Learning to drive fast on a DuckieTown highway

We train a small Nvidia AI JetRacer to follow the road on a small DuckieTown highway. In the real-world, roads do not always have the same appearance, so the system should not be trained on lane markings alone but on the complete view of the front camera. To make this possible, the system is trained in simulation using a recent reinforcement learning approach in an end-to-end fashion. This driving experience is then transferred to the circumstances encountered on a real track. Transfer learning is surprisingly successful, although this method is very sensitive to the details of the vehicle dynamics. We trained multiple models at different speeds and evaluated their performance both in simulation and in the real world. Increasing the velocity proves difficult, as the learned policy breaks down at higher speeds. The result is a small Nvidia AI JetRacer, which is able to drive around a DuckieTown highway, based
on simulated experiences.