Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload
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Abstract (arXiv)
Explanations given by automation are often used to promote automation adoption. However, it remains unclear whether explanations promote acceptance of automated vehicles (AVs). In this study, we conducted a within-subject experiment in a driving simulator with 32 participants, using four different conditions. The four conditions included: (1) no explanation, (2) explanation given before or (3) after the AV acted and (4) the option for the driver to approve or disapprove the AV's action after hearing the explanation. We examined four AV outcomes: trust, preference for AV, anxiety and mental workload. Results suggest that explanations provided before an AV acted were associated with higher trust in and preference for the AV, but there was no difference in anxiety and workload. These results have important implications for the adoption of AVs.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 May 2019.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 3; 2024: 3; 2025: 3; 2026: 1.
12 papers.
- 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
- 2023Exploring XAI for the Arts: Explaining Latent Space in Generative Musiccited by 8
- 2024Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmapcited by 6
- 2024Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learningcited by 5
- 2023Exploring Variational Auto-Encoder Architectures, Configurations, and Datasets for Generative Music Explainable AIcited by 4
- 2024Improving Explainable Object-induced Model through Uncertainty for Automated Vehiclescited by 4
- 2023Intention-Aware Decision-Making for Mixed Intersection Scenarioscited by 1
- 2025DriveBLIP2: Attention-Guided Explanation Generation for Complex Driving Scenarioscited by 1
- 2021From the Head or the Heart? An Experimental Design on the Impact of Explanation on Cognitive and Affective Trustcited by 0
- 2025What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisionscited by 0
- 2025Context-aware, Ante-hoc Explanations of Driving Behaviourcited by 0
- 2026When2Talk: When Should a Proactive In-Car Agent Talk?cited by 0
- 2026When2Talk: When Should a Proactive In-Car Agent Talk?11 Sep 2026
- 2025Context-aware, Ante-hoc Explanations of Driving Behaviour18 Nov 2025
- 2025DriveBLIP2: Attention-Guided Explanation Generation for Complex Driving Scenarios25 Jun 2025
- 2025What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions9 May 2025
- 2024Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning26 Sep 2024
- 2024Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap19 Mar 2024
- 2024Improving Explainable Object-induced Model through Uncertainty for Automated Vehicles23 Feb 2024
- 2023Exploring Variational Auto-Encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI14 Nov 2023
- 2023Exploring XAI for the Arts: Explaining Latent Space in Generative Music10 Aug 2023
- 2023Intention-Aware Decision-Making for Mixed Intersection Scenarios29 Mar 2023
- 2021From the Head or the Heart? An Experimental Design on the Impact of Explanation on Cognitive and Affective Trust7 Oct 2021
- 2021Explainability of deep vision-based autonomous driving systems: Review and challenges13 Jan 2021