Domain-centric ADAS Datasets

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Domain-centric ADAS Datasets

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Domain-centric ADAS Datasets

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/CK03000179" target="_blank" >CK03000179: Vytvoření dynamického digitálního modelu ulice pro potřeby autonomního řízení v Plzni</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Workshop on Artificial Intelligence Safety 2023

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

CEUR-WS

Místo vydání

Washington D.C.

Místo konání akce

Washington D.C.

Datum konání akce

13. 2. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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