  Over the past few years there is a growing interest in the learning-based
self driving system. To ensure safety, such systems are first developed and
validated in simulators before being deployed in the real world. However, most
of the existing driving simulators only contain a fixed set of scenes and a
limited number of configurable settings. That might easily cause the
overfitting issue for the learning-based driving systems as well as the lack of
their generalization ability to unseen scenarios. To better evaluate and
improve the generalization of end-to-end driving, we introduce an open-ended
and highly configurable driving simulator called PGDrive, following a key
feature of procedural generation. Diverse road networks are first generated by
the proposed generation algorithm via sampling from elementary road blocks.
Then they are turned into interactive training environments where traffic flows
of nearby vehicles with realistic kinematics are rendered. We validate that
training with the increasing number of procedurally generated scenes
significantly improves the generalization of the agent across scenarios of
different traffic densities and road networks. Many applications such as
multi-agent traffic simulation and safe driving benchmark can be further built
upon the simulator. To facilitate the joint research effort of end-to-end
driving, we release the simulator and pretrained models at
https://decisionforce.github.io/pgdrive
