AI-Driven Infotainment: Advancing Contextual and Personalized Automotive Systems

Authors: Ronak Indrasinh Kosamia

DOI: https://doi.org/10.5281/zenodo.15086591

Short DOI: https://doi.org/g89tf2

Country: United States

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Abstract: In-vehicle infotainment has traditionally delivered static user interfaces and limited adaptability, despite the automotive industry’s broader push toward connected and semi-autonomous systems. Recent advances in artificial intelligence (AI) and machine learning (ML) suggest that infotainment can evolve into a contextually aware ecosystem—adjusting display layouts, anticipating user needs, and coordinating multi-regional or multi-modal features. This paper outlines a framework for AI-driven infotainment that unifies occupant classification, environmental triggers, and cloud-based analytics to provide real-time personalization. We focus on occupant-centric gating of features to reduce driver distraction, integrate predictive maintenance alerts at opportune moments, and exploit partial offline caching for robust operation in connectivity-limited regions. Preliminary evidence, including pilot user tests, indicates that occupant-based UI adaptation can bolster user acceptance while safeguarding against information overload. The approach also highlights potential synergy with e-commerce microservices, advanced route planning, and region-specific customizations. By bridging occupant recognition, environment variables, and learning-based modules, the proposed system underscores how future automotive infotainment can deliver higher levels of convenience, safety, and global scalability.

Keywords: AI-driven infotainment, automotive systems, occupant classification, predictive maintenance, driver distraction, connected vehicles, microservices, offline caching, machine learning, multi-regional deployment

Paper Id: 232280

Published On: 2019-09-24

Published In: Volume 7, Issue 5, September-October 2019

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