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Owing to the merits of early safety and reliability guarantee, autonomousdriving virtual testing has recently gains increasing attention compared withclosed-loop testing in real scenarios. Although the availability and quality ofautonomous driving datasets and toolsets are the premise to diagnose theautonomous driving system bottlenecks and improve the system performance, dueto the diversity and privacy of the datasets and toolsets, collecting andfeaturing the perspective and quality of them become not only time-consumingbut also increasingly challenging. This paper first proposes a SystematicLiterature review approach for Autonomous driving tests (SLA), then presents anoverview of existing publicly available datasets and toolsets from 2000 to2020. Quantitative findings with the scenarios concerned, perspectives andtrend inferences and suggestions with 35 automated driving test tool sets and70 test data sets are also presented. To the best of our knowledge, we are thefirst to perform such recent empirical survey on both the datasets and toolsetsusing a SLA based survey approach. Our multifaceted analyses and new findingsnot only reveal insights that we believe are useful for system designers,practitioners and users, but also can promote more researches on a systematicsurvey analysis in autonomous driving surveys on dataset and toolsets.

Perspective, Survey and Trends: Public Driving Datasets and Toolsets for Autonomous Driving Virtual Test