Journal of Advances in Developmental Research

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Runtime Personalization of In-Vehicle Assistants using Vehicle Context and Driver Profiles

Author(s) Ronak Indrasinh Kosamia
Country United States
Abstract In-vehicle assistants (IVAs) are increasingly central to enhancing driver experience, yet most existing systems rely heavily on cloud-based personalization, which poses latency, connectivity, and privacy concerns. This project addresses the challenge of delivering runtime personalization in IVAs through a lightweight, onboard adaptive intelligence layer. The proposed system modifies speech, visual overlays, and prompts dynamically, using real-time inputs such as vehicle context (e.g., speed, driving mode) and driver profiles (e.g., user role, behavior patterns). A modular rule-based engine combined with embedded machine learning allows adaptation without external server dependency, enabling consistent performance even in low-connectivity scenarios. The architecture supports three personalization tiers—trip-type, vehicle-state, and user-role—and demonstrates a latency reduction of up to 42% compared to conventional cloud-reliant methods. Early prototype testing in a simulated vehicle environment indicates a 23% improvement in driver engagement scores and smoother human-machine interaction. This approach bridges infotainment usability with local intelligence, paving the way for future personalization strategies that prioritize responsiveness, contextual relevance, and data sovereignty.
Keywords Runtime personalization, in-vehicle assistants, driver profiling, vehicle context, adaptive overlays, onboard machine learning.
Field Engineering
Published In Volume 12, Issue 2, July-December 2021
Published On 2021-12-03
Cite This Runtime Personalization of In-Vehicle Assistants using Vehicle Context and Driver Profiles - Ronak Indrasinh Kosamia - IJAIDR Volume 12, Issue 2, July-December 2021. DOI 10.71097/IJAIDR.v12.i2.1555
DOI https://doi.org/10.71097/IJAIDR.v12.i2.1555
Short DOI https://doi.org/g92pdr
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