用户用文字/数值指令控制交通模拟，实现高精度行为调控。
Promptable Closed-loop Traffic Simulation
- 通过多模态提示控制每个交通参与者的行为意图
- 在无提示时仍达Waymo挑战赛竞争力水平
- 适合自动驾驶测试与可控场景生成研究
仿真在自动驾驶安全高效研发中至关重要。理想的仿真系统应生成真实、响应迅速且可控制的交通模式。本文提出ProSim，一个支持多模态提示的闭环交通仿真框架。用户可通过数值、类别或文本提示指导每个智能体的行为与意图，系统以闭环方式演进交通场景，建模各参与者间的交互。实验表明，无论有无提示，ProSim均表现出高可控性，在未加提示时达到Waymo Sim Agents Challenge的竞争力水平。为推动该方向研究，我们构建了ProSim-Instruct-520k数据集，包含超过520,000个真实驾驶场景及1000万条文本提示。代码、数据与标注工具将公开于https://ariostgx.github.io/ProSim。
原文摘要 · Abstract (English)
Simulation stands as a cornerstone for safe and efficient autonomous driving development. At its core a simulation system ought to produce realistic, reactive, and controllable traffic patterns. In this paper, we propose ProSim, a multimodal promptable closed-loop traffic simulation framework. ProSim allows the user to give a complex set of numerical, categorical or textual prompts to instruct each agent's behavior and intention. ProSim then rolls out a traffic scenario in a closed-loop manner, modeling each agent's interaction with other traffic participants. Our experiments show that ProSim achieves high prompt controllability given different user prompts, while reaching competitive performance on the Waymo Sim Agents Challenge when no prompt is given. To support research on promptable traffic simulation, we create ProSim-Instruct-520k, a multimodal prompt-scenario paired driving dataset with over 10M text prompts for over 520k real-world driving scenarios. We will release code of ProSim as well as data and labeling tools of ProSim-Instruct-520k at https://ariostgx.github.io/ProSim.
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