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Intelligent Fault Detection and Classification Based on Hybrid Deep Learning Methods for Hardware-in-the-Loop Test of Automotive Software Systems

Mohammad Abboush, Daniel Bamal, Christoph Knieke, Andreas Rausch
2022 Sensors  
Hardware-in-the-Loop (HIL) has been recommended by ISO 26262 as an essential test bench for determining the safety and reliability characteristics of automotive software systems (ASSs).  ...  Even though data-driven fault diagnosis is superior to other approaches, selecting the appropriate technique from the wide range of Deep Learning (DL) techniques is challenging.  ...  By doing so, real-time constraints are taken into account and the majority of common faults in automotive software signals are covered.  ... 
doi:10.3390/s22114066 pmid:35684686 pmcid:PMC9185421 fatcat:lz7zs37nxjfuxinigmdssql25a

Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry

Markus Borg, Cristofer Englund, Krzysztof Wnuk, Boris Duran, Christoffer Levandowski, Shenjian Gao, Yanwen Tan, Henrik Kaijser, Henrik Lönn, Jonas Törnqvist
2019 Journal of Automotive Software Engineering  
A B S T R A C T Deep neural networks (DNNs) will emerge as a cornerstone in automotive software engineering. However, developing systems with DNNs introduces novel challenges for safety assessments.  ...  This paper reviews the state-of-the-art in verification and validation of safety-critical systems that rely on machine learning.  ...  ACKNOWLEDGMENTS Thanks go to all participants in the SMILE workshops, in particular Carl Zandén, Michaël Simoen, and Konstantin Lindström.  ... 
doi:10.2991/jase.d.190131.001 fatcat:jdledybuk5dehiy32zfukr3ptu

2020 VT Year End Index

2020 IEEE Vehicular Technology Magazine  
., +, MVT Sept. 2020 77-85 Virtualized In Situ Software Update Verification: Verification of Over-the-Air Automotive Software Updates.  ...  ., +, MVT Sept. 2020 77-85 Virtualized In Situ Software Update Verification: Verification of Over-the-Air Automotive Software Updates.  ...  H Handover Prediction-Based Conditional Handover for 5G mm-Wave Networks: A Deep-Learning Approach. Lee, C., +,  ... 
doi:10.1109/mvt.2020.3042426 fatcat:ehwwig4xdbcfhnj4c3yoeuwal4

RadarConf21 2021 Blank Page

2021 2021 IEEE Radar Conference (RadarConf21)  
DEEPREFLECS: Deep Learning for Automotive Object Classification with Radar Reflections by Michael Ulrich, Claudius Gläser, Fabian Timm 3.  ...  Deep Transfer Learning for WiFi Localization by Peizheng Li, Han Cui, Aftab Khan, Usman Raza, Robert Piechocki, Angela Doufexi, Tim Farnham Software Defined Radar & Low-cost radar Begins: 5/12/2021 14:  ... 
doi:10.1109/radarconf2147009.2021.9455240 fatcat:mfgpxueblfdvtli4uh6vyfgw4m

Searching for common ground: existing literature on automotive agile software product lines

Philipp Hohl, Javad Ghofrani, Jürgen Münch, Michael Stupperich, Kurt Schneider
2017 Proceedings of the 2017 International Conference on Software and System Process - ICSSP 2017  
Typical characteristics of the automotive domain that need to be considered are the deep integration between hardware and software, a strong focus on development processes, a close supplier involvement  ...  Fig. 1 : 1 What is the state-of-the-art to combine agile software development and software product lines in the automotive domain, according to published literature?  ... 
doi:10.1145/3084100.3084109 dblp:conf/ispw/HohlGMSS17 fatcat:iil5wdcoyzczfmn364wo2ibg5e

On Automotive Electronics

2020 ATZelectronics worldwide  
Aptiv's pockets were not deep enough, so it sold half of its L4 self-driving venture to Hyundai for 2 billion US dollars: 1.6 billion US dollars in cash, plus 400 million US dollars worth of engineering  ...  Aptiv paid 400 million US dollars plus 50 million US dollars in earn outs for nuTonomy.  ...  For the last two years, the Toyota Research Institute has been using AWS's deep learning framework to train ADAS and autonomous systems it is developing.  ... 
doi:10.1007/s38314-020-0240-0 fatcat:z643336s6jgj3hrh2btnd5psba

SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks [article]

Natasha Alkhatib, Hadi Ghauch, Jean-Luc Danger
2021 arXiv   pre-print
In this paper, we present a deep learning-based sequential model for offline intrusion detection on SOME/IP application layer protocol.  ...  Furthermore, we also propose a recurrent neural network (RNN), as an instance of deep learning-based sequential model, that we apply to our generated dataset.  ...  In this paper, we have proposed a deep learning based IDS that can be leveraged to detect intrusions on SOME/IP automotive protocol.  ... 
arXiv:2108.08262v2 fatcat:vb7xqm772vevlngqwf3yycjmoa

BONSEYES

Tim Llewellynn, Sebastian Koller, Georgios Goumas, Peter Leitner, Ganesh Dasika, Lei Wang, Kurt Tutschku, M. Milagro Fernández-Carrobles, Oscar Deniz, Samuel Fricker, Amos Storkey, Nuria Pazos (+3 others)
2017 Proceedings of the Computing Frontiers Conference on ZZZ - CF'17  
The Bonseyes EU H2020 collaborative project aims to develop a platform consisting of a Data Marketplace, a Deep Learning Toolbox, and Developer Reference Platforms for organizations wanting to adopt Articial  ...  In addition, it will solve a causality problem for organizations who lack access to Data and Models. Its open software architecture will facilitate adoption of the whole concept on a wider scale.  ...  Deep Learning Toolbox The objective of the Deep Learning Toolbox is to provide a set of deep learning components that are tailored for embedded, constrained, distributed systems operating in real environments  ... 
doi:10.1145/3075564.3076259 dblp:conf/cf/LlewellynnFDFSP17 fatcat:bzhu5u72h5echb2wswrsbwz52e

CondenseNeXtV2: Light-Weight Modern Image Classifier Utilizing Self-Querying Augmentation Policies

Priyank Kalgaonkar, Mohamed El-Sharkawy
2022 Journal of Low Power Electronics and Applications  
improved efficiency in image classification computation and accuracy.  ...  This work is an extension of the award-winning paper entitled 'CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded Systems' published for the 2021 IEEE 11th Annual Computing and Communication  ...  , b utilizing advanced computing hardware for deep learning, computer vision and senso fusion [13] .  ... 
doi:10.3390/jlpea12010008 fatcat:clexdefdbnal5oteuigvb3nmtq

Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry [article]

Markus Borg, Cristofer Englund, Krzysztof Wnuk, Boris Duran, Christoffer Levandowski, Shenjian Gao, Yanwen Tan, Henrik Kaijser, Henrik Lönn, Jonas Törnqvist
2018 arXiv   pre-print
Deep Neural Networks (DNN) will emerge as a cornerstone in automotive software engineering. However, developing systems with DNNs introduces novel challenges for safety assessments.  ...  This paper reviews the state-of-the-art in verification and validation of safety-critical systems that rely on machine learning.  ...  Introduction As an enabling technology for autonomous driving, Deep learning Neural Networks (DNN) will emerge arXiv:1812.05389v1 [cs.SE] 13 Dec 2018 as a cornerstone in automotive software engineering  ... 
arXiv:1812.05389v1 fatcat:ihtks3k77zdaxnhnz27zrzzhze

VEDLIoT: Very Efficient Deep Learning in IoT [article]

Martin Kaiser, Rene Griessl, Nils Kucza, Carola Haumann, Lennart Tigges, Kevin Mika, Jens Hagemeyer, Florian Porrmann, Ulrich Rückert, Micha vor dem Berge, Stefan. Krupop, Mario Porrmann (+24 others)
2022 arXiv   pre-print
The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications.  ...  The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020.  ...  However, due to the probabilistic nature of deep learning, the assumption that only systematic faults exist in software does not hold anymore.  ... 
arXiv:2207.00675v1 fatcat:mbjeq2zwk5frbhawuynqm5mlki

Deep learning in the automotive industry: Applications and tools

Andre Luckow, Matthew Cook, Nathan Ashcraft, Edwin Weill, Emil Djerekarov, Bennie Vorster
2016 2016 IEEE International Conference on Big Data (Big Data)  
In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision.  ...  Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services.  ...  We acknowledge Darius Cepulis for his early work on deep learning benchmarks.  ... 
doi:10.1109/bigdata.2016.7841045 dblp:conf/bigdataconf/LuckowCAWDV16 fatcat:6nlqgynur5djblmkzrve7gwb7q

Deep Learning in Manufacturing

Matthew N. O. Sadiku, Guddi K. Suman, Sarhan M. Musa
2021 International Journal of Advances in Scientific Research and Engineering  
This paper discusses deep learning algorithms and their applications in manufacturing.  ...  Deep learning is presently receiving a lot of attention. It is a subset of machine learning, based on multi-layer neural networks or deep neural networks.  ...  Deep learning has many potential applications in the automotive industry during development, manufacturing, and sales.  ... 
doi:10.31695/ijasre.2021.34027 fatcat:d2fvxmfptzfslmy5hrphct4x3a

Table of Contents

2021 2021 Zooming Innovation in Consumer Technologies Conference (ZINC)  
Armoogum; Ravi Foogooa Recyclable Waste Classification Using Computer Vision And Deep Learning 11 Vladimir Petrović An Inexpensive Design of Agent's Behavior During a "Picking Task" in a Simulated  ...  Teslic One Solution for Deterministic Scheduling on GPU for Automotive Algorithms Unsupervised gender prediction based on deep facial features 1 Timea Bezdan; Aleksandar Petrovic; Miodrag Zivkovic  ... 
doi:10.1109/zinc52049.2021.9499272 fatcat:bhizalgiknfivmo5qdf2swgqs4

SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks

Natasha Alkhatib, Hadi Ghauch, Jean-Luc Danger
2021 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)  
In this paper, we present a deep learning-based sequential model for offline intrusion detection on SOME/IP application layer protocol.  ...  Furthermore, we also propose a recurrent neural network (RNN), as an instance of deep learningbased sequential model, that we apply to our generated dataset.  ...  In this paper, we have proposed a deep learning based IDS that can be leveraged to detect intrusions on SOME/IP automotive protocol.  ... 
doi:10.1109/iemcon53756.2021.9623129 fatcat:2aerf64wq5fqzf45twdx6nu7qy
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