Paper 2026/1043

Safe and Secure Autonomy by Machine Learning Techniques: A Systematic Literature Review

Abstract

This systematic literature review investigates recent advancements in machine learning techniques aimed at ensuring safety and security in autonomous systems. By analyzing 129 scholarly articles published between 2018 and 2024, we identify dominant methodologies, prominent machine learning techniques, and key application domains. As the paper illustrates, Reinforcement Learning (RL) and Deep Learning (DL) have emerged as leading approaches, especially in contexts requiring real-time adaptation such as autonomous vehicles, Unmanned Aerial Vehicles (UAVs) and robotics. Our analysis reveals significant research trends, including a strong emphasis on safe motion, prediction, vulnerability detection, and security assurance. We highlight current research gaps, notably the need for standardized benchmarks and improved model robustness under adversarial conditions. This review provides valuable insights and future research directions, serving as a comprehensive guide for young researchers entering the field of safe and secure autonomy.