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.

翻译：数据驱动的自动驾驶运动生成任务常受限于数据集规模及数据集间的领域差异，这阻碍了其在真实场景中的广泛应用。为解决此问题，我们提出了SMART，一种新颖的自动驾驶运动生成范式，它将矢量化地图与智能体轨迹数据建模为离散的序列词元。这些词元随后通过仅解码器的Transformer架构进行处理，以训练其在时空序列上的下一词元预测任务。这种GPT风格的方法使模型能够学习真实驾驶场景中的运动分布。SMART在生成式Sim Agents挑战赛的大部分指标上取得了最先进的性能，在Waymo开放运动数据集（WOMD）的排行榜上位列第一，并展现出卓越的推理速度。此外，SMART作为自动驾驶运动领域的生成模型，展现了零样本泛化能力：仅使用NuPlan数据集进行训练并以WOMD进行验证，SMART在Sim Agents挑战赛上获得了0.71的竞争性分数。最后，我们从多个数据集中收集了超过10亿个运动词元，验证了模型的可扩展性。这些结果表明，SMART已初步模拟了两个重要特性：可扩展性与零样本泛化能力，并初步满足了大规模实时仿真应用的需求。我们已开源全部代码，以促进自动驾驶领域运动生成模型的探索。