Advancements in Scalable Deep Learning Models for Real-Time Decision Making in Autonomous Systems

Abstract

The rapid advancements in autonomous systems, such as self-driving cars, drones, and robotics, have highlighted the need for scalable deep learning models that can support real-time decision-making. However, the implementation of deep learning in these systems is challenged by issues of scalability, computational efficiency, and real-time data processing. This paper explores the latest techniques for scaling deep learning models to meet the stringent demands of autonomous systems. We discuss recent advancements in reinforcement learning, model optimization, and edge computing, focusing on their ability to facilitate decision-making in resource-constrained environments. Additionally, we examine case studies from various autonomous systems to highlight the application of scalable models in real-world settings. The findings suggest that future advancements in model compression, distributed learning, and hybrid architectures will be key to overcoming current challenges. The paper concludes with potential research directions, including the integration of quantum computing and neuromorphic systems, to further enhance the scalability and efficiency of real-time decision-making in autonomous systems.