Abstract
Despite the increasing sophistication of autonomous vehicles (AVs) and promises of increased safety, accidents will occur. These will corrode public trust and negatively impact user acceptance, adoption and continued use. It is imperative to explore methods that can potentially reduce this impact. The aim of the current paper is to investigate the efficacy of informational assistants (IAs) varying by anthropomorphism (humanoid robot vs. no robot) and dialogue style (conversational vs. informational) on trust in and blame on a highly autonomous vehicle in the event of an accident. The accident scenario involved a pedestrian violating the Highway Code by stepping out in front of a parked bus and the AV not being able to stop in time during an overtake manoeuvre. The humanoid (Nao) robot IA did not improve trust (across three measures) or reduce blame on the AV in Experiment 1, although communicated intentions and actions were perceived by some as being assertive and risky. Reducing assertiveness in Experiment 2 resulted in higher trust (on one measure) in the robot condition, especially with the conversational dialogue style. However, there were again no effects on blame. In Experiment 3, participants had multiple experiences of the AV negotiating parked buses without negative outcomes. Trust significantly increased across each event, although it plummeted following the accident with no differences due to anthropomorphism or dialogue style. The perceived capabilities of the AV and IA before the critical accident event may have had a counterintuitive effect. Overall, evidence was found for a few benefits and many pitfalls of anthropomorphising an AV with a humanoid robot IA in the event of an accident situation.
| Original language | English |
|---|---|
| Number of pages | 35 |
| Journal | Multimodal Technologies and Interaction |
| Early online date | 5 Dec 2024 |
| DOIs | |
| Publication status | Published - 5 Dec 2024 |
Data Availability Statement
Data are available via the ES/T007079/1-Rule of Law in the Age of AI: Principles of Distributive Liability for Multi-Agent Societies ESRC Data Archive. Please contact the corresponding author for details.Acknowledgements
We thank our project collaborators for their invaluable discussions regarding all aspects of the research presented in this paper: Minoru Asada (Osaka University), Tatsuhiko Inatani (Kyoto University), Hirofumi Katsuno (Doshisha University), and Yoshiyuki Ueda (Kyoto University). We thank Arpit Patel for significantly contributing to the programming of the driving scenarios across all experiments. We also thank three anonymous reviewers of an earlier draft for their insightful comments and suggestions. Finally, we wish to dearly thank Dylan M. Jones, who we sadly lost too suddenly and too soon in 2022. Dylan was a Co-Investigator on the project (and so many others), an inspiration to us all, and a dear colleague and friend.Funding
This work was funded by the ESRC-JST project: Rule of Law in the Age of AI: Principles of Distributive Liability for Multi-Agent Societies. P.L.M. was the UK Lead Investigator, and Prof Tatsuhiko Inatani (Kyoto University) was the Japan Lead Investigator. ES/T007079/1-Rule of Law in the Age of AI: Principles of Distributive Liability for Multi-Agent Societies (ESRC and JST).
Keywords
- autonomous vehicle
- nformational assistant
- robot
- dialogue style
- anthropomorphism
- trust
- blame
- accident outcome