  Data-driven autonomous driving motion generation tasks are frequently
impacted by the limitations of dataset size and the domain gap between
datasets, which precludes their extensive application in real-world scenarios.
To address this issue, we introduce SMART, a novel autonomous driving motion
generation paradigm that models vectorized map and agent trajectory data into
discrete sequence tokens. These tokens are then processed through a
decoder-only transformer architecture to train for the next token prediction
task across spatial-temporal series. This GPT-style method allows the model to
learn the motion distribution in real driving scenarios. SMART achieves
state-of-the-art performance across most of the metrics on the generative Sim
Agents challenge, ranking 1st on the leaderboards of Waymo Open Motion Dataset
(WOMD), demonstrating remarkable inference speed. Moreover, SMART represents
the generative model in the autonomous driving motion domain, exhibiting
zero-shot generalization capabilities: Using only the NuPlan dataset for
training and WOMD for validation, SMART achieved a competitive score of 0.71 on
the Sim Agents challenge. Lastly, we have collected over 1 billion motion
tokens from multiple datasets, validating the model's scalability. These
results suggest that SMART has initially emulated two important properties:
scalability and zero-shot generalization, and preliminarily meets the needs of
large-scale real-time simulation applications. We have released all the code to
promote the exploration of models for motion generation in the autonomous
driving field.
