A Review on Testing Approaches for Autonomous Driving Systems Based on Metamorphic Testing and Fuzzing Testing

ABSTRACT With the rapid development of deep learning–based autonomous driving technology, how to verify and improve the robustness and safety of autonomous driving models and systems has become a research hotspot in academia and industry. The environment faced by autonomous driving systems is diverse, dynamic, and complex, which makes its testing work face many problems such as high‐dimensional input data, environmental uncertainty, and black‐box characteristics of models. Therefore, this paper systematically sorts out the current testing methods for autonomous driving models and systems, focusing on two mainstream testing methods: metamorphic testing and fuzzing testing. In terms of metamorphic testing, this paper deeply analyzes the design challenges of Test Oracle and explores how to effectively transform complex scenarios to ensure the comprehensiveness and accuracy of the test. In terms of fuzzing testing, this paper focuses on the application of optimization search technology in generating test scenarios, especially the effects of technologies such as genetic algorithms and neural networks in discovering model failure points and improving system robustness. Furthermore, this paper also discusses how to effectively combine these two mainstream testing methods, summarizes and generalizes the commonly used datasets, models and simulation simulators in these two tests, and also discusses the key points and improvement suggestions of each method. It strives to fully present the application status and development trend of current testing technology. A comparative analysis of representative methods is conducted using metrics like violation rate and unique violations, revealing performance trends under unified conditions. Finally, this paper points out the limitations of current research and proposes possible directions for future research. This article aims to provide reference and guidance for in‐depth research on autonomous driving model testing technology and promote the further development of autonomous driving systems in terms of safety and reliability.