# Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload

arXiv: https://arxiv.org/abs/1905.08878 · HTML: https://mlsanity.com/paper/1905.08878/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2019.

## Built on by (most cited first, 12 of 12)

- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Exploring XAI for the Arts: Explaining Latent Space in Generative Music](https://mlsanity.com/paper/2308.05496/) (2023)
- [Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap](https://mlsanity.com/paper/2404.00019/) (2024)
- [Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning](https://mlsanity.com/paper/2409.17659/) (2024)
- [Exploring Variational Auto-Encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI](https://mlsanity.com/paper/2311.08336/) (2023)
- [Improving Explainable Object-induced Model through Uncertainty for Automated Vehicles](https://mlsanity.com/paper/2402.15572/) (2024)
- [Intention-Aware Decision-Making for Mixed Intersection Scenarios](https://mlsanity.com/paper/2303.17493/) (2023)
- [DriveBLIP2: Attention-Guided Explanation Generation for Complex Driving Scenarios](https://mlsanity.com/paper/2506.22494/) (2025)
- [From the Head or the Heart? An Experimental Design on the Impact of Explanation on Cognitive and Affective Trust](https://mlsanity.com/paper/2110.03433/) (2021)
- [What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) Decisions](https://mlsanity.com/paper/2505.06428/) (2025)
- [Context-aware, Ante-hoc Explanations of Driving Behaviour](https://mlsanity.com/paper/2511.14428/) (2025)
- [When2Talk: When Should a Proactive In-Car Agent Talk?](https://mlsanity.com/paper/2609.12503/) (2026)

## 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.