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Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

Rösch, Kevin and Heidecker, Florian and Truetsch, Julian and Kowol, Kamil and Schicktanz, Clemens and Bieshaar, Maarten and Sick, Bernhard and Stiller, Christoph (2022) Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving. In: 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022. IEEE SSCI 2022, 05.-07. Dez. 2022, Singapur. doi: 10.1109/SSCI51031.2022.10022241. ISBN 978-166548768-9. (In Press)

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Abstract

Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns. Based on these predictions — and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users — the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.

Item URL in elib:https://elib.dlr.de/188337/
Document Type:Conference or Workshop Item (Speech)
Title:Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Rösch, KevinUNSPECIFIEDhttps://orcid.org/0000-0002-6841-8484UNSPECIFIED
Heidecker, FlorianUNSPECIFIEDhttps://orcid.org/0000-0003-2895-0254UNSPECIFIED
Truetsch, JulianUNSPECIFIEDhttps://orcid.org/0000-0001-5824-2617UNSPECIFIED
Kowol, KamilUNSPECIFIEDhttps://orcid.org/0000-0001-6951-7081UNSPECIFIED
Schicktanz, ClemensUNSPECIFIEDhttps://orcid.org/0000-0002-3234-2086UNSPECIFIED
Bieshaar, MaartenUNSPECIFIEDhttps://orcid.org/0000-0002-6471-6062UNSPECIFIED
Sick, BernhardUNSPECIFIEDhttps://orcid.org/0000-0001-9467-656XUNSPECIFIED
Stiller, ChristophUNSPECIFIEDhttps://orcid.org/0000-0003-4165-2075UNSPECIFIED
Date:2022
Journal or Publication Title:2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI:10.1109/SSCI51031.2022.10022241
ISBN:978-166548768-9
Status:In Press
Keywords:taxonomy, corner case, trajectory data
Event Title:IEEE SSCI 2022
Event Location:Singapur
Event Type:international Conference
Event Dates:05.-07. Dez. 2022
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - V&V4NGC - Methoden, Prozesse und Werkzeugketten für die Validierung & Verifikation von NGC
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Transportation Systems > Information Gathering and Modelling, BA
Deposited By: Schicktanz, Clemens
Deposited On:10 Jan 2023 13:23
Last Modified:17 Jul 2023 12:17

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