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Motion Selectivity of Neurons in Self-driving Networks

  • Baladitya YellapragadaEmail author
  • Alexander AndersonEmail author
  • Stella YuEmail author
  • Karl ZipserEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11133)

Abstract

We investigated if optical flow filters were implicitly learned by a neural network trained to drive a vehicle. The network was not trained to predict optical flow across the frames, but, through a series of controlled experiments, we claim that optical flow filters are present in the network. However, this appears to be only the case for sideways flows more relevant for steering predictions. For motor throttle predictions, the network looks at the variance of the pixels over time rather than computing optical flow. In addition, the filters that are likely used for motor throttle predictions dominate primarily in the middle of the network.

Keywords

Optical flow Motion selectivity Self-driving Autonomous driving Convolutional neural network Stereoscopic disparity 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of California, BerkeleyBerkeleyUSA
  2. 2.International Computer Science InstituteBerkeleyUSA
  3. 3.Redwood Center for Theoretical NeuroscienceUniversity of CaliforniaBerkeleyUSA

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