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 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% and along the interaction metric by 9.9%.
We ablate our modeling choices in full autonomy and 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.

通过离散序列建模方法，我们研究了自动驾驶开发中模拟基于记录驾驶日志的动态驾驶场景的挑战。使用简单的数据驱动分词方案，将轨迹离散化到厘米级分辨率，并采用类似 GPT 模型的编码器 - 解码器来建模车辆、行人和骑车人在驾驶场景中的互动。通过在模型中抽样场景，我们展示了该模型具有最先进的真实性，且在 Waymo Sim Agents Benchmark 上超越先前的工作，其中真实性值成功提高了 3.3%，与其他模型相比，互动度提高了 9.9%。我们还在完全自动驾驶和部分自动驾驶环境中分析了我们的建模选择，并展示了我们的模型学到的表示可以快速适用于改进 nuScenes 的性能。此外，我们还对模型的参数数量和数据集规模进行了可扩展性评估，并利用模型得出的密度估计量化了上下文长度和时间间隔内交互对于交通建模任务的重要性。