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% 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.

翻译：自动驾驶发展中的一个长期挑战是从记录的驾驶日志中模拟动态驾驶场景。为实现这一功能，我们运用离散序列建模工具，对车辆、行人和骑车人在驾驶场景中的交互进行建模。采用简单的数据驱动词元化方案，我们使用小型词元集将轨迹离散化至厘米级精度。随后，我们利用类似GPT的编码器-解码器架构对多智能体离散运动词元序列进行建模，该架构在时间上具有自回归特性，并考虑了同时间步内智能体间的交互。从我们的模型中采样的场景展现出最先进的逼真度；该模型在Waymo智能体模拟基准测试中位居榜首，在逼真度元指标和交互指标上分别超越先前工作3.3%和9.9%。我们在完全自主和部分自主环境下对建模选择进行了消融实验，并证明模型所学表示可快速迁移至nuScenes数据集以提升性能。此外，我们评估了模型在参数量和数据集规模上的扩展性，并利用模型密度估计量化了上下文长度和同时间步交互在交通建模任务中的重要性。