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research-article

Deep learning for self-driving cars: chances and challenges

Authors:

Jelena FrtunikjAuthors Info & Claims

SEFAIS '18: Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems

Pages 35 - 38

https://doi.org/10.1145/3194085.3194087

Published: 28 May 2018 Publication History

Abstract

Artificial Intelligence (AI) is revolutionizing the modern society. In the automotive industry, researchers and developers are actively pushing deep learning based approaches for autonomous driving. However, before a neural network finds its way into series production cars, it has to first undergo strict assessment concerning functional safety. The chances and challenges of incorporating deep learning for self-driving cars are presented in this paper.

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Cited By

Aljehane N(2024)A Study to Investigate the Role and Challenges Associated with the Use of Deep Learning in Autonomous VehiclesWorld Electric Vehicle Journal10.3390/wevj1511051815:11(518)Online publication date: 12-Nov-2024
https://doi.org/10.3390/wevj15110518
Chen MGan HWu H(2024)Research on Fault Diagnosis Method for Marine Diesel Engines Based on Multi-Scale Attention Mechanism TransformerJournal of Marine Science and Engineering10.3390/jmse1212234812:12(2348)Online publication date: 21-Dec-2024
https://doi.org/10.3390/jmse12122348
Pereira IMadureira ABettencourt NCoelho DRebelo MAraújo Cde Oliveira D(2024)A Machine Learning as a Service (MLaaS) Approach to Improve Marketing SuccessInformatics10.3390/informatics1102001911:2(19)Online publication date: 15-Apr-2024
https://doi.org/10.3390/informatics11020019
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Index Terms

Deep learning for self-driving cars: chances and challenges
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation

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Information

Published In

cover image ACM Conferences

SEFAIS '18: Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems

May 2018

63 pages

ISBN:9781450357395

DOI:10.1145/3194085

Program Chairs:
Reinhard Stolle
BMW Group, München, Germany
,
Stephan Scholz
Volkswagen AG, Wolfsburg, Germany
,
Manfred Broy
Technische Universität München, Zentrum Digitalisierung Bayern, München, Germany

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 May 2018

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ICSE '18

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SIGSOFT
IEEE-CS

ICSE '18: 40th International Conference on Software Engineering

May 28, 2018

Gothenburg, Sweden

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Cited By

Aljehane N(2024)A Study to Investigate the Role and Challenges Associated with the Use of Deep Learning in Autonomous VehiclesWorld Electric Vehicle Journal10.3390/wevj1511051815:11(518)Online publication date: 12-Nov-2024
https://doi.org/10.3390/wevj15110518
Chen MGan HWu H(2024)Research on Fault Diagnosis Method for Marine Diesel Engines Based on Multi-Scale Attention Mechanism TransformerJournal of Marine Science and Engineering10.3390/jmse1212234812:12(2348)Online publication date: 21-Dec-2024
https://doi.org/10.3390/jmse12122348
Pereira IMadureira ABettencourt NCoelho DRebelo MAraújo Cde Oliveira D(2024)A Machine Learning as a Service (MLaaS) Approach to Improve Marketing SuccessInformatics10.3390/informatics1102001911:2(19)Online publication date: 15-Apr-2024
https://doi.org/10.3390/informatics11020019
Diviš VSpatz BHrúz M(2024)TRust Your GENerator (TRYGEN): Enhancing Out-of-Model Scope DetectionAI10.3390/ai50401045:4(2127-2146)Online publication date: 30-Oct-2024
https://doi.org/10.3390/ai5040104
Alkashto HElewi A(2024)Integration of blockchain and machine learning for safe and efficient autonomous car systems: A surveyTurkish Journal of Engineering10.31127/tuje.13662488:2(282-299)Online publication date: 30-Apr-2024
https://doi.org/10.31127/tuje.1366248
von Stein MWang HElbaum S(2024)Automated Generation of Transformations to Mitigate Sensor Hardware Migration in ADSIEEE Robotics and Automation Letters10.1109/LRA.2024.34058109:7(6480-6487)Online publication date: Jul-2024
https://doi.org/10.1109/LRA.2024.3405810
Agostini NHaris JGibson PJayaweera MRubin NTumeo AAbellán JCano JKaeli DGrosser TDubach CSteuwer MXue JOttoni GQuintão Pereira F(2024)AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based AcceleratorsProceedings of the 2024 IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO57630.2024.10444801(143-157)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1109/CGO57630.2024.10444801
Zhao LDuan XYang CLiu YDong YWang XWang W(2024)Efficient verification of neural networks based on neuron branching and LP abstractionNeurocomputing10.1016/j.neucom.2024.127936596:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127936
Gligorić ZGörçün ÖGligorić MPamucar DSimic VKüçükönder H(2024)Evaluating the deep learning software tools for large-scale enterprises using a novel TODIFFA-MCDM frameworkJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10207936:5Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1016/j.jksuci.2024.102079
Jing JJinhang CZhang HZhang W(2024)Boundedness and Convergence of Mini-batch Gradient Method with Cyclic Dropconnect and PenaltyNeural Processing Letters10.1007/s11063-024-11581-556:2Online publication date: 19-Mar-2024
https://doi.org/10.1007/s11063-024-11581-5
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