  Real-world autonomous driving (AD) especially urban driving involves many
corner cases. The lately released AD simulator CARLA v2 adds 39 common events
in the driving scene, and provide more quasi-realistic testbed compared to
CARLA v1. It poses new challenge to the community and so far no literature has
reported any success on the new scenarios in V2 as existing works mostly have
to rely on specific rules for planning yet they cannot cover the more complex
cases in CARLA v2. In this work, we take the initiative of directly training a
planner and the hope is to handle the corner cases flexibly and effectively,
which we believe is also the future of AD. To our best knowledge, we develop
the first model-based RL method named Think2Drive for AD, with a world model to
learn the transitions of the environment, and then it acts as a neural
simulator to train the planner. This paradigm significantly boosts the training
efficiency due to the low dimensional state space and parallel computing of
tensors in the world model. As a result, Think2Drive is able to run in an
expert-level proficiency in CARLA v2 within 3 days of training on a single
A6000 GPU, and to our best knowledge, so far there is no reported success
(100\% route completion)on CARLA v2. We also propose CornerCase-Repository, a
benchmark that supports the evaluation of driving models by scenarios.
Additionally, we propose a new and balanced metric to evaluate the performance
by route completion, infraction number, and scenario density, so that the
driving score could give more information about the actual driving performance.
