User Education in Automated Driving: Owner’s Manual and Interactive Tutorial Support Mental Model Formation and Human-Automation Interaction

Klicks: 356
ID: 15886
2019
Artikelqualität & Leistungskennzahlen
Gesamtqualität
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
KI-Qualitätsbewertung
Nicht analysiert
Readership in this journal
Steady

Ranked #3 of 39 articles by views in information

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Automated driving systems (ADS) and a combination of these with advanced driver assistance systems (ADAS) will soon be available to a large consumer population. Apart from testing automated driving features and human–machine interfaces (HMI), the development and evaluation of training for interacting with driving automation has been largely neglected. The present work outlines the conceptual development of two possible approaches of user education which are the owner’s manual and an interactive tutorial. These approaches are investigated by comparing them to a baseline consisting of generic information about the system function. Using a between-subjects design, N = 24 participants complete one training prior to interacting with the ADS HMI in a driving simulator. Results show that both the owner’s manual and an interactive tutorial led to an increased understanding of driving automation systems as well as an increased interaction performance. This work contributes to method development for the evaluation of ADS by proposing two alternative approaches of user education and their implications for both application in realistic settings and HMI testing.
Referenzschlüssel
forster2019userinformation Verwenden Sie diesen Schlüssel zum automatischen Zitieren im Manuskript bei Verwendung von SciMatic Manuscript Manager oder Thesis Manager
Autoren Forster, Yannick;Hergeth, Sebastian;Naujoks, Frederik;Krems, Josef;Keinath, Andreas;
Zeitschrift information
Jahr 2019
DOI
DOI nicht gefunden
URL
Schlüsselwörter Schlüsselwörter nicht gefunden

Zitationen

Keine Zitationen gefunden. Um eine Zitation hinzuzufügen, kontaktieren Sie den Administrator unter info@scimatic.org

Noch keine Kommentare. Seien Sie der Erste, der diesen Artikel kommentiert.