Recently, extensive research has been conducted on generating virtual training data in a digital twin-based simulator to reduce the time and cost associated with acquiring high-quality training data necessary for autonomous driving. In this study, we propose an efficient method for generating synthetic training datasets for autonomous driving by combining real-world and virtual training data. Specifically, we propose a method for implementing a digital twin-based autonomous driving simulator, collecting large amounts of virtual training data using its camera sensor, and generating synthetic training datasets by combining virtual and real-world training data in various ratios. The effectiveness of these datasets is then validated in deep learning applications, particularly for detecting traffic lights and signal information. Validation results indicate that synthetic training datasets significantly improve deep learning performance, provided they include a sufficient amount of real-world training data to avoid class imbalance issues. We conclude that synthetic training datasets generated using a digital twin-based simulator are cost-effective and practical for deep learning applications.