Zohdy, MohamedTaleb, YamenLouis, StevenKaur, AmanpreetAlwerfali, Daw2026-07-172026-07-172026-01-01https://hdl.handle.net/10323/22138This dissertation presents a cloud-based Kalman filter framework designed to reduce noise in diagnostic trouble code (DTC) signals and improve First Notice of Loss (FNOL) accuracy within connected vehicle systems. Modern vehicles generate extensive CAN-based diagnostic and sensor data, yet these signals are often influenced by noise, transient disturbances, and low-severity impacts that can trigger false DTC activations and unnecessary FNOL alerts. These inaccuracies create operational inefficiencies for automative OEMs and contribute to increased claim volumes, higher warranty expenses, and inconsistent assessments for insurance partners.To address these challenges, this research develops and evaluates a Kalman filter-based noise reduction model executed in the cloud. Using CAN signal files collected from real vehicles testing, the model estimations the true underlying diagnostic state by filtering out random fluctuations, road disturbances, and non-critical impact signatures. The framework also incorporates a smart contract mechanism that securely timestamps validated FNOL events, ensuring trusted, tamper-resistant data exchange between OEM and insurance companies. Validation is conduction using CAN logs, simulated DTC fault scenarios, and synthetic impact profiles. Results demonstrate that the proposed model significantly reduce false-positive FNOL detection and accurately differentiates true collision events from low-impact disturbances. The integration of smart contracts provides an automated and verifiable method for sharing incident data with insurers, enabling faster claim triage, reduced fraud risk, and more precise repair cost estimation. This work offers a scalable, cloud driven methodology that enhances vehicle diagnostic reliability, improves FNOL accuracy and strengthens collaboration between OEMs and insurance companies. The framework lays the foundation for a more secure, transparent, and data-driven ecosystem in modern mobility.