Autonomous driving is the future development trend of the automotive industry, while autonomous vehicles mainly rely on interconnected systems and software that control their operation. System software vulnerabilities lead to serious safety hazards for vehicles. Therefore, automatic driving safety test is an important link to further improve the safety of autonomous vehicle. Fuzzing, as an automated vulnerability testing technology, exhibits outstanding vulnerability exploration capabilities when dealing with complex software systems. Aiming to explore the potential of fuzzing for widespread application in autonomous driving systems, this paper provides a systematic summary of widely used open-source fuzzing tools. Through an in-depth analysis of the characteristics of autonomous driving systems, the study identifies key challenges currently faced by research in this field, including: 1) difficulty in comprehensively considering input dimensions; 2) challenges in uncovering issues related to multifunctional collaborative concurrency; 3) mismatch of security issue categories. In response to these challenges, the research proposes corresponding recommendations, providing guidance for future related research.