research-article

Leveraging Interpretability: Concept-based Pedestrian Detection with Deep Neural Networks

Authors:

Patrick Feifel,

Frank Bonarens,

Frank KösterAuthors Info & Claims

CSCS '21: Proceedings of the 5th ACM Computer Science in Cars Symposium

Article No.: 2, Pages 1 - 10

https://doi.org/10.1145/3488904.3493379

Published: 30 November 2021 Publication History

Abstract

The automation of driving systems relies on proof of the correct functioning of perception. Arguing the safety of deep neural networks (DNNs) must involve quantifiable evidence. Currently, the application of DNNs suffers from an incomprehensible behavior. It is still an open question if post-hoc methods mitigate the safety concerns of trained DNNs. Our work proposes a method for inherently interpretable and concept-based pedestrian detection (CPD). CPD explicitly structures the latent space with concept vectors that learn features for body parts as predefined concepts. The distance-based clustering and separation of latent representations build an interpretable reasoning process. Hence, CPD predicts a body part segmentation based on distances of latent representations to concept vectors. A non-interpretable 2d bounding box prediction for pedestrians complements the segmentation. The proposed CPD generates additional information that can be of great value in a safety argumentation of a DNN for pedestrian detection. We report competitive performance for the task of pedestrian detection. Finally, CPD enables concept-based tests to quantify evidence of a safe perception in automated driving systems.

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Cited By

Decker TGross RKoebler ALebacher MSchnitzer RWeber S(2023)The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of ResearchArtificial Intelligence in HCI10.1007/978-3-031-35891-3_13(184-208)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-35891-3_13

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Published In

cover image ACM Conferences

CSCS '21: Proceedings of the 5th ACM Computer Science in Cars Symposium

November 2021

101 pages

ISBN:9781450391399

DOI:10.1145/3488904

Editors:
Björn Brücher
Intel Germany
,
Christoph Krauß
Hochschule Darmstadt, Fraunhofer Institute for Secure Information Technology (SIT)
,
Mario Fritz
CISPA Helmholtz Center for Information Security, Germany
,
Hans-Joachim Hof
Technical University of Ingolstadt, Germany
,
Oliver Wasenmüller
University for Applied Science Mannheim, Germany

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Published: 30 November 2021

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CSCS '21: Computer Science in Cars Symposium

November 30, 2021

Ingolstadt, Germany

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Cited By

Decker TGross RKoebler ALebacher MSchnitzer RWeber S(2023)The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of ResearchArtificial Intelligence in HCI10.1007/978-3-031-35891-3_13(184-208)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-35891-3_13

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