论文标题
深度表示学习和交通场景的聚类
Deep Representation Learning and Clustering of Traffic Scenarios
论文作者
论文摘要
确定交通场景空间是对自动驾驶功能的处理和覆盖范围评估的主要挑战。与主要基于方案并依赖专家知识的当前方法相反，我们介绍了两个数据驱动的自动编码模型，以了解交通场景的潜在表示。首先是基于CNN的时空模型，该模型可自动编码交通参与者的位置。其次，我们开发了一个纯粹的基于时间RNN的模型，该模型自动编码一个集合。为了处理无序的集合数据，我们必须合并置换不变性属性。最后，我们展示了如何将潜在方案嵌入方式用于聚类流量方案和相似性检索。
Determining the traffic scenario space is a major challenge for the homologation and coverage assessment of automated driving functions. In contrast to current approaches that are mainly scenario-based and rely on expert knowledge, we introduce two data driven autoencoding models that learn a latent representation of traffic scenes. First is a CNN based spatio-temporal model that autoencodes a grid of traffic participants' positions. Secondly, we develop a pure temporal RNN based model that auto-encodes a sequence of sets. To handle the unordered set data, we had to incorporate the permutation invariance property. Finally, we show how the latent scenario embeddings can be used for clustering traffic scenarios and similarity retrieval.
