Context-Aware Autonomous Driving Decision Support System Based on Personalized Environmental Information Retrieval
Keywords:
autonomous driving, decision support systems, context-aware retrieval, personalization, environmental modeling, system architecture, fairness, governanceAbstract
The rapid advancement of autonomous driving technologies has shifted research focus from basic perception and control toward holistic decision support systems that reconcile environmental complexity with individual driver preferences. This paper presents a comprehensive analysis of a context-aware autonomous driving decision support system grounded in personalized environmental information retrieval. Moving beyond one-size-fits-all autonomy, we argue that robust deployment in mixed-traffic and diverse geographical settings demands architectures capable of dynamically fusing real-time sensor streams, external knowledge bases, and fine-grained driver profiles. We propose a multi-layered system design in which a personalized retrieval engine continuously mines, ranks, and adapts environmental cues according to driver-specific risk tolerances, route familiarity, and comfort parameters. The paper dissects structural trade-offs among on-board edge processing, cloud-based semantic reasoning, and hybrid split-inference architectures, foregrounding latency, energy, and privacy considerations. Through the lens of infrastructure governance, we examine how such systems can be operationalized across heterogeneous vehicle fleets while respecting regulatory fragmentation and evolving safety standards. Special attention is paid to fairness and bias emergence when personalization interacts with geospatial data disparities, and we outline mitigation strategies drawn from recent advances in federated learning and differential privacy. The discussion extends to long-term sustainability and maintenance, highlighting the need for continuous model retraining pipelines that accommodate shifting environmental distributions without eroding reliability. By integrating perspectives from retrieval-augmented generation, human-centered AI, and transportation policy, this work provides a blueprint for next-generation decision support frameworks that are simultaneously adaptive, explainable, and societally accountable.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
