@article{Abhiram_2026, title={Integration of Artificial Intelligence Algorithms into On-Board Vehicle Diagnostic and Control Systems}, volume={12}, url={http://dx.doi.org/10.22161/ijaems.122.15}, DOI={10.22161/ijaems.122.15}, abstractNote={<jats:p>The article examines architectural and engineering approaches for integrating artificial intelligence algorithms into on-board vehicle diagnostic and control systems. Relevance follows from software-defined vehicles, Electronic Control Unit (ECU) density, and the need to convert On-Board Diagnostics II (OBD-II) and Controller Area Network (CAN) telemetry into dependable decisions under real-time and safety constraints. Novelty consists of a synthesis that links data acquisition and preprocessing, model families for fault prediction, multi-fault diagnosis, and intrusion detection, and deployment patterns spanning in-vehicle compute with cloud/edge services. The objective is to systematize design decisions that yield robust diagnostics, interpretable outputs for service workflows, and controlled mitigation actions. Methods comprise comparative analysis of recent publications, taxonomy building across task types, and architectural decomposition of pipelines. The source base covers OBD-II machine learning, deep-learning predictive maintenance, attention-based fault prediction, explainable multi-fault diagnosis, CAN intrusion detection, connected-vehicle platforms, deterministic AUTOSAR Adaptive communication, and ML-aware functional-safety extensions. The article targets automotive software engineers, data scientists, and platform architects.</jats:p>}, number={2}, journal={International Journal of Advanced Engineering, Management and Science}, publisher={AI Publications}, author={Abhiram, Madugula}, year={2026}, pages={128–134} }