Mapping the Machine Learning Landscape in Autonomous Vehicles: A Scientometric Review of Research Trends, Applications, Challenges, and Future Directions

Abstract

In recent years, Machine Learning (ML) has emerged as a transformative technology in the field of Autonomous Vehicles and Autonomous Driving , fundamentally reshaping vehicle perception, decision-making, and control mechanisms. The integration of ML techniques is pivotal to advancing the safety, efficiency, and adaptability of AV systems, driving significant academic and industrial interest. Over the past decade, there has been an unprecedented surge in research output exploring diverse ML methodologies tailored to AV applications. However, despite this exponential growth, there remains a critical gap in systematically understanding the evolution, intellectual landscape, research hotspots, and collaborative dynamics of this rapidly expanding domain, limiting scholars’ and practitioners’ ability to grasp the current state of knowledge and identify emerging trends.To fill this gap, this study presents an extensive bibliometric and conceptual structure analysis, offering a consolidated view of the field’s development from 2011 to 2025. The results uncover exponential growth in scholarly output, largely driven by the proliferation of deep learning, reinforcement learning, and federated learning approaches. The intellectual landscape of ML in AVs is organized around five dominant thematic clusters: perception, planning and decision-making, control systems, safety and robustness, and connected or federated systems.Additionally, an in-depth qualitative review of the most influential publications highlights critical methodological contributions while exposing persistent challenges related to model generalization, safety validation, and interpretability.It concludes with a forward-looking agenda, emphasizing interdisciplinary research, robust validation frameworks, and the integration of emerging technologies such as Digital Twins, Edge AI, Quantum Computing, and Large Language Models (LLMs) to enhance intelligence, scalability, reasoning, adaptability, and human-machine collaboration in future AV systems.

Ähnliche Arbeiten