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The Cartographer
Highly Automated Learning for Improved Active Safety of Vulnerable Road Users
March 09, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Maarten Bieshaar, GΓΌnther Reitberger, Viktor KreΓ, Stefan Zernetsch, Konrad Doll, Erich Fuchs, Bernhard Sick
arXiv ID
1803.03479
Category
cs.AI: Artificial Intelligence
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Highly automated driving requires precise models of traffic participants. Many state of the art models are currently based on machine learning techniques. Among others, the required amount of labeled data is one major challenge. An autonomous learning process addressing this problem is proposed. The initial models are iteratively refined in three steps: (1) detection and context identification, (2) novelty detection and active learning and (3) online model adaption.
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