Abstract
The core obstacle towards a large-scale deployment of autonomous vehicles currently lies in the long tail of rare events. These are extremely challenging since they do not occur often in the utilized training data for deep neural networks. To tackle this problem, we propose the generation of additional synthetic training data, covering a wide variety of corner case scenarios. As ontologies can represent human expert knowledge while enabling computational processing, we use them to describe scenarios. Our proposed master ontology is capable to model scenarios from all common corner case categories found in the literature. From this one master ontology, arbitrary scenario-describing ontologies can be derived. In an automated fashion, these can be converted into the OpenSCENARIO format and subsequently executed in simulation. This way, also challenging test and evaluation scenarios can be generated.
D. Bogdoll and S. Guneshka—Contributed equally.
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Notes
- 1.
We follow the definitions of scene and scenario by Ulbrich et al. [41], where a scene is a snapshot, and a scenario consists of successive scenes.
- 2.
CARLA version 0.9.13 was utilized.
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Acknowledgment
This work results partly from the project KI Data Tooling (19A20001J) funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK).
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Bogdoll, D., Guneshka, S., Zöllner, J.M. (2023). One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13805. Springer, Cham. https://doi.org/10.1007/978-3-031-25072-9_29
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