Decision-fatigue mitigation in Automotive HMI System

Abstract - With advent of digital displays in driver cabins in commercial vehicles, the drivers are getting offered with many features. Due to availability of vast number of features, drivers face decision fatigue in choosing the appropriate features. Many are unaware of all available functionalities displayed in the HMI System, leading to a bare minimum usage or complete neglect of helpful features. This not only affects the driving efficiency but also increases cognitive load, especially in complex driving scenarios. Instead of manually choosing between multiple settings, the driver can simply activate a recommendation mode, allowing the system to optimize selections dynamically. Novelty of this proposal focuses on introducing Intelligence in HMI Systems in such a way that it will maximize the usage index and reduce decision fatigue in drivers. To achieve this, an AI-driven recommendation system is introduced, based on User Customization and Adaptive Learning Model along with ML Algorithms which will utilize data derived from driver preferences, road conditions, and vehicle dynamics to recommend the most suitable features. This enhances ease of use, improves safety, and ensures that drivers receive the most relevant assistance without unnecessary distractions.