World Journal of Engineering Research and Technology

Mohammed Sharfuddin*

ABSTRACT

High-definition (HD) maps are essential for the reliable operation of autonomous vehicles, offering precise geometric and semantic data about the road environment. However, their usefulness depends heavily on maintaining high quality and consistency. This paper presents a scalable data mining pipeline for real-time HD map quality monitoring, which continuously analyzes large-scale sensor and telemetry data to detect inconsistencies, anomalies, and degradation inmap data. The proposed system leverages big data frameworks, feature extraction, and anomaly detection models to identify quality issues, enabling proactive maintenance and updates of HD maps. Experiments show its effectiveness in large-scale deployments, improving both safety and operational efficiency.

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