Machine Learning in Autonomous Vehicles, Drones, and Robotics: A Comprehensive Review of Algorithms and Applications

Abstract

Machine Learning (ML) has emerged as a pivotal technology in the advancement of autonomous systems, including self-driving vehicles, unmanned aerial drones, and intelligent robotic platforms. These systems rely heavily on ML to interpret sensor data, make decisions, and execute tasks with minimal human intervention. This review explores the broad landscape of ML algorithms and their applications across key domains, categorizing them into supervised, unsupervised, reinforcement, and deep learning paradigms. Each learning type contributes uniquely supervised learning for classification and regression, unsupervised learning for clustering and dimensionality reduction, reinforcement learning for goal-oriented decision-making, and deep learning for hierarchical feature extraction from complex data. The integration of ML spans core functions of autonomous systems, including perception, control, planning, and human-robot interaction. For instance, ML-driven perception enables systems to detect and interpret objects, environments, and dynamic changes through computer vision and sensor fusion. In decision-making, ML facilitates real-time predictions and adaptive responses to uncertain scenarios. Control strategies also benefit from ML by learning optimal policies and behaviors that improve efficiency and responsiveness. Furthermore, ML enhances human-robot collaboration by enabling natural language understanding, gesture recognition, and adaptive interaction based on user intent. As autonomous systems are increasingly deployed in real-world, unstructured environments, the importance of ML continues to grow. Its ability to process vast and diverse datasets, learn from experience, and generalize across tasks is critical to improving safety, reliability, and autonomy. However, several challenges remain, including limited availability of high-quality annotated data, ensuring robustness under edge-case conditions, and addressing ethical concerns such as bias, transparency, and accountability. This paper highlights the inherently interdisciplinary nature of ML in autonomous systems, underscoring the necessity for collaboration across robotics, artificial intelligence, control engineering, and human-computer interaction. Finally, we propose future directions aimed at enhancing resilience, generalization, and human-centered design in next-generation autonomous technologies.