For the safety assessment of autonomous driving systems (ADS), simulation testing has become an important complementary technique to physical road testing. In essence, simulation testing is a scenario-driven approach, whose effectiveness is highly dependent on the quality of given simulation scenarios. Moreover, simulation scenarios should be encoded into well-formatted files, otherwise, ADS simulation platforms cannot take them as inputs. Without large public datasets of simulation scenario files, both industry and academic applications of ADS simulation testing are hindered.

To fill this gap, we propose a transformation-based approach SCTrans to construct simulation scenario files, utilizing existing traffic scenario datasets (i.e., naturalistic movement of road users recorded on public roads) as data sources. Specifically, we try to transform existing traffic scenario recording files into simulation scenario files that are compatible with the most advanced ADS simulation platforms, and this task is formalized as a Model Transformation Problem. Following this idea, we construct a dataset consisting of over 1,900 diverse simulation scenarios, each of which can be directly used to test the state-of-the-art ADSs (i.e., Apollo and Autoware) via high-fidelity simulators (i.e., Carla and LGSVL). To further demonstrate the utility of our dataset, we showcase that it can boost the collision-finding capability of existing simulation-based ADS fuzzers, helping identify about 7 times more unique ADS-involved collisions within the same time period. By analyzing these collisions at the code level, we identify 9 safety-critical bugs of Apollo and Autoware, each of which can be stably exploited to cause vehicle crashes. Till now, 4 of them have been confirmed.