This paper proposes a method for on-demand scenario generation in simulation, grounded on real-world data. Evaluating the behaviour of Autonomous Vehicles (AVs) in both safety-critical and regular scenarios is essential for assessing their robustness before real-world deployment. By integrating scenarios derived from real-world datasets into the simulation, we enhance the plausibility and validity of testing sets. This work introduces a novel approach that employs temporal scene graphs to capture evolving spatiotemporal relationships among scene entities from a real-world dataset, enabling the generation of dynamic scenarios in simulation through Graph Neural Networks (GNNs). User-defined action and criticality conditioning are used to ensure flexible, tailored scenario creation. Our model significantly outperforms the benchmarks in accurately predicting links corresponding to the requested scenarios. We further evaluate the validity and compatibility of our generated scenarios in an off-the-shelf simulator.

翻译：本文提出一种基于真实世界数据的按需仿真场景生成方法。评估自动驾驶车辆在安全关键场景和常规场景中的行为，对于其在真实世界部署前评估其鲁棒性至关重要。通过将源自真实世界数据集的场景整合到仿真中，我们增强了测试集的合理性与有效性。本工作提出一种创新方法，该方法使用时序场景图从真实世界数据集中捕捉场景实体间不断演化的时空关系，并通过图神经网络在仿真中生成动态场景。用户定义的动作和关键性条件被用于确保灵活、定制化的场景创建。我们的模型在准确预测与请求场景对应的链接方面显著优于基准方法。我们进一步在一个现成的仿真器中评估了所生成场景的有效性与兼容性。