Abstract
Realistic driving simulation requires that NPCs not only mimic natural driving behaviors but also react to the behavior of other simulated agents. Recent developments in diffusion-based scenario generation focus on creating diverse and realistic traffic scenarios by jointly modelling the motion of all the agents in the scene. However, these traffic scenarios do not react when the motion of agents deviates from their modelled trajectories. For example, the ego-agent can be controlled by a stand along motion planner. To produce reactive scenarios with joint scenario models, the model must regenerate the scenario at each timestep based on new observations in a Model Predictive Control (MPC) fashion. Although reactive, this method is time-consuming, as one complete possible future for all NPCs is generated per simulation step. Alternatively, one can utilize an autoregressive model (AR) to predict only the immediate next-step future for all NPCs. Although faster, this method lacks the capability for advanced planning. We present a rolling diffusion based traffic scene generation model which mixes the benefits of both methods by predicting the next step future and simultaneously predicting partially noised further future steps at the same time. We show that such model is efficient compared to diffusion model based AR, achieving a beneficial compromise between reactivity and computational efficiency.
Abstract (translated)
现实中的驾驶模拟要求NPC不仅模仿自然的驾驶行为，还能够对其他仿真代理的行为作出反应。近期基于扩散模型的情景生成技术着重于通过共同建模场景中所有代理的运动来创建多样且逼真的交通情景。然而，这些交通情景在代理的运动偏离其预设轨迹时并不会做出反应。例如，主控代理（ego-agent）可以由独立的路径规划器控制。为了利用联合情景模型生成响应式的情景，模型必须基于新的观察结果以模型预测控制（MPC）的方式，在每个时间步重新生成场景。尽管这种方法具备响应性，但它非常耗时，因为每模拟一步就需要为所有NPC生成一个完整的可能未来。 另一种方法是使用自回归模型（AR），仅预测所有NPC的下一步未来的运动。虽然此法较快，但缺乏对未来进行深入规划的能力。我们提出了一种滚动扩散基交通场景生成模型，该模型结合了上述两种方法的优点：它同时预测接下来一步的情景和部分模糊化后的未来步骤。我们展示了这种模型相比于基于扩散模型的自回归模型在效率上具有优势，并且能够在响应性和计算效率之间取得有利的平衡。 这种方法通过混合预测方式，即预测下一步的具体行为同时对更远期的行为做出概率性推测，从而实现了更高的灵活性和效率，在保持场景真实性的前提下提高了系统的响应速度。
URL
https://arxiv.org/abs/2502.09587
