Bridging the Gap between Real-world and Synthetic Images for Testing Autonomous Driving Systems
This work addresses a critical issue for autonomous driving developers by improving the reliability and efficiency of simulation-based testing, though it is incremental as it builds on existing domain translation methods.
The paper tackles the problem of distribution mismatch between real-world training and synthetic test images for autonomous driving systems, showing that domain translators like SAEVAE significantly narrow the accuracy gap and do not compromise test diversity or fault detection, with SAEVAE incurring negligible simulation overhead.
Deep Neural Networks (DNNs) for Autonomous Driving Systems (ADS) are typically trained on real-world images and tested using synthetic simulator images. This approach results in training and test datasets with dissimilar distributions, which can potentially lead to erroneously decreased test accuracy. To address this issue, the literature suggests applying domain-to-domain translators to test datasets to bring them closer to the training datasets. However, translating images used for testing may unpredictably affect the reliability, effectiveness and efficiency of the testing process. Hence, this paper investigates the following questions in the context of ADS: Could translators reduce the effectiveness of images used for ADS-DNN testing and their ability to reveal faults in ADS-DNNs? Can translators result in excessive time overhead during simulation-based testing? To address these questions, we consider three domain-to-domain translators: CycleGAN and neural style transfer, from the literature, and SAEVAE, our proposed translator. Our results for two critical ADS tasks -- lane keeping and object detection -- indicate that translators significantly narrow the gap in ADS test accuracy caused by distribution dissimilarities between training and test data, with SAEVAE outperforming the other two translators. We show that, based on the recent diversity, coverage, and fault-revealing ability metrics for testing deep-learning systems, translators do not compromise the diversity and the coverage of test data, nor do they lead to revealing fewer faults in ADS-DNNs. Further, among the translators considered, SAEVAE incurs a negligible overhead in simulation time and can be efficiently integrated into simulation-based testing. Finally, we show that translators increase the correlation between offline and simulation-based testing results, which can help reduce the cost of simulation-based testing.