  In this paper, we present a state-of-the-art reinforcement learning method
for autonomous driving. Our approach employs temporal difference learning in a
Bayesian framework to learn vehicle control signals from sensor data. The agent
has access to images from a forward facing camera, which are preprocessed to
generate semantic segmentation maps. We trained our system using both ground
truth and estimated semantic segmentation input. Based on our observations from
a large set of experiments, we conclude that training the system on ground
truth input data leads to better performance than training the system on
estimated input even if estimated input is used for evaluation. The system is
trained and evaluated in a realistic simulated urban environment using the
CARLA simulator. The simulator also contains a benchmark that allows for
comparing to other systems and methods. The required training time of the
system is shown to be lower and the performance on the benchmark superior to
competing approaches.
