An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving

Heidecker, Florian; Breitenstein, Jasmin; Rösch, Kevin; Löhdefink, Jonas; Bieshaar, Maarten; Stiller, Christoph; Fingscheidt, Tim; Sick, Bernhard

doi:10.1109/iv48863.2021.9575933

Cited by 32 publications

(27 citation statements)

References 24 publications

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“…This was also the basis for a subsequent publication with additional examples [5]. Since the approach in these references is camera-based, a categorization of corner cases at sensor level was adapted in [6], where RADAR and LiDAR sensors were also considered. Furthermore, this reference presents a toolchain for data generation and processing for corner case detection.…”

Section: A Corner Casesmentioning

confidence: 99%

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A-Eye: Driving with the Eyes of AI for Corner Case Generation

Kowol¹,

Bracke²,

Gottschalk³

2022

Preprint

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The overall goal of this work is to enrich training data for automated driving with so called corner cases. In road traffic, corner cases are critical, rare and unusual situations that challenge the perception by AI algorithms. For this purpose, we present the design of a test rig to generate synthetic corner cases using a human-in-the-loop approach. For the test rig, a realtime semantic segmentation network is trained and integrated into the driving simulation software CARLA in such a way that a human can drive on the network's prediction. In addition, a second person gets to see the same scene from the original CARLA output and is supposed to intervene with the help of a second control unit as soon as the semantic driver shows dangerous driving behavior. Interventions potentially indicate poor recognition of a critical scene by the segmentation network and then represents a corner case. In our experiments, we show that targeted enrichment of training data with corner cases leads to improvements in pedestrian detection in safety relevant episodes in road traffic.

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Section: A Corner Casesmentioning

confidence: 99%

“…Outside normal parameters also includes terms such as anomalies, novelties, or outliers, which, according to [6], correlate strongly with the term corner case. In road traffic, the detection of new and unknown objects, anomalies or obstacles, which must also be evaluated as 'outside the operating parameters', is essential.…”

Section: A Corner Casesmentioning

confidence: 99%

A-Eye: Driving with the Eyes of AI for Corner Case Generation

Kowol¹,

Bracke²,

Gottschalk³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Compared to the works of Yin and Berger and Laflamme et al however, this collection is much smaller in scope. (Heidecker et al, 2021) takes on the topic of corner cases in highly automated driving. Here, too, a section revolves solely around suitable data sets for corner case detectors.…”

Section: Scientific Papersmentioning

confidence: 99%

Ad-datasets: a meta-collection of data sets for autonomous driving

Bogdoll,

Schreyer,

Zöllner

2022

Preprint

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Autonomous driving is among the largest domains in which deep learning has been fundamental for progress within the last years. The rise of datasets went hand in hand with this development. All the more striking is the fact that researchers do not have a tool available that provides a quick, comprehensive and up-to-date overview of data sets and their features in the domain of autonomous driving. In this paper, we present ad-datasets, an online tool that provides such an overview for more than 150 data sets. The tool enables users to sort and filter the data sets according to currently 16 different categories. ad-datasets is an open-source project with community contributions. It is in constant development, ensuring that the content stays up-to-date.

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“…The development and training of such methods needs to incorporate corner case situations, which deviate from regular traffic scenes and are hard to find in large datasets. The most challenging corner cases for automated vehicles, according to [15], are within the scene and scenario level. To detect relevant scenes within the scene graph, it is necessary to analyze the criticality of relations between traffic participants.…”

Section: Criticality Measuresmentioning

confidence: 99%

Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality

Töttel,

Zipfl,

Bogdoll

et al. 2021

Preprint

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An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving

Cited by 32 publications

References 24 publications

A-Eye: Driving with the Eyes of AI for Corner Case Generation

A-Eye: Driving with the Eyes of AI for Corner Case Generation

Ad-datasets: a meta-collection of data sets for autonomous driving

Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality

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