In recent years, the rapid development of deep neural networks and their application technologies has propelled autonomous driving systems (ADS) towards commercialisation. However, due to the high safety requirements of ADSs, ensuring their safety and reliability remains a critical challenge in software engineering. Existing testing methods focus on simple driving scenarios with few traffic participants, neglecting the impact of high traffic density on ADS driving performance. This paper presents an empirical study exploring how traffic density affects ADS behaviour. We developed a testing framework using two open-source ADSs to generate scenarios with varying traffic densities in a high-fidelity simulator. Our results indicate that changes in traffic density significantly affect ADS performance. Different traffic densities reveal various types of safety violations and help identify potential design flaws in ADSs. This study highlights the importance of considering traffic density in ADS testing and contributes to a better understanding of ADS performance under different traffic conditions.