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Test Your Self-Driving Algorithm: An Overview of Publicly Available Driving Datasets and Virtual Testing Environments

Artikel i vetenskaplig tidskrift
Författare Yue Kang
Hang Yin
Christian Berger
Publicerad i IEEE Transactions on Intelligent Vehicles
ISSN 2379-8858
Publiceringsår 2019
Publicerad vid Institutionen för data- och informationsteknik, Software Engineering (GU)
Språk en
Länkar https://ieeexplore.ieee.org/stamp/s...
Ämneskategorier Datorseende och robotik (autonoma system), Programvaruteknik

Sammanfattning

Many companies aim for delivering systems for autonomous driving reaching out for SAE Level-5. As these systems run much more complex software than typical premium cars of today, a thorough testing strategy is needed. Early prototyping of such systems can be 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 a substantial 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 with a summary of publicly available virtual testing environments to enable closed-loop testing. As such, a steadily growing number of 36 datasets for open-loop testing and 21 virtual testing environments for closed-loop testing have been surveyed. Thus, conducting research towards autonomous driving is significantly supported from complementary community efforts: A growing number of publicly accessible datasets allow for experiments with perception approaches, while virtual testing environments enable end-to-end simulations.

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