Generative AI for Adaptive In-Vehicle Experiences
Keywords:
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.
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