- Titel
- Understanding Driver Monitoring Systems: The Impact of User Knowledge on Evaluation and Intention to Use
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:ch1-qucosa2-1026292
- Konferenz
- 9th Humanist Virtual Centre of Excellence Conference 2025. Chemnitz, 27.-29. August 2025
- Quellenangabe
- The 9th HUMANIST Conference - Virtual Centre of Excellence - Chemnitz, Germany 27–29 August 2025
- DOI
- https://doi.org/10.60687/2026-0052
- Abstract (EN)
- With the increasing integration of safety technologies in vehicles, Driver Monitoring Systems (DMS) have emerged as tools for monitoring driver states and mitigating distractions. To ensure the effectiveness of in-vehicle DMS, it is crucial to understand how drivers perceive and comprehend them, which, according to the Technology Acceptance Model (TAM), impacts the intention to use them. In this driving simulator study, 40 participants were divided into two groups: one was informed about the DMS functionalities whereas the other was not. After experiencing the DMS in a driving simulator, a qualitative interview was conducted to assess the participants’ evaluation and intention to use the DMS. An inductive category formation (Mayring, 2015) was employed to analyse the data. The descriptive analysis showed that the majority of participants described the DMS positively, although many expressed annoyance due to its frequency of warnings. Participants who received instructions about the DMS functionalities evaluated it more positively. More participants indicated a willingness to use the DMS rather than deactivating it, particularly when they received instructions about its functionalities. Furthermore, a significant correlation (r = 0.37, p = .030) was identified, showing that a more positive evaluation of the DMS led to higher intention to use it and vice versa. The results highlight the positive aspects of instruction for DMS and provide initial evidence that the TAM is also applicable to DMS.
- Freie Schlagwörter (EN)
- driver monitoring system, visual distraction, qualitative data, driving simulator study
- Klassifikation (DDC)
- 158
- Normschlagwörter (GND)
- Ablenkung, Qualitative Daten, Fahrsimulator
- Herausgeber (Institution)
- Technische Universität Chemnitz, Chemnitz
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:ch1-qucosa2-1026292
- Veröffentlichungsdatum Qucosa
- 26.02.2026
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY-SA 4.0