Estimation of Driving Resistance Coefficients with Neural Network and Its Low-Power Implementation using FPGA

It is important to acquire the states of a car not only on test environments but also on real roads to realize a carbon-neutral society. However, it is labor- and cost-inefficient due to the need of many in-car sensors. To alleviate this issue, this research acquires driving resistance coefficients by not directly measuring them but by predicting them using a neural network. In addition, the authors implement the proposed neural network model on an FPGA to enable its execution within the spare power of a car.

Language

  • English
  • Japanese

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Filing Info

  • Accession Number: 01847715
  • Record Type: Publication
  • Source Agency: Japan Science and Technology Agency (JST)
  • Files: TRIS, JSTAGE
  • Created Date: May 31 2022 3:36PM