用多维缩放技术学习道路网络等距嵌入，提升自动驾驶泛化能力
Learning Isometric Embeddings of Road Networks using Multidimensional Scaling
- 通过多维缩放将道路图结构映射到低维特征空间
- 实现不同道路场景的统一表征，支持复杂环境下的运动规划
- 适合研究自动驾驶通用性与神经网络特征空间设计者
当前基于学习的自动驾驶应用泛化能力有限，仅能覆盖狭窄的道路场景。一个具备泛化能力的方法应能捕捉多种道路结构与拓扑，并考虑交通参与者及环境动态变化，使车辆能在最复杂情况下完成导航与运动规划。设计能涵盖各类道路场景的神经网络运动规划器特征空间仍是开放挑战。本文针对此问题，探讨利用多维缩放（MDS）技术处理道路网络图表示，构建此类特征空间。分析了适用于自动驾驶场景的先进图表示与MDS方法，并讨论节点嵌入策略，以简化学习过程并实现降维。
原文摘要 · Abstract (English)
The lack of generalization in learning-based autonomous driving applications is shown by the narrow range of road scenarios that vehicles can currently cover. A generalizable approach should capture many distinct road structures and topologies, as well as consider traffic participants, and dynamic changes in the environment, so that vehicles can navigate and perform motion planning tasks even in the most difficult situations. Designing suitable feature spaces for neural network-based motion planers that encapsulate all kinds of road scenarios is still an open research challenge. This paper tackles this learning-based generalization challenge and shows how graph representations of road networks can be leveraged by using multidimensional scaling (MDS) techniques in order to obtain such feature spaces. State-of-the-art graph representations and MDS approaches are analyzed for the autonomous driving use case. Finally, the option of embedding graph nodes is discussed in order to perform easier learning procedures and obtain dimensionality reduction.
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