Domain-centric ADAS Datasets

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969641" target="_blank" >RIV/49777513:23520/23:43969641 - isvavai.cz</a>
Result on the web
<a href="https://ceur-ws.org/Vol-3381/33.pdf" target="_blank" >https://ceur-ws.org/Vol-3381/33.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Domain-centric ADAS Datasets
Original language description
Since the rise of Deep Learning methods in the automotive field, multiple initiatives have been collecting datasets in order to train neural networks on different levels of autonomous driving. This requires collecting relevant data and precisely annotating objects, which should represent uniformly distributed features for each specific use case. In this paper, we analyze several large-scale autonomous driving datasets with 2D and 3D annotations in regard to their statistics of appearance and their suitability for training robust object detection neural networks. We discovered that despite spending huge effort on driving hundreds of hours in different regions of the world, merely any focus is spent on analyzing the quality of the collected data, from an operational domain perspective. The analysis of safety-relevant aspects of autonomous driving functions, in particular trajectory planning with relation to time-to-collision feature, showed that most datasets lack annotated objects at further distances and that the distributions of bounding boxes and object positions are unbalanced. We therefore propose a set of rules which help find objects or scenes with inconsistent annotation styles. Lastly, we questioned the relevance of mean Average Precision (mAP) without relation to the object size or distance.
Czech name
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Czech description
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Project
<a href="/en/project/CK03000179" target="_blank" >CK03000179: Dynamic digital street model for the usage of autonomous mobility in Pilsen</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the Workshop on Artificial Intelligence Safety 2023
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
CEUR-WS
Place of publication
Washington D.C.
Event location
Washington D.C.
Event date
Feb 13, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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