English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

With the level of automation increases in vehicles, such as conditional andhighly automated vehicles (AVs), drivers are becoming increasingly out of thecontrol loop, especially in unexpected driving scenarios. Although it might benot necessary to require the drivers to intervene on most occasions, it isstill important to improve drivers' situation awareness (SA) in unexpecteddriving scenarios to improve their trust in and acceptance of AVs. In thisstudy, we conceptualized SA at the levels of perception (SA L1), comprehension(SA L2), and projection (SA L3), and proposed an SA level-based explanationframework based on explainable AI. Then, we examined the effects of theseexplanations and their modalities on drivers' situational trust, cognitiveworkload, as well as explanation satisfaction. A three (SA levels: SA L1, SA L2and SA L3) by two (explanation modalities: visual, visual + audio)between-subjects experiment was conducted with 340 participants recruited fromAmazon Mechanical Turk. The results indicated that by designing theexplanations using the proposed SA-based framework, participants could redirecttheir attention to the important objects in the traffic and understand theirmeaning for the AV system. This improved their SA and filled the gap ofunderstanding the correspondence of AV's behavior in the particular situationswhich also increased their situational trust in AV. The results showed thatparticipants reported the highest trust with SA L2 explanations, although themental workload was assessed higher in this level. The results also providedinsights into the relationship between the amount of information inexplanations and modalities, showing that participants were more satisfied withvisual-only explanations in the SA L1 and SA L2 conditions and were moresatisfied with visual and auditory explanations in the SA L3 condition.

Investigating Explanations in Conditional and Highly Automated Driving: The Effects of Situation Awareness and Modality