  Prediction, decision-making, and motion planning are essential for autonomous
driving. In most contemporary works, they are considered as individual modules
or combined into a multi-task learning paradigm with a shared backbone but
separate task heads. However, we argue that they should be integrated into a
comprehensive framework. Although several recent approaches follow this scheme,
they suffer from complicated input representations and redundant framework
designs. More importantly, they can not make long-term predictions about future
driving scenarios. To address these issues, we rethink the necessity of each
module in an autonomous driving task and incorporate only the required modules
into a minimalist autonomous driving framework. We propose BEVGPT, a generative
pre-trained large model that integrates driving scenario prediction,
decision-making, and motion planning. The model takes the bird's-eye-view (BEV)
images as the only input source and makes driving decisions based on
surrounding traffic scenarios. To ensure driving trajectory feasibility and
smoothness, we develop an optimization-based motion planning method. We
instantiate BEVGPT on Lyft Level 5 Dataset and use Woven Planet L5Kit for
realistic driving simulation. The effectiveness and robustness of the proposed
framework are verified by the fact that it outperforms previous methods in 100%
decision-making metrics and 66% motion planning metrics. Furthermore, the
ability of our framework to accurately generate BEV images over the long term
is demonstrated through the task of driving scenario prediction. To the best of
our knowledge, this is the first generative pre-trained large model for
autonomous driving prediction, decision-making, and motion planning with only
BEV images as input.
