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ATZelectronics worldwide

Cloud Intelligence and Collective Learning for Automated and Connected Driving

  • 01-11-2022
  • Development
Published in:
Authors
Bastian Lampe
Lennart Reiher
Timo Woopen
Lutz Eckstein
Published in
ATZelectronics worldwide | Issue 11/2022

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Excerpt

The article delves into the complexities of automated driving and how artificial intelligence, particularly deep learning methods, is revolutionizing the field. It discusses the necessity of powerful cloud systems for processing vast amounts of data required for learning algorithms. The concept of cloud intelligence is explored, highlighting the potential to offload functions to the cloud and leverage its computational power for enhanced safety and performance. The article also introduces collective learning, a process that enables continuous improvement of models through data gathered from connected vehicles. Methods such as cross-view and cross-time label generation, as well as edge case scenario sampling, are presented as key techniques for this process. The integration of these advanced technologies is expected to significantly improve the safety and efficiency of automated driving systems.
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previous article Development of an 800-V Inverter for Electric Vehicles
next article Simulation of Dense Traffic Situations to Validate Autonomous Driving Functions
Title
Cloud Intelligence and Collective Learning for Automated and Connected Driving
Authors
Bastian Lampe
Lennart Reiher
Timo Woopen
Lutz Eckstein
Publication date
01-11-2022
Publisher
Springer Fachmedien Wiesbaden
Published in
ATZelectronics worldwide / Issue 11/2022
Electronic ISSN: 2524-8804
DOI
https://doi.org/10.1007/s38314-022-1405-9

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