Towards Inclusive Mobility: A Comprehensive Survey of Agentic AI-Based Driving Systems for Neurodivergent Individuals

DOI

10.2991/978-94-6463-948-3_61How to use a DOI?

Keywords

neurodivergent driving; AI; accessibility; HCI; cognitive-aware; agents; multimodal emotion recognition; ethics

Abstract

Driving presents distinct cognitive challenges for neurodivergent individuals, including those with attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and related conditions. These challenges such as inattention, impulsivity, executive dysfunction, and sensory overload can significantly affect driving safety. Advances in artificial intelligence have introduced assistive technologies, including emotion recognition, adaptive interfaces, natural language interaction, and cognitively informed route planning. However, these developments remain fragmented and show limited integration into inclusive driver-assistance frameworks.

This survey reviews 20 studies at the intersection of artificial intelligence (AI), human computer interaction (HCI), and neurodiversity in driving. Existing approaches are categorized into key themes and evaluated for their strengths and limitations. Notable gaps include the absence of holistic cognitive-aware systems, limited personalization across neurodivergent profiles, and challenges in real-time adaptability. To address these challenges, the survey further examine advanced AI architectures, emphasizing multimodal emotion recognition via vocal biomarkers and dynamic agent frameworks such as AutoAgents, to enable adaptive, personalized support for neurodivergent individuals while ensuring adherence to ethical considerations. The survey concludes by identifying future directions for inclusive, AI-driven driving assistants that address cognitive accessibility needs.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Saniya Gupte
AU  - Amruta Kadam
AU  - Pradnya Bagde
AU  - Mohit More
AU  - Milind Mahajan
PY  - 2026
DA  - 2026/01/06
TI  - Towards Inclusive Mobility: A Comprehensive Survey of Agentic AI-Based Driving Systems for Neurodivergent Individuals
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 885
EP  - 902
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_61
DO  - 10.2991/978-94-6463-948-3_61
ID  - Gupte2026
ER  -