, with interaction effects observed between age and sub-attribute intensity. Additionally, participants exhibited varying reaction time patterns in response to different sub-attribute intensities. By evaluating individual differences in perception based on modality characteristics, often overlooked in prior research, this study serves as a foundational contribution to future research in the field.
Keywords
take over request; signal perception; autonomous driving; individual difference; warning signal; modality
Subject
Social Sciences, Transportation
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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