论文标题
专家持久：专家知识指导的潜在空间用于交通方案
Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios
论文作者
论文摘要
基于场景的自动驾驶汽车测试需要聚类的交通情况和检测新型方案类型。这些任务受益于良好的相似性度量或交通情况的良好表示。在这项工作中，提出了针对流量情景的专家知识辅助表示。如此形成的潜在空间用于成功聚类和新型场景类型检测。专家知识用于定义目的，即交通场景的潜在表示。它是通过这些目标设计的网络体系结构和损失的方式，从而结合了专家知识。提出了一种自动采矿策略，因此不需要手动标记。结果显示与基线方法相比的性能优势。此外，对潜在空间进行了广泛的分析。
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.
