Development of an AI-driven Predictive Model for Vehicle Feature Optimization & Diagnostic Tuning Using an Extended Kalman Filter (EKF) State Estimator

Abstract

Modern connected and automated vehicles rely on increasingly complex sensing, perception, and estimation architectures to infer internal system states and predict future behavior from high-volume, noisy, and nonlinear sensor data. Similar challenges arise across a broad class of engineered systems and intelligent devices, including robotic and autonomous platforms, where accurate perception and prediction under uncertainty are essential for safe, reliable, and sustained operation. As these systems age or operate across diverse environments and usage conditions, discrepancies emerge between modeled dynamics and observed measurements due to sensor drift, component degradation, environmental variability, and unmodeled nonlinear effects. These discrepancies degrade state estimation accuracy, destabilize diagnostic interpretations, and reduce the reliability of perception-driven and prediction-driven decision-making. This dissertation develops an adaptive perception and prediction framework that integrates nonlinear state estimation with data-driven learning to support continuous v system understanding under uncertainty. The proposed approach combines a model-based estimation layer that captures nominal system dynamics and enforces physical consistency with a learning-based predictive layer that models residual nonlinearities, long-term signal drift, and cross-sensor dependencies that are difficult to represent analytically. Through recursive estimation and forward prediction, the framework enables simultaneous inference of latent system states and anticipation of future system behavior. An adaptive tuning mechanism further refines perception and diagnostic interpretations by dynamically adjusting internal thresholds and confidence bounds based on predicted behavior, operational context, historical system performance, and evolving system characteristics. The framework is evaluated using both real-world and simulated datasets representative of connected vehicle networks and autonomous sensing systems. The evaluation demonstrates accurate estimation and prediction of motion states, actuation responses, thermal behavior, and system health indicators under varying operating conditions. Experimental results show improved estimation accuracy, enhanced predictive stability, faster convergence, and increased robustness to noise and sensor degradation when compared to conventional model-based estimators, standalone learning-based approaches, and static diagnostic frameworks. The proposed methodology further enables more consistent perception and diagnostic interpretation across heterogeneous systems, devices, and operating environments. This research contributes a scalable and generalizable methodology for integrated perception and prediction in modern connected vehicles and autonomous systems. By vi enabling estimation and diagnostic models to continuously learn, adapt, and recalibrate over time, the proposed framework supports long-term autonomy, resilience, and reliability in complex cyber-physical systems operating under uncertainty, change, and degradation. The proposed framework supports next-generation predictive maintenance strategies in connected fleets and contributes to the realization of adaptive, self-calibrating diagnostic systems for intelligent vehicles.