Leveraging Scalable Cloud Infrastructure for Autonomous Driving Data Lakes and Real-Time Decision Making

Autonomous driving technology relies heavily on the effective management of vast datasets generated by various sensors and vehicle systems. As such, leveraging scalable cloud infrastructure becomes paramount for improving data handling and decision-making capabilities. In this paper, we introduce the Autonomous Driving Data Lakes (ADDL) framework, designed to streamline the storage, retrieval, and processing of extensive driving data in real-time. By utilizing cloud technology, ADDL ensures tight integration of data from diverse sources to enhance situational awareness for autonomous systems. Our architecture features robust data pipelines that support real-time analytics and machine learning applications, which are crucial for timely and accurate decision-making. Extensive experiments with largescale datasets demonstrate how our approach significantly boosts processing efficiency, data accessibility, and decision-making reliability. The findings highlight advancements in autonomous driving technologies, addressing the challenges associated with data management and enhancing operational effectiveness in changing driving environments.