GenAI-Driven Adaptation in Next-Generation Automotive Human–Machine Interaction
DOI:
https://doi.org/10.5281/zenodo.20591684Keywords:
Generative AI, personalization, behavior-adaptive interfaces, automotive HCI, multimodal interfaces,In-vehicle personalization,Driver behavior modeling,Adaptive human–machine interface (HMI),Context-aware interaction,Multimodal user sensing,Generative conversational assistant,Real-time intent prediction,Emotion-aware UX,Proactive recommendations (in-car),Privacy-preserving personalization.Abstract
The rapid improvement of interactive generative artificial intelligence systems, such as ChatGPT and DALL·E, enables ample opportunities for generative AI applications in various fields. In the context of the automotive domain, their utilization is expected to provide personalized in-vehicle experiences and services, as well as behavior-adaptive interfaces for both drivers and passengers. Recent advances in natural language processing and image synthesis open new horizons for addressing the technical challenges of enabling personalized infotainment experiences and behavior-adaptive multimodal interfaces in vehicles. Infotainment systems can benefit from their user intent modeling ability to deliver contextually relevant responses, while the rich generative models trained on large-scale visual datasets can enhance communication of driving states and intentions. Inferring drivers' emotional states from physiological signals enables the incorporation of multimodal signals into the design of intuitive interfaces that adapt to user behavior, situational context, and mental stress.
Generative AI for Personalized In-Vehicle Experiences and Behavior-Adaptive Interfaces: adopt an objective, evidence-based scholarly tone; present clear, formal arguments with structured progression and well-supported claims. Research design is delineated within a dedicated sub-section. The discussion covers user intent representation and preference modeling, design principles for behavior-adaptive interfaces, and multimodal interaction in automotive environments. Data acquisition, privacy, and ethical aspects receive attention. Data acquisition, security, and consent are addressed alongside fairness, transparency, and accountability considerations. System architecture is examined in terms of among on-device versus cloud-enabled personalization, real-time inference and latency constraints, and safety-critical integration. Evaluation encompasses user-centric methodologies, objective metrics for adaptivity and usability, and simulation and field trials. Finally, deployment scenarios and applications are explored, focusing on infotainment personalization, driver state and stress monitoring, and route and context-aware adaptation.
References
[1] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21), 610–623.
[2] Boesl, M. G., & Friedrich, M. (2020). Driver monitoring systems: Current state and future perspectives. IEEE Intelligent Transportation Systems Magazine, 12(3), 18–29.
[3] Boudier, G., Cour, M., & Revel, A. (2021). Adaptive automotive user interfaces: A review of context awareness and personalization. International Journal of Human–Computer Interaction, 37(9), 865–884.
[4] Chen, Y., Zhang, Y., Li, S., & Wang, H. (2024). Cockpit-Llama: Driver intent prediction in intelligent cockpits using large language models. Sensors, 24(XX), Article XXXX.
[5] Deng, J., Wang, Y., Li, C., & Zhao, X. (2025). Analysis of adaptive systems based on driver’s workload: Design guidelines and machine learning approaches. Applied Ergonomics, 115, Article 104129.
[6] Dritsas, E., & Sarigiannidis, P. (2025). Multimodal interaction, interfaces, and communication: Challenges and opportunities. Multimodal Technologies and Interaction, 9(1), Article 6.
[7] Dong, H., & Tran, T. T. M. (2024). A review on the development of the in-vehicle human–machine interfaces in driving automation: A design perspective. Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’24), 1–26.
[8] Dourish, P. (2004). What we talk about when we talk about context. Personal and Ubiquitous Computing, 8(1), 19–30.
[9] Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32–64.
[10] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672–2680.
[11] Grobelna, I. (2025). Design of automotive HMI: New challenges in enhancing user experience and safety. Applied Sciences, 15(10), Article 5572.
[12] Hancock, P. A., & Warm, J. S. (1989). A dynamic model of stress and sustained attention. Human Factors, 31(5), 519–537.
[13] Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human mental workload (pp. 139–183). North-Holland.
[14] Hoffman, J., Bender, E., Roberts, A., & Wiesner, M. (2023). Foundations and risks of conversational AI for safety-critical domains. ACM Computing Surveys, 55(XX), Article XXXX.
[15] International Organization for Standardization. (2019). Road vehicles — Ergonomic aspects of transport information and control systems — Specifications and compliance procedures for in-vehicle visual presentation (ISO 15008:2019). ISO.
[16] International Organization for Standardization. (2020). Road vehicles — Measurement of driver visual behaviour with respect to transport information and control systems — Part 1: Definitions and parameters (ISO 15007-1:2020). ISO.
[17] Katzouris, N., Artikis, A., & Paliouras, G. (2021). A survey on event recognition and behavior understanding for intelligent vehicles. IEEE Transactions on Intelligent Transportation Systems, 22(9), 5671–5688.
[18] Klein, G. (2008). Naturalistic decision making. Human Factors, 50(3), 456–460.
[19] Kuutti, K., & Bannon, L. J. (2014). The turn to practice in HCI: Towards a research agenda. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14), 3543–3552.
[20] Lamichhane, B. R., Sharma, A., & Karki, P. (2025). Context-aware decision making in autonomous vehicles: A review and research challenges. Results in Engineering, 19, Article 100XXX.
[21] Liu, Y. X., Kim, J., & Park, S. (2024). A study of trends in automotive personalized HMI design. Journal of the Ergonomics Society of Korea, 43(2), 1–18.
[22] Llach, C., Poveda-Reyes, S., & Molloy, R. (2023). Driver monitoring and adaptive assistance systems: A systematic review of sensing, inference, and interaction. IEEE Access, 11, 112345–112372.
[23] Ma, Y., Li, X., & Wang, J. (2022). Personalized driver modeling: A review of datasets, methods, and validation. IEEE Transactions on Intelligent Vehicles, 7(4), 659–675.
[24] McDuff, D., & Czerwinski, M. (2018). Affective computing in HCI: Strategies, methods, and challenges. Foundations and Trends in Human–Computer Interaction, 11(3), 141–184.
[25] Mobini Seraji, M. H., Rahmani, M., & Mohammadi, A. (2025). A state-of-the-art review on machine learning techniques for driving behavior analysis and prediction. Complex & Intelligent Systems, 11, Article 1988.
[26] Murray, N., & Kanki, B. (2021). Human performance in advanced vehicle automation: Workload, trust, and adaptation. Ergonomics, 64(8), 1012–1029.
[27] National Highway Traffic Safety Administration. (2024). Visual-manual NHTSA driver distraction guidelines for in-vehicle electronic devices (Report No. DOT HS 812 XXX). U.S. Department of Transportation.
[28] Norman, D. A. (2013). The design of everyday things (Revised and expanded ed.). Basic Books.
[29] Oviedo-Trespalacios, O., Haque, M. M., King, M., & Washington, S. (2019). Understanding the impacts of mobile phone distraction on driving performance: A systematic review. Transportation Research Part C: Emerging Technologies, 105, 416–434.
[30] Papanikolaou, M., Kalliris, G., & Moustakas, K. (2022). Multimodal in-vehicle interaction: A survey on speech, gesture, gaze, and haptic interfaces. ACM Computing Surveys, 55(6), 1–36.
[31] Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, and abuse. Human Factors, 39(2), 230–253.
[32] Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141.
[33] Roy, R., & Roy, A. (2025). Enhancing user experience through adaptive interfaces in autonomous vehicles. arXiv.
[34] Salazar-Calderón, L. A., García, J., & Muñoz, D. (2025). Framework, implementation, and user experience aspects of driver monitoring systems: A systematic review. Applied Sciences, 15(21), Article 11638.
[35] Shi, Y., Chen, T., Oviedo-Trespalacios, O., & Kim, I. (2025). Personalizing driver agent using large language models for driving safety and smarter human–machine interactions. Proceedings of the ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, 1–14.
[36] Stojmenovic, I., & Wen, S. (2020). The fog computing paradigm: Scenarios and security issues in connected vehicles. IEEE Communications Magazine, 58(9), 52–58.
[37] Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
[38] Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., & Lample, G. (2023). LLaMA: Open and efficient foundation language models. arXiv.
[39] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
[40] Weiser, M. (1991). The computer for the 21st century. Scientific American, 265(3), 94–104.
Additional Files
Published
Data Availability Statement
NONE
