World Journal of Engineering Research and Technology

*Ashraf Ali Khan Mohammed

ABSTRACT

This research paper presents an advanced data analytics framework designed to enhance real-time decision-making capabilities in autonomous systems. With a particular focus on autonomous vehicle applications, the framework integrates streaming data platforms, machine learning models, and feedback-driven mechanisms to improve the accuracy and scalability of HD map validation processes. The study addresses key challenges such as data heterogeneity, volume, and latency, proposing a modular architecture that supports continuousdata ingestion, feature extraction, anomaly detection, and real-time insight generation. A case study on HD map quality monitoring demonstrates the framework’s effectiveness in improving validation precision, reducing processing latency, and enabling self-learning through human feedback. The results highlight the potential of advanced analytics in fostering safer and more adaptive autonomous navigation systems. The paper builds on prior research contributions by incorporating AI-powered detection, scalable data mining pipelines, and real-time analytics engines, thereby offering a comprehensive solution for intelligent, data-driven automation.

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