Trajeglish: Traffic Modeling as Next-Token Prediction
arxiv(2023)
摘要
A longstanding challenge for self-driving development is simulating dynamic
driving scenarios seeded from recorded driving logs. In pursuit of this
functionality, we apply tools from discrete sequence modeling to model how
vehicles, pedestrians and cyclists interact in driving scenarios. Using a
simple data-driven tokenization scheme, we discretize trajectories to
centimeter-level resolution using a small vocabulary. We then model the
multi-agent sequence of discrete motion tokens with a GPT-like encoder-decoder
that is autoregressive in time and takes into account intra-timestep
interaction between agents. Scenarios sampled from our model exhibit
state-of-the-art realism; our model tops the Waymo Sim Agents Benchmark,
surpassing prior work along the realism meta metric by 3.3
interaction metric by 9.9
partial autonomy settings, and show that the representations learned by our
model can quickly be adapted to improve performance on nuScenes. We
additionally evaluate the scalability of our model with respect to parameter
count and dataset size, and use density estimates from our model to quantify
the saliency of context length and intra-timestep interaction for the traffic
modeling task.
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