Transferring Autonomous Driving Knowledge on Simulated and Real Intersections

11/30/2017
by   David Isele, et al.
0

We view intersection handling on autonomous vehicles as a reinforcement learning problem, and study its behavior in a transfer learning setting. We show that a network trained on one type of intersection generally is not able to generalize to other intersections. However, a network that is pre-trained on one intersection and fine-tuned on another performs better on the new task compared to training in isolation. This network also retains knowledge of the prior task, even though some forgetting occurs. Finally, we show that the benefits of fine-tuning hold when transferring simulated intersection handling knowledge to a real autonomous vehicle.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset