Machine Learning Optimization in Large-Scale Sensor Data Environments for Autonomous Driving Systems

Abstract

This paper investigates advanced machine learning optimization techniques for handling large-scale sensor data in autonomous driving systems. With the increasing adoption of autonomous vehicles, the volume and variety of sensor data, such as LiDAR, radar, cameras, and inertial measurement units, have expanded dramatically. Effectively processing and analyzing this sensor data is crucial for accurate perception, localization, prediction, and control. The primary goal of this work is to outline a comprehensive methodology that integrates scalable machine learning algorithms, state-of-the-art optimization frameworks, and robust linear algebraic formulations to process high-dimensional sensor inputs in real time. The discussion emphasizes novel approaches for reducing computational overhead while retaining high levels of accuracy, reliability, and interpretability. We present mathematical models that address the challenges of heterogeneous data fusion, latency constraints, and resource limitations, as well as techniques for ensuring stability and robustness in dynamic driving environments. We highlight the interplay of distributed computing architectures, gradient-based optimization methods, and specialized regularization schemes. Extensive experimental validation demonstrates that the proposed framework enhances autonomy by enabling the system to adapt and learn from complex, ever-changing environments at scale. These findings have broad implications for both academic research and commercial systems, offering a path toward safer, more efficient, and more intelligent self-driving vehicles capable of responding effectively to real-world conditions.