  Unlike popular modularized framework, end-to-end autonomous driving seeks to
solve the perception, decision and control problems in an integrated way, which
can be more adapting to new scenarios and easier to generalize at scale.
However, existing end-to-end approaches are often lack of interpretability, and
can only deal with simple driving tasks like lane keeping. In this paper, we
propose an interpretable deep reinforcement learning method for end-to-end
autonomous driving, which is able to handle complex urban scenarios. A
sequential latent environment model is introduced and learned jointly with the
reinforcement learning process. With this latent model, a semantic birdeye mask
can be generated, which is enforced to connect with a certain intermediate
property in today's modularized framework for the purpose of explaining the
behaviors of learned policy. The latent space also significantly reduces the
sample complexity of reinforcement learning. Comparison tests with a simulated
autonomous car in CARLA show that the performance of our method in urban
scenarios with crowded surrounding vehicles dominates many baselines including
DQN, DDPG, TD3 and SAC. Moreover, through masked outputs, the learned policy is
able to provide a better explanation of how the car reasons about the driving
environment. The codes and videos of this work are available at our github repo
and project website.
