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- Chai, Chunlei, et al.
(författare)
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The effects of various auditory takeover requests : A simulated driving study considering the modality of non-driving-related tasks
- 2024
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Ingår i: Applied Ergonomics. - 0003-6870 .- 1872-9126. ; 118
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Tidskriftsartikel (refereegranskat)abstract
- With the era of automated driving approaching, designing an effective auditory takeover request (TOR) is critical to ensure automated driving safety. The present study investigated the effects of speech-based (speech and spearcon) and non-speech-based (earcon and auditory icon) TORs on takeover performance and subjective preferences. The potential impact of the non-driving-related task (NDRT) modality on auditory TORs was considered. Thirty-two participants were recruited in the present study and assigned to two groups, with one group performing the visual N-back task and another performing the auditory N-back task during automated driving. They were required to complete four simulated driving blocks corresponding to four auditory TOR types. The earcon TOR was found to be the most suitable for alerting drivers to return to the control loop because of its advantageous takeover time, lane change time, and minimum time to collision. Although participants preferred the speech TOR, it led to relatively poor takeover performance. In addition, the auditory NDRT was found to have a detrimental impact on auditory TORs. When drivers were engaged in the auditory NDRT, the takeover time and lane change time advantages of earcon TORs no longer existed. These findings highlight the importance of considering the influence of auditory NDRTs when designing an auditory takeover interface. The present study also has some practical implications for researchers and designers when designing an auditory takeover system in automated vehicles.
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| 2. |
- Li, Guofa, et al.
(författare)
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Driver Behavior in Intelligent Transportation Systems
- 2022
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Ingår i: IEEE Intelligent Transportation Systems Magazine. - 1939-1390 .- 1941-1197. ; 14:3, s. 7-9
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Drivers are the center of road/air/sea transportation systems, and they can be either human beings or artificial beings. Inconsistency between human driver behavior and artificial driver behavior will lead to accidents and congestion in intelligent transportation systems (ITSs) [1] , [2] . To make future ITSs trustworthy for traffic safety and acceptable for travel efficiency, developing industrial ITS applications based on drivers’ reliable behavioral and cognitive intelligence is essential [3] . However, there are many challenges to be addressed, including real-time behavior prediction, reliable decision making, safe interaction among human and artificial drivers, and so on.
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