IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i10p3476-d812120.html
   My bibliography  Save this article

Advances in Automated Driving Systems

Author

Listed:
  • Arno Eichberger

    (Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, Austria)

  • Zsolt Szalay

    (Department of Automotive Technologies, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economic, 1111 Budapest, Hungary)

  • Martin Fellendorf

    (Institute of Transport Planning and Traffic Engineering, Graz University of Technology, 8010 Graz, Austria)

  • Henry Liu

    (Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA)

Abstract

Electrification, automation of vehicle control, digitalization and new mobility are the mega trends in automotive engineering and they are strongly connected to each other [...]

Suggested Citation

  • Arno Eichberger & Zsolt Szalay & Martin Fellendorf & Henry Liu, 2022. "Advances in Automated Driving Systems," Energies, MDPI, vol. 15(10), pages 1-5, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3476-:d:812120
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/10/3476/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/10/3476/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Demin Nalic & Aleksa Pandurevic & Arno Eichberger & Martin Fellendorf & Branko Rogic, 2021. "Software Framework for Testing of Automated Driving Systems in the Traffic Environment of Vissim," Energies, MDPI, vol. 14(11), pages 1-9, May.
    2. Björn Klamann & Hermann Winner, 2021. "Comparing Different Levels of Technical Systems for a Modular Safety Approval—Why the State of the Art Does Not Dispense with System Tests Yet," Energies, MDPI, vol. 14(22), pages 1-16, November.
    3. Sadegh Arefnezhad & Arno Eichberger & Matthias Frühwirth & Clemens Kaufmann & Maximilian Moser & Ioana Victoria Koglbauer, 2022. "Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness Using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals," Energies, MDPI, vol. 15(2), pages 1-25, January.
    4. Xuan Fang & Hexuan Li & Tamás Tettamanti & Arno Eichberger & Martin Fellendorf, 2022. "Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario," Energies, MDPI, vol. 15(6), pages 1-15, March.
    5. Viktor Tihanyi & András Rövid & Viktor Remeli & Zsolt Vincze & Mihály Csonthó & Zsombor Pethő & Mátyás Szalai & Balázs Varga & Aws Khalil & Zsolt Szalay, 2021. "Towards Cooperative Perception Services for ITS: Digital Twin in the Automotive Edge Cloud," Energies, MDPI, vol. 14(18), pages 1-26, September.
    6. Martin Holder & Lukas Elster & Hermann Winner, 2022. "Digitalize the Twin: A Method for Calibration of Reference Data for Transfer Real-World Test Drives into Simulation," Energies, MDPI, vol. 15(3), pages 1-16, January.
    7. Philipp Clement & Omar Veledar & Clemens Könczöl & Herbert Danzinger & Markus Posch & Arno Eichberger & Georg Macher, 2022. "Enhancing Acceptance and Trust in Automated Driving trough Virtual Experience on a Driving Simulator," Energies, MDPI, vol. 15(3), pages 1-22, January.
    8. Mohammad Junaid & Zsolt Szalay & Árpád Török, 2021. "Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions," Energies, MDPI, vol. 14(21), pages 1-16, November.
    9. Jianfei Huang & Xinchun Cheng & Yuying Shen & Dewen Kong & Jixin Wang, 2021. "Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders," Energies, MDPI, vol. 14(21), pages 1-16, November.
    10. Szilárd Czibere & Ádám Domina & Ádám Bárdos & Zsolt Szalay, 2021. "Model Predictive Controller Design for Vehicle Motion Control at Handling Limits in Multiple Equilibria on Varying Road Surfaces," Energies, MDPI, vol. 14(20), pages 1-17, October.
    11. Sorin Liviu Jurj & Dominik Grundt & Tino Werner & Philipp Borchers & Karina Rothemann & Eike Möhlmann, 2021. "Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning," Energies, MDPI, vol. 14(22), pages 1-19, November.
    12. Darko Babić & Dario Babić & Mario Fiolić & Željko Šarić, 2021. "Analysis of Market-Ready Traffic Sign Recognition Systems in Cars: A Test Field Study," Energies, MDPI, vol. 14(12), pages 1-10, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David Sziroczák & Daniel Rohács, 2021. "Automated Conflict Management Framework Development for Autonomous Aerial and Ground Vehicles," Energies, MDPI, vol. 14(24), pages 1-27, December.
    2. Xuxu Li & Xiaojiang Liu & Yun Xiao & Yao Zhang & Xiaomei Yang & Wenhai Zhang, 2022. "An Improved U-Net Segmentation Model That Integrates a Dual Attention Mechanism and a Residual Network for Transformer Oil Leakage Detection," Energies, MDPI, vol. 15(12), pages 1-15, June.
    3. Couraud, Benoit & Andoni, Merlinda & Robu, Valentin & Norbu, Sonam & Chen, Si & Flynn, David, 2023. "Responsive FLEXibility: A smart local energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    4. Arno Eichberger & Marianne Kraut & Ioana V. Koglbauer, 2022. "Improved Perception of Motorcycles by Simulator-Based Driving Education," Sustainability, MDPI, vol. 14(9), pages 1-16, April.
    5. Mateusz Malarczyk & Jules-Raymond Tapamo & Marcin Kaminski, 2022. "Application of Neural Data Processing in Autonomous Model Platform—A Complex Review of Solutions, Design and Implementation," Energies, MDPI, vol. 15(13), pages 1-22, June.
    6. Sorin Liviu Jurj & Tino Werner & Dominik Grundt & Willem Hagemann & Eike Möhlmann, 2022. "Towards Safe and Sustainable Autonomous Vehicles Using Environmentally-Friendly Criticality Metrics," Sustainability, MDPI, vol. 14(12), pages 1-52, June.
    7. Andrea Gemma & Tina Onorato & Stefano Carrese, 2023. "Performances and Environmental Impacts of Connected and Autonomous Vehicles for Different Mixed-Traffic Scenarios," Sustainability, MDPI, vol. 15(13), pages 1-19, June.
    8. Serajeddin Ebrahimian & Ali Nahvi & Masoumeh Tashakori & Hamed Salmanzadeh & Omid Mohseni & Timo Leppänen, 2022. "Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks," IJERPH, MDPI, vol. 19(17), pages 1-17, August.
    9. Maksymilian Mądziel, 2023. "Vehicle Emission Models and Traffic Simulators: A Review," Energies, MDPI, vol. 16(9), pages 1-31, May.

    More about this item

    Keywords

    n/a;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3476-:d:812120. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.