Autonomous vehicles hold great promise in improving the future of transportation. The driving models used in these vehicles are based on neural networks, which can be difficult to validate. However, ensuring the safety of these models is crucial. Traditional field tests can be costly, time-consuming, and dangerous. To address these issues, scenario-based closed-loop simulations can simulate many hours of vehicle operation in a shorter amount of time and allow for specific investigation of important situations. Nonetheless, the detection of relevant traffic scenarios that also offer substantial testing benefits remains a significant challenge. To address this need, in this paper we build an imitation learning based trajectory prediction for traffic participants. We combine an image-based (CNN) approach to represent spatial environmental factors and a graph-based (GNN) approach to specifically represent relations between traffic participants. In our understanding, traffic scenes that are highly interactive due to the network's significant utilization of the social component are more pertinent for a validation process. Therefore, we propose to use the activity of such sub networks as a measure of interactivity of a traffic scene. We evaluate our model using a motion dataset and discuss the value of the relationship information with respect to different traffic situations.

翻译：自动驾驶技术在改善未来交通方面具有巨大潜力。这些车辆所采用的驾驶模型基于神经网络，其验证过程存在一定难度。然而，确保这些模型的安全性至关重要。传统的实地测试往往成本高昂、耗时费力且存在安全隐患。为解决这些问题，基于场景的闭环仿真能够在较短时间内模拟数小时的车辆运行，并允许针对重要情境进行专项研究。尽管如此，如何检测既能提供实质性测试效益又具备相关性的交通场景仍是一项重大挑战。为此，本文构建了一种基于模仿学习的交通参与者轨迹预测模型。我们结合基于图像的方法（CNN）表征空间环境因素，并采用基于图的方法（GNN）专门表征交通参与者之间的相互关系。我们认为，那些因网络对社会交互组件的显著利用而具有高度交互性的交通场景，对验证过程更具相关性。因此，我们提出将此类子网络的激活程度作为衡量交通场景交互性的指标。我们利用运动数据集对所提模型进行验证，并探讨了关系信息在不同交通情境下的价值。