A Novel approach to Services and Fault Prediction Diagnostics of Artificial Intelligence based Smart Driver Assistant of Two-Wheeler

This paper presents a novel approach to services and fault prediction diagnostics for an AI-based smart driver assistant system designed for two-wheelers. The system comprises a hardware ECU (Electronic Control Unit) and a Flutter-based application. Current commercial advanced driver assistance systems (ADAS) in the Indian market are limited in capability, primarily offering basic GPS navigation and simple digital dashboards. The purpose of the proposed AI- ADAS system is to integrate multi-modal sensor-based features for real-time fault detection and alert systems, navigation assistance, and predictive diagnostics. Our methodology involves designing and implementing a low-cost, mountable smart digital Flutter-based ADAS application with extensive testing and validation to ensure its efficacy in diverse riding environments. The salient features of the proposed system are Two-wheeler Health Diagnostics, Early Weather Warnings, Two-wheeler health Prediction, Rider’s Risk Analysis and Behaviour Prediction, and Improved Driver Awareness. These enhancements aim to significantly improve rider safety and overall experience.