AI-Driven Predictive Maintenance with Real-Time Contextual Data Fusion for Connected Vehicles

This paper puts forward a new AI-based predictive maintenance system for connected vehicles through real-time contextual data fusion that can enhance predictive maintenance and raise vehicle reliability. Conventional vehicle maintenance systems base their focus almost entirely on internal diagnostic data like engine health and wear indicators. This system integrates external conditions—road conditions, weather patterns, traffic density, and driver behavior—to make more holistic and context-aware predictions for maintenance requirements. With the use of edge computing, the system analyzes data locally on the vehicle, allowing for instantaneous feedback and low-latency decision-making for vital maintenance notifications. Additionally, the suggested system utilizes AI-driven algorithms to foresee vehicle breakdowns prior to failure, providing information about component wear and tear and suggesting repair actions in advance. It estimates the number of days to the next servicing. The system also offers automated service scheduling, linking the vehicle directly to dealer networks for effortless repair reservations according to forecasted maintenance requirements. This solution should decrease unplanned vehicle breakdowns, maximize servicing schedules, decrease maintenance costs, and enhance general vehicle lifespan. The combination of predictive maintenance and contextual awareness is a key evolutionary step in the automotive network technologies, with opportunities for both consumer vehicles and fleets.