  Motion prediction and planning are vital tasks in autonomous driving, and
recent efforts have shifted to machine learning-based approaches. The
challenges include understanding diverse road topologies, reasoning traffic
dynamics over a long time horizon, interpreting heterogeneous behaviors, and
generating policies in a large continuous state space. Inspired by the success
of large language models in addressing similar complexities through model
scaling, we introduce a scalable trajectory model called State Transformer
(STR). STR reformulates the motion prediction and motion planning problems by
arranging observations, states, and actions into one unified sequence modeling
task. Our approach unites trajectory generation problems with other sequence
modeling problems, powering rapid iterations with breakthroughs in neighbor
domains such as language modeling. Remarkably, experimental results reveal that
large trajectory models (LTMs), such as STR, adhere to the scaling laws by
presenting outstanding adaptability and learning efficiency. Qualitative
results further demonstrate that LTMs are capable of making plausible
predictions in scenarios that diverge significantly from the training data
distribution. LTMs also learn to make complex reasonings for long-term
planning, without explicit loss designs or costly high-level annotations.
