Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model called State Transformer (STR). STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task. Our approach unites trajectory generation problems with other sequence modeling problems, powering rapid iterations with breakthroughs in neighbor domains such as language modeling. Remarkably, experimental results reveal that large trajectory models (LTMs), such as STR, adhere to the scaling laws by presenting outstanding adaptability and learning efficiency. Qualitative results further demonstrate that LTMs are capable of making plausible predictions in scenarios that diverge significantly from the training data distribution. LTMs also learn to make complex reasonings for long-term planning, without explicit loss designs or costly high-level annotations.

翻译：运动预测与规划是自动驾驶中的关键任务，近年来研究重点转向基于机器学习的方法。其挑战包括理解多样化的道路拓扑结构、推理长时序交通动态、解读异质行为，并在大连续状态空间中生成策略。受大语言模型通过模型扩展解决类似复杂性的成功启发，我们提出了一种名为状态变换器（STR）的可扩展轨迹模型。STR通过将观测、状态和动作整合为统一序列建模任务，重新表述了运动预测与运动规划问题。我们的方法将轨迹生成问题与其他序列建模问题统一起来，借助语言建模等邻域（如语言建模）的突破性进展实现快速迭代。值得注意的是，实验结果表明，大型轨迹模型（LTM，如STR）通过展现出色的适应性和学习效率遵循缩放定律。定性结果进一步表明，LTM能够在显著偏离训练数据分布的场景中生成合理的预测。LTM还能学习进行长期规划的复杂推理，无需显式损失设计或昂贵的高层标注。