Advances in Autonomous Vehicle Testing: The State of the Art and Future Outlook on Driving Datasets, Simulators, and Proving Grounds

ABSTRACT As autonomous driving technology rapidly advances, effective testing tools and methods become crucial. This paper comprehensively assesses the capabilities and limitations of publicly available autonomous driving datasets, simulators, and proving grounds, exploring their roles in testing autonomous vehicles. The aim of the paper is to analyze how these tools can assist in evaluating the capabilities of autonomous driving systems and their tasks in the actual verification process of autonomous driving technology. Furthermore, this paper discusses the challenges faced by autonomous driving datasets, simulators, and proving grounds, as well as future directions for development. Additionally, we propose the Integrated Testing Framework for Autonomous Vehicles (ITF‐AV), which unifies these tools into a cohesive testing strategy, providing guidance for researchers and practitioners to select appropriate methods based on specific testing needs.