  Owing to the merits of early safety and reliability guarantee, autonomous
driving virtual testing has recently gains increasing attention compared with
closed-loop testing in real scenarios. Although the availability and quality of
autonomous driving datasets and toolsets are the premise to diagnose the
autonomous driving system bottlenecks and improve the system performance, due
to the diversity and privacy of the datasets and toolsets, collecting and
featuring the perspective and quality of them become not only time-consuming
but also increasingly challenging. This paper first proposes a Systematic
Literature review approach for Autonomous driving tests (SLA), then presents an
overview of existing publicly available datasets and toolsets from 2000 to
2020. Quantitative findings with the scenarios concerned, perspectives and
trend inferences and suggestions with 35 automated driving test tool sets and
70 test data sets are also presented. To the best of our knowledge, we are the
first to perform such recent empirical survey on both the datasets and toolsets
using a SLA based survey approach. Our multifaceted analyses and new findings
not only reveal insights that we believe are useful for system designers,
practitioners and users, but also can promote more researches on a systematic
survey analysis in autonomous driving surveys on dataset and toolsets.
