Distributed Computing Paradigms for Scalable Big Data Architectures in Autonomous Driving Applications

Abstract

Recent advances in autonomous driving technology have led to an exponential growth in the data generated by connected vehicles, sensor networks, and high-resolution on-board cameras. Handling this deluge of information in real-time poses significant challenges in data collection, storage, and analysis. Distributed computing paradigms offer an efficient approach for addressing these challenges by leveraging parallelism, scalability, and resource-sharing capabilities across geographically dispersed infrastructures. This paper investigates the design and implementation of scalable big data architectures for autonomous driving applications, emphasizing the interplay between distributed computing frameworks and advanced data processing pipelines. Through rigorous mathematical analysis and empirical observations, the paper delves into the performance implications of employing various distributed paradigms, including streaming and batch processing models, alongside graph-based and matrix factorization approaches for large-scale sensor fusion. The discussion encompasses fault tolerance, task scheduling, and efficient load balancing methods that can handle the complexities of heterogeneous data and dynamic network conditions common in automotive environments. Furthermore, we provide a technical exploration of how scalable big data architectures can address latency-sensitive tasks such as real-time object detection, path planning, and situational awareness. We conclude with forward-looking insights on potential research directions, highlighting the significance of collaborative intelligence and the emerging roles of cloud-edge interplay in shaping the next generation of autonomous driving systems.