Deep Reinforcement Learning for Vehicle Control based on Segmentation

The concept

Domain-independent Segmentation Network Training

An example of a simulator image and its label.

Domain adapting the segmentation network

  • Training on combined Source & Target domain labelled set (S&T)
  • Histogram Matching source images with target samples (HM)
  • Domain converting source set using CycleGAN
  • Semi-supervised DA via Minimax Entropy (MME)
Comparison of domain adaptation results on three random images.
Numerical evaluation results. Note that the test set count was relatively small.
Qualitative evaluation results based on a prediction on two real-domain videos.

Deep Reinforcement Learning-based Agent Training

The final result of our agent. As you can see, wiggling is practically eliminated and we are rushing ahead along the track.

Future plans

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Deep Learning and AI solutions from Budapest University of Technology and Economics. http://smartlab.tmit.bme.hu/

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SmartLab AI

SmartLab AI

Deep Learning and AI solutions from Budapest University of Technology and Economics. http://smartlab.tmit.bme.hu/

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