An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

Details

• Creators:
  Vitela, Javier E. ; Hanebutte, Ulf R. ; Reifman, Jaques
• Corporate Creators:
  Argonne National Laboratory
• Corporate Contributors:
  United States. Department of Energy
• Subject/TRT Terms:
  • Intelligent transportation systems
  • Applications
  • Neural networks
  • Autonomous intelligent cruise control
  • Nonlinear systems
  • Dynamic models
  • Intelligent Controllers
• Resource Type:
  Research Paper
• Geographical Coverage:
  United States
• Corporate Publisher:
  United States. Department of Energy. Nuclear Energy Programs
• Abstract:
  An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/13536/dot_13536_DS1.pdf
• File Type:
  [PDF - 807.97 KB]
• Collection(s):
  Autonomous Vehicles ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:428e1852f5a2557037faf984768ad0572fddfc1186ff240c1df619cdb91783c9bc633cd9d676f34eae8c8e4ba344b22db9f1e160551b60eff5b894e61e820183