Unmanned vehicle’s control real-time method based on neural network and selection function

The article deals with the problem of controlling an unmanned vehicle in real time. The authors trained a convolution neural network (CNN) to display raw pixels from a single front camera directly into commands of control. This end-to-end approach is almost optimal control based on the selection function. The system automatically remembers internal representations of necessary steps, such as detecting useful road characteristics with restrictions only based on the MPC’s controller calculating control commands as a training signal. Compared to explicit problem decomposition, such as obstacle detection, lane marking, path planning, and management, the authors system optimizes all processing step simultaneously. An example of using the method on real robot is given.

• Record URL:
  https://doi.org/10.1016/j.procs.2021.04.141
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S187705092100956X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770509
• Supplemental Notes:
  • © 2021 Anna N. Daryina, et al. Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Daryina, Anna N
  • Prokopiev, Igor V
• Conference:
  • 14th International Symposium "Intelligent Systems"
  • Location: Moscow , Russia
  • Date: 2020-12-14 to 2020-12-16
• Publication Date: 2021

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 217-226
• Serial:
  • Procedia Computer Science
  • Volume: 186
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0509
  • Serial URL: http://www.sciencedirect.com/science/journal/18770509
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Command guidance; Digital cameras; Neural networks; Obstructions (Navigation); Optimization; Real time control; Traffic characteristics; Trajectory control
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01774820
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 23 2021 2:37PM