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Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Wörmann, Julian and Bogdoll, Daniel and Srinivas, Gurucharan and Kelsch, Johann and Bührle, Etienne and Chen, Han and Fuh Chuo, Evaristus and Cvejoski, Kostadin and Gleißner, Tobias and van Elst, Ludger and Gottschall, Philip and Griesche, Stefan and Hellert, Christian and Hesels, Christian and Houben, Sebastian and Joseph, Tim and Keil, Niklas and Königshof, Hendrik and Kraft, Erwin and Kreuser, Leonie and Krone, Kevin and Latka, Tobias and Mattern, Denny and Matthes, Stefan and Munir, Mohsin and Nekolla, Moritz and Paschke, Adrian and Alexander Pintz, Maximilian and Qiu, Tianming and Qureishi, Faraz and Tahseen Raza Rizvi, Syed and Reichardt, Jörg and von Rueden, Laura and Rudolph, Stefan and Sagel, Alexander and Schunk, Gerhard and Shen, Hao and Stapelbroek, Hendrik and Stehr, Vera and Tuan Tran, Anh and Vivekanandan, Abhishek and Wang, Ya and Wasserrab, Florian and Werner, Tino and Wirth, Christian and Zwicklbauer, Stefan (2022) Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey. [Other]

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Official URL: https://arxiv.org/pdf/2205.04712.pdf

Abstract

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.

Item URL in elib:https://elib.dlr.de/186400/
Document Type:Other
Title:Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Wörmann, Julianfortiss GmbHUNSPECIFIEDUNSPECIFIED
Bogdoll, DanielFZI Forschungszentrum InformatikUNSPECIFIEDUNSPECIFIED
Srinivas, GurucharanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kelsch, JohannUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bührle, EtienneFZI Forschungszentrum InformatikUNSPECIFIEDUNSPECIFIED
Chen, HanCapgemini EngineeringUNSPECIFIEDUNSPECIFIED
Fuh Chuo, EvaristusCapgemini EngineeringUNSPECIFIEDUNSPECIFIED
Cvejoski, KostadinFraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.UNSPECIFIEDUNSPECIFIED
Gleißner, TobiasFraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.UNSPECIFIEDUNSPECIFIED
van Elst, LudgerDeutsches Forschungszentrum für Künstliche Intelligenz GmbHUNSPECIFIEDUNSPECIFIED
Gottschall, PhilipFraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.UNSPECIFIEDUNSPECIFIED
Griesche, StefanRobert Bosch GmbHUNSPECIFIEDUNSPECIFIED
Hellert, ChristianContinental AGUNSPECIFIEDUNSPECIFIED
Hesels, ChristianFraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.UNSPECIFIEDUNSPECIFIED
Houben, SebastianFraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.UNSPECIFIEDUNSPECIFIED
Joseph, TimFZI Forschungszentrum InformatikUNSPECIFIEDUNSPECIFIED
Keil, NiklasAlexander Thamm GmbHUNSPECIFIEDUNSPECIFIED
Königshof, HendrikFZI Forschungszentrum InformatikUNSPECIFIEDUNSPECIFIED
Kraft, ErwinContinental AGUNSPECIFIEDUNSPECIFIED
Kreuser, LeonieAlexander Thamm GmbHUNSPECIFIEDUNSPECIFIED
Krone, KevinFraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.UNSPECIFIEDUNSPECIFIED
Latka, TobiasElektronische Fahrwerksysteme GmbHUNSPECIFIEDUNSPECIFIED
Mattern, DennyFraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.UNSPECIFIEDUNSPECIFIED
Matthes, Stefanfortiss GmbHUNSPECIFIEDUNSPECIFIED
Munir, MohsinDeutsches Forschungszentrum für Künstliche Intelligenz GmbHUNSPECIFIEDUNSPECIFIED
Nekolla, MoritzFZI Forschungszentrum InformatikUNSPECIFIEDUNSPECIFIED
Paschke, AdrianFraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.UNSPECIFIEDUNSPECIFIED
Alexander Pintz, MaximilianFraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.UNSPECIFIEDUNSPECIFIED
Qiu, Tianmingfortiss GmbHUNSPECIFIEDUNSPECIFIED
Qureishi, FarazValeo Schalter und Sensoren GmbHUNSPECIFIEDUNSPECIFIED
Tahseen Raza Rizvi, SyedDeutsches Forschungszentrum für Künstliche Intelligenz GmbHUNSPECIFIEDUNSPECIFIED
Reichardt, JörgContinental AGUNSPECIFIEDUNSPECIFIED
von Rueden, LauraFraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.UNSPECIFIEDUNSPECIFIED
Rudolph, StefanElektronische Fahrwerksysteme GmbHUNSPECIFIEDUNSPECIFIED
Sagel, Alexanderfortiss GmbHUNSPECIFIEDUNSPECIFIED
Schunk, GerhardValeo Schalter und Sensoren GmbHUNSPECIFIEDUNSPECIFIED
Shen, Haofortiss GmbHUNSPECIFIEDUNSPECIFIED
Stapelbroek, HendrikCapgemini EngineeringUNSPECIFIEDUNSPECIFIED
Stehr, VeraValeo Schalter und Sensoren GmbHUNSPECIFIEDUNSPECIFIED
Tuan Tran, AnhRobert Bosch GmbHUNSPECIFIEDUNSPECIFIED
Vivekanandan, AbhishekFZI Forschungszentrum InformatikUNSPECIFIEDUNSPECIFIED
Wang, YaFraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.UNSPECIFIEDUNSPECIFIED
Wasserrab, FlorianAlexander Thamm GmbHUNSPECIFIEDUNSPECIFIED
Werner, TinoUNSPECIFIEDhttps://orcid.org/0000-0002-3512-8667UNSPECIFIED
Wirth, ChristianContinental AGUNSPECIFIEDUNSPECIFIED
Zwicklbauer, StefanContinental AGUNSPECIFIEDUNSPECIFIED
Date:10 May 2022
Journal or Publication Title:arxiv.org
Refereed publication:No
Open Access:Yes
Number of Pages:93
Status:Published
Keywords:Knowledge Augmented Machine Learning, Informed Machine Learning, Knowledge drive Machine Learning
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 - KoKoVI - Koordinierter kooperativer Verkehr mit verteilter, lernender Intelligenz
Location: Braunschweig , Oldenburg
Institutes and Institutions:Institute of Transportation Systems > Cooperative Systems, BS
Institute of Systems Engineering for Future Mobility > Systems Theory and Design
Deposited By: Srinivas, Gurucharan
Deposited On:07 Nov 2022 11:00
Last Modified:29 Mar 2023 00:02

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