# Effects of Explanation Specificity on Passengers in Autonomous Driving

arXiv: https://arxiv.org/abs/2307.00633 · HTML: https://mlsanity.com/paper/2307.00633/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 2 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Explanations in Autonomous Driving: A Survey](https://mlsanity.com/paper/2103.05154/) (2021)
- [DReyeVR: Democratizing Virtual Reality Driving Simulation for Behavioural & Interaction Research](https://mlsanity.com/paper/2201.01931/) (2022)
- [From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles' Actions](https://mlsanity.com/paper/2204.09109/) (2022)
- [Understanding Consumer Preferences for Explanations Generated by XAI Algorithms](https://mlsanity.com/paper/2107.02624/) (2021)
- [Importance of Instruction for Pedestrian-Automated Driving Vehicle Interaction with an External Human Machine Interface: Effects on Pedestrians' Situation Awareness, Trust, Perceived Risks and Decision Making](https://mlsanity.com/paper/2102.07958/) (2021)

## Built on by (most cited first, 2 of 2)

- [Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap](https://mlsanity.com/paper/2404.00019/) (2024)
- [An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving](https://mlsanity.com/paper/2605.00291/) (2026)

## Abstract (arXiv)

The nature of explanations provided by an explainable AI algorithm has been a topic of interest in the explainable AI and human-computer interaction community. In this paper, we investigate the effects of natural language explanations' specificity on passengers in autonomous driving. We extended an existing data-driven tree-based explainer algorithm by adding a rule-based option for explanation generation. We generated auditory natural language explanations with different levels of specificity (abstract and specific) and tested these explanations in a within-subject user study (N=39) using an immersive physical driving simulation setup. Our results showed that both abstract and specific explanations had similar positive effects on passengers' perceived safety and the feeling of anxiety. However, the specific explanations influenced the desire of passengers to takeover driving control from the autonomous vehicle (AV), while the abstract explanations did not. We conclude that natural language auditory explanations are useful for passengers in autonomous driving, and their specificity levels could influence how much in-vehicle participants would wish to be in control of the driving activity.