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We thank Sidney Pacanowski for the labeling effort, Dariyoush Shiri for support in coding and Matthias Rottmann for interesting discussions. This work has been funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) via the research consortia Safe AI for Automated Driving (grant no. 19A19005R), AI Delta Learning (grant no. 19A19013Q), AI Data Tooling (grant no. 19A20001O) and the Ministry of Culture and Science of the German state of North Rhine-Westphalia as part of the KI-Starter research funding program.
images and labels of the Street Obstacle Sequences dataset
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 26 | |
| downloads | 7 |

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