Human-Centric explanations for users in automated Vehicles: A systematic review
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View abstract on PubMed
Summary
This summary is machine-generated.Explanations for automated vehicles (AVs) should clarify reasoning, not just actions. Tailoring explanations to driving context and providing them before action improves user trust and acceptance, enhancing safety.
Area Of Science
- Human-Computer Interaction
- Automated Vehicle Systems
- Trustworthy AI
Background
- Automated vehicle (AV) decision-making can reduce user trust and acceptance.
- Human-centric explanations are crucial for transparency, trust, and safe human-vehicle interaction.
- Limited research exists on how explanation types affect drivers and how to evaluate them.
Purpose Of The Study
- To systematically review human-centric explanations in automated vehicle contexts.
- To identify best practices for what, when, and how to provide explanations.
- To address the impact of explanations on user trust, acceptance, and situational awareness.
Main Methods
- Systematic literature review following PRISMA guidelines.
- Searched five major databases (Scopus, Web of Science, IEEE Xplore, TRID, Semantic Scholar) from 2000 to April 2024.
- Included 59 articles out of 266 identified.
Main Results
- Explanations clarifying reasoning are more effective than those describing actions.
- Explanations before action are recommended, but optimal timing requires further study.
- Multimodal explanations are best when distinct; otherwise, visual-only is preferred. Narrative perspective impacts trust differently.
Conclusions
- Human-centric explanations must be tailored to specific driving contexts.
- Future research should focus on explanation length, timing, modality, and real-world validation.
- Optimized explanations are vital for safer, more trustworthy human-AV interactions.
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