Many companies aim for delivering systems for autonomous driving reaching out for SAE Level-5. As these systems run muchmore complex software than typical premium cars of today, a thorough testing strategy is needed. Early prototyping of such systems canbe supported using recorded data from on-board and surrounding sensors as long as open-loop testing is applicable; later, though,closed-loop testing is necessary – either by testing on the real vehicle or by using a virtual testing environment. This paper is asubstantial extension of our work presented at the 2017 IEEE International Conference on Intelligent Transportation Systems (ITSC)surveying the area of publicly available driving datasets. Our previous results are extended by additional datasets and complemented witha summary of publicly available virtual testing environments to enable closed-loop testing. As such, a steadily growing number of 36datasets for open-loop testing and 21 virtual testing environments for closed-loop testing have been surveyed. Thus, conducting researchtowards autonomous driving is significantly supported from complementary community efforts: A growing number of publicly accessibledatasets allow for experiments with perception approaches, while virtual testing environments enable end-to-end simulations.
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