The effects of various auditory takeover requests: A simulated driving study considering the modality of non-driving-related tasksShow others and affiliations
Number of Authors: 82024 (English)In: Applied Ergonomics, ISSN 0003-6870, E-ISSN 1872-9126, Vol. 118, article id 104252Article in journal (Refereed) Published
Abstract [en]
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.
Place, publisher, year, edition, pages
2024. Vol. 118, article id 104252
Keywords [en]
Conditional automated driving, Non-speech-based takeover request, Takeover performance, Speech-based takeover request
National Category
Information Systems, Social aspects
Identifiers
URN: urn:nbn:se:su:diva-228734DOI: 10.1016/j.apergo.2024.104252ISI: 001192631300001PubMedID: 38417230Scopus ID: 2-s2.0-85186112161OAI: oai:DiVA.org:su-228734DiVA, id: diva2:1854202
2024-04-242024-04-242025-02-17Bibliographically approved