Adaptive Personalization and AI-Based Driver Profiling in Software-Defined Vehicles: A Comprehensive Technical Analysis

Abstract

Software-Defined Vehicles allow an unparalleled level of personalization by integrating artificial intelligence and distributed computing structures, making the conventional comfort systems adaptive and intelligent subsystems. The framework introduced combines multimodal sensing technologies that capture biometric, positional, and environmental measurements with machine learning inferences located on hierarchical edge computing platforms to produce autonomous driver recognition and preference-based comfort settings. Hybrid convolutional-recurrent neural network models operate on the spatial and temporal pattern of behavior of occupants to create new occupant signatures, allowing automatic rearrangement of seat assignments, steering column layouts, and environmental controls with automated adjustments. The architectural design considers the most important automotive needs, such as compliance with functional safety by using multi-layered protective features, the real-time performance constraint by distributed edge inference, and data privacy maintenance by localized processing to ensure that sensitive biometric data is not transferred to non-secure processing environments. The latest automotive software standards are compatible with the AUTOSAR Adaptive Platform standards, with integrated support to enable the system to evolve with the use of over-the-air updates. The presented structure brings the automotive personalization to the next level by providing the path to the intelligent, context-aware environments where the personal preferences of the occupants are always constantly learned and updated without any compromise to the safety standards and adherence to the regulations.