We propose an approach to simulating trajectories of multiple interacting agents (road users) based on transformers and probabilistic graphical models (PGMs), and apply it to the Waymo SimAgents challenge. The transformer baseline is based on the MTR model, which predicts multiple future trajectories conditioned on the past trajectories and static road layout features. We then improve upon these generated trajectories using a PGM, which contains factors which encode prior knowledge, such as a preference for smooth trajectories, and avoidance of collisions with static obstacles and other moving agents. We perform (approximate) MAP inference in this PGM using the Gauss-Newton method. Finally we sample $K=32$ trajectories for each of the $N \sim 100$ agents for the next $T=8 \Delta$ time steps, where $\Delta=10$ is the sampling rate per second. Following the Model Predictive Control (MPC) paradigm, we only return the first element of our forecasted trajectories at each step, and then we replan, so that the simulation can constantly adapt to its changing environment. We therefore call our approach "Model Predictive Simulation" or MPS. We show that MPS improves upon the MTR baseline, especially in safety critical metrics such as collision rate. Furthermore, our approach is compatible with any underlying forecasting model, and does not require extra training, so we believe it is a valuable contribution to the community.

翻译：我们提出了一种基于Transformer与概率图模型（PGMs）的多交互智能体（道路使用者）轨迹仿真方法，并将其应用于Waymo SimAgents挑战赛。Transformer基线模型基于MTR模型，该模型根据历史轨迹与静态道路布局特征预测多条未来轨迹。随后，我们通过概率图模型对这些生成轨迹进行优化，该模型包含编码先验知识的因子，例如对平滑轨迹的偏好、避免与静态障碍物及其他移动智能体发生碰撞等。我们使用高斯-牛顿法在该概率图模型中进行（近似）最大后验概率推断。最终，我们对$N \sim 100$个智能体中的每一个，在后续$T=8 \Delta$时间步内采样$K=32$条轨迹，其中$\Delta=10$为每秒采样率。遵循模型预测控制（MPC）范式，我们在每一步仅返回预测轨迹的首个元素，随后重新规划，使仿真能够持续适应动态变化的环境。因此，我们将该方法称为“模型预测仿真”（MPS）。实验表明，MPS在MTR基线上实现了性能提升，尤其在碰撞率等安全关键指标上表现显著。此外，本方法与任何底层预测模型兼容，且无需额外训练，我们认为这对相关研究领域具有重要价值。