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• W4207068909 endingPage "103695" @default.
• W4207068909 startingPage "103695" @default.
• W4207068909 abstract "This study explored the possibility of applying personalized takeover requests (TORs) in an automated driving system (ADS), which required drivers to regain control when the system reached its limits. A driving simulator experiment was conducted to investigate how speech-based TOR voices impacted driver performance in takeover scenarios with two lead time conditions in conditionally automated driving (level 3). Eighteen participants drove in three sessions, with each session having a different TOR voice (a synthesized male voice, a synthesized female voice, and a significant other voice). Two scenarios with a lead time of 5 s and two scenarios with a lead time of 12 s were provided per session. The driver takeover time and quality data were collected. A follow-up interview was conducted to gain a clearer understanding of the drivers' psychological feelings about each TOR voice during takeovers. Changes in takeover time and takeover quality caused by TOR voices were similar in both lead time conditions, except for the lateral acceleration. The synthesized male voice led to a larger maximum lateral acceleration than the other two voices in the 5 s condition. Interestingly, most drivers preferred choosing the synthesized female voice for future takeovers and showed negative attitudes toward the significant other voice. Our results implied that choosing TOR voices should consider the drivers' daily voice-usage habits as well as specific context of use, and personalized TOR voices should be incorporated into the ADS prudently." @default.
• W4207068909 created "2022-01-26" @default.
• W4207068909 creator A5010583292 @default.
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• W4207068909 date "2022-05-01" @default.
• W4207068909 modified "2023-10-17" @default.
• W4207068909 title "Speech-based takeover requests in conditionally automated driving: Effects of different voices on the driver takeover performance" @default.
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• W4207068909 doi "https://doi.org/10.1016/j.apergo.2022.103695" @default.
• W4207068909 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35091271" @default.
• W4207068909 hasPublicationYear "2022" @default.
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