用于交通场景模拟的滚动前瞻扩散
Rolling Ahead Diffusion for Traffic Scene Simulation
AI总结：
针对现有交通场景生成模型反应性与计算效率难以兼顾的问题，提出基于滚动扩散的模型，同时预测下一步及部分带噪远未来步骤，在反应性与计算效率间实现折中且比基于扩散的AR更高效。
摘要：
真实的驾驶模拟要求NPC不仅模仿自然驾驶行为，还需对其他模拟智能体的行为做出反应。近年来，基于扩散的场景生成技术的发展聚焦于通过联合建模场景中所有智能体的运动来创建多样化且真实的交通场景。然而，当智能体的运动偏离其建模轨迹时，这些交通场景无法做出反应——例如，自车智能体可能由独立的运动规划器控制。为了用联合场景模型生成具有反应性的场景，模型必须基于新观测以模型预测控制（MPC）方式在每个时间步重新生成场景。尽管具有反应性，但该方法耗时，因为每个模拟步骤都要为所有NPC生成一个完整的可能未来。另一种方法是利用自回归模型（AR）仅预测所有NPC的紧邻下一步未来，虽速度更快，但缺乏高级规划能力。我们提出一种基于滚动扩散的交通场景生成模型，通过同时预测下一步未来和部分带噪的更远未来步骤，融合了上述两种方法的优势。我们表明，该模型与基于扩散模型的AR相比更高效，在反应性和计算效率之间实现了有益的折中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
