We present a learning-based planner that aims to robustly drive a vehicle by mimicking
human drivers' driving behavior. We leverage a mid-to-mid approach that allows us to
manipulate the input to our imitation learning network freely. With that in mind, we propose a
novel feedback synthesizer for data augmentation. It allows our agent to gain more driving
experience in various previously unseen environments that are likely to encounter, thus
improving overall performance. This is in contrast to prior works that rely purely on random …