Volume 15, Number 5

Neuro-Adaptive AI for Dynamic Distraction Mitigation in Autonomous Vehicle Environments

  Authors

Vivek Ghulaxe, Publix Technology, USA

  Abstract

As autonomous vehicles develop, driver distraction becomes even more crucial as it affects both safety and operational efficiency. In this work, we investigate the gamut of new AI tools for combating and processing visual distraction scenarios within autonomous vehicles. This includes AI-based driver monitoring systems to determine the level of attention, visual distraction classification with deep learning models, augmented reality head-up displays for focal projection of critical information and gesture/voice-controlled interfaces are used in order to reduce visual interactions. This also includes how predictive analytics; adaptive user interfaces and personalized distraction mitigation programs will see AI improve driver focus and thus safety. These advanced systems are designed to provide a safer and more efficient driving experience in the emerging era of autonomous capabilities by leveraging the scalability of advanced driver-assistance technologies.

  Keywords

Autonomous Vehicles, Driver Visual Distraction, AI Applications in Automotive, Driver Monitoring Systems (DMS), Deep Learning in Autonomous Vehicles, Augmented Reality Head-Up Displays (AR HUDs), Gesture Recognition in Vehicles, Predictive Analytics for Driver Safety, Adaptive User Interface Design, Driver Attention Detection, AI in Driver Safety Enhancement, Visual Distraction Classification, Machine Learning for Driver Assistance, In-Cabin AI Interfaces, Real-Time Distraction Detection, Cognitive Load Detection, Context-Aware AI Systems, Human-Machine Interaction in Vehicles, Multimodal Sensor Fusion in Vehicles, AI-Enhanced Driver Assistance Systems, Robotic Process Automation (RPA)