按需生成自动驾驶测试用的动态交通场景。
GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulation
- 基于时序图神经网络，根据用户偏好生成动态交通场景图。
- 生成场景在关键性、多样性上优于基线模型，准确率更高。
- 适合自动驾驶仿真测试与安全验证，支持定制化需求。
在真实部署前，对自动驾驶汽车（AV）在安全关键且多样的交通场景中进行测试与验证至关重要。然而，在仿真环境中手动构建此类场景仍面临巨大挑战，耗时且效率低。本文提出一种新方法，可按需生成对应多样交通场景的动态时序场景图，支持用户自定义偏好，如车辆行为、动态主体集合及关键性等级。该方法采用时序图神经网络（GNN），学习自车、其他交通参与者与静态结构之间的关系，依据真实世界的时空交互模式，并受语义本体约束，仅预测合法链接。实验表明，该模型在生成符合要求场景链接方面显著优于基线。我们将预测结果渲染至仿真环境，进一步验证其作为自动驾驶测试平台的有效性。
原文摘要 · Abstract (English)
Testing and validating Autonomous Vehicle (AV) performance in safety-critical and diverse scenarios is crucial before real-world deployment. However, manually creating such scenarios in simulation remains a significant and time-consuming challenge. This work introduces a novel method that generates dynamic temporal scene graphs corresponding to diverse traffic scenarios, on-demand, tailored to user-defined preferences, such as AV actions, sets of dynamic agents, and criticality levels. A temporal Graph Neural Network (GNN) model learns to predict relationships between ego-vehicle, agents, and static structures, guided by real-world spatiotemporal interaction patterns and constrained by an ontology that restricts predictions to semantically valid links. Our model consistently outperforms the baselines in accurately generating links corresponding to the requested scenarios. We render the predicted scenarios in simulation to further demonstrate their effectiveness as testing environments for AV agents.
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