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Design and Evaluation of a Virtual Reality Application to Enhance the Acceptability of Autonomous Vehicles for Disabled People

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AI Approaches for Designing and Evaluating Interactive Intelligent Systems (ROCHI 2022)

Abstract

Artificial intelligence (AI) is now fully integrated into our daily lives, and its influence on human–computer interaction (HCI) is undeniable. As technology evolves, AI is playing a key role in improving the usability of interactions between humans and computers, making them more intuitive, efficient and user-friendly. This article explores the possibilities of AI in HCI and the potential benefits it offers. This study aims to assess the acceptability of individuals with disabilities toward connected and autonomous vehicles (CAV). To achieve this goal, a virtual reality application was created to simulate two types of fully autonomous shuttles and a conventional bus. Eleven participants with physical disabilities tested the application. The findings indicate that the participants exhibit a relatively high level of trust in the autonomous transport system, expressing a preference for it over the conventional bus. The autonomous shuttle with enhanced onboard services received particular acclaim from the participants. These results are promising and align with research conducted on the general population. Further validation of these acceptability findings is deemed necessary through testing larger cohorts in real-world scenarios.

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Acknowledgements

The contribution of this chapter has been supported by all the partners of the European Horizon 2020 project Enhance driver behaviour and Public Acceptance of Connected and Autonomous vehicLes.

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Correspondence to Nicolas Bert .

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Bert, N., Zare, M., Larique, M., Sagot, J.C. (2024). Design and Evaluation of a Virtual Reality Application to Enhance the Acceptability of Autonomous Vehicles for Disabled People. In: Kolski, C., Mihăescu, M.C., Rebedea, T. (eds) AI Approaches for Designing and Evaluating Interactive Intelligent Systems. ROCHI 2022. Learning and Analytics in Intelligent Systems, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-031-53957-2_4

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