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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
2022
Sensors
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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
2019
Journal of Automotive Software Engineering
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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
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., +, 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)
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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
2017
Proceedings of the 2017 International Conference on Software and System Process - ICSSP 2017
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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
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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]
2021
arXiv
pre-print
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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
2017
Proceedings of the Computing Frontiers Conference on ZZZ - CF'17
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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
2022
Journal of Low Power Electronics and Applications
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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]
2018
arXiv
pre-print
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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]
2022
arXiv
pre-print
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2021
2021 IEEE 37th International Conference on Data Engineering (ICDE)
unpublished
doi:10.1109/icde51399.2021.00025 fatcat:ep4yupesfjgzrbjhvlgpdayzge
doi:10.1109/icde51399.2021.00025 fatcat:ep4yupesfjgzrbjhvlgpdayzge
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
2016
2016 IEEE International Conference on Big Data (Big Data)
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2017 pre-print arXiv:1705.00346v1 fatcat:he5murdv7nht3ovpkapyftevxi
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
2021
International Journal of Advances in Scientific Research and Engineering
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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)
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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
2021
2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)
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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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