Deep reinforcement learning of driving using virtual paths

Objective: Interactive training of deep networks for vision-based autonomous systems. Reproducible machine learning of navigation and path following in autonomous vehicles such as cars and drones

PIs: Michael Felsberg (LiU), Kai-Kuang Ma (NTU)

Targeted problem: Generate learning data in an interactive, dynamic, and adaptive way.

Approach:

  • Virtual paths, i.e., synthetic environments are projected onto the ground.
  • Small-scale vehicles drive autonomously in these synthetic scenarios and acquire their training data in a dynamic environment that can adapt to the learning progress.
  • Allowing for full control of all environmental parameters, for appropriate
  • Generation of training data by photo realistic rendering of virtual images during real navigation and path following

 

NTU Deep reinforcement learning 1

 

NTU Deep reinforcement learning 2