In recent years, intelligent vehicles operating in urban environments have demonstrated the capability to autonomously execute various tasks, such as object detection, lane detection, segmentation, etc. This advancement is facilitated by the extensive datasets accumulated by researchers, alongside advancements in intelligent algorithms, as well as significant breakthroughs in software and hardware. However, within the autonomous driving community, there is a scarcity of data regarding scenarios encountered in mining environments. This scarcity presents challenges and bottlenecks for the advancement of comprehensive autonomous driving systems and autonomousoperations. Although we previously released our dataset, AutoMine, which includes over 18 hours of driving data in open-pit mines, its scope is limited to two specific tasks. This scope limitation impedes the training and validation of the majority of algorithms for different tasks in this particular scenario. To broaden the scope of autonomous driving visual tasks in mining environments, we have curated a diverse collection encompassing multiple tasks, including detection, segmentation, tracking, etc. Additionally, we have established benchmarks and set up baselines for the aforementioned multiple tasks. By comparing the performance differences of visual algorithms between mining areas and other scenarios, we demonstrate the distinctive characteristics of mining regions in an intuitive manner. We have developed a suite of tools for converting annotated data into the standardized format used in existing driving datasets. Our aspiration is to establish data and benchmark foundations, supporting research endeavors in intelligent transportation within mining environments and autonomous driving in comprehensive scenarios. Our project website can be seen in AutoMine, and the dataset can be downloaded via AutoMine-Benchmark.