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.
- 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