PMScenes: A Parallel Mine Dataset for Autonomous Driving in Surface Mines

Synthetic data, complementary with real-world datasets, have shown their effectiveness for autonomous driving in urban scenarios. However, for specific and complex environments, such as surface mines, which often involve extreme weather and corner cases, synthetic datasets are notably scarce. In this article, PMScenes, the first synthetic dataset for autonomous driving in surface mines, is built upon the previously public AutoMine dataset. It extends the available data by constructing corner case scenarios, such as unusual intrusions; collisions; and extreme weather scenarios including rain, blizzards, fog, and dust storms—situations that would bring significant safety risks and are hard to capture in the real world. Our article explores the enhancement methods for synthetic to real for perception tasks of autonomous driving under different challenging scenarios. The outcomes demonstrate that PMScenes significantly enhances the performance across a variety of perception tasks compared with the results obtained when training only with real-world datasets. PMScenes is released at https://github.com/PMScenes/PMScenes.