Clustering traffic scenarios and detecting novel scenario types are required for scenario-based testing of autonomous vehicles. These tasks benefit from either good similarity measures or good representations for the traffic scenarios. In this work, an expert-knowledge aided representation learning for traffic scenarios is presented. The latent space so formed is used for successful clustering and novel scenario type detection. Expert-knowledge is used to define objectives that the latent representations of traffic scenarios shall fulfill. It is presented, how the network architecture and loss is designed from these objectives, thereby incorporating expert-knowledge. An automatic mining strategy for traffic scenarios is presented, such that no manual labeling is required. Results show the performance advantage compared to baseline methods. Additionally, extensive analysis of the latent space is performed.

翻译：在对自主车辆进行基于情景的测试时,需要组合交通情景和探测新情景类型,这些任务受益于良好的类似措施或交通情景的恰当表述;在这项工作中,介绍了对交通情景的专家知识辅助代表性学习;由此形成的潜在空间用于成功的集群和新型情景探测;利用专家知识确定交通情景潜在表现形式应实现的目标;介绍网络架构和损失如何从这些目标中设计,从而纳入专家知识;介绍交通情景的自动采矿战略,因此不需要人工标识;结果显示与基线方法相比的性能优势;对潜在空间进行广泛分析。