The progress in autonomous driving is also due to the increased availability
of vast amounts of training data for the underlying machine learning
approaches. Machine learning systems are generally known to lack robustness,
e.g., if the training data did rarely or not at all cover critical situations.
The challenging task of corner case detection in video, which is also somehow
related to unusual event or anomaly detection, aims at detecting these unusual
situations, which could become critical, and to communicate this to the
autonomous driving system (online use case). Such a system, however, could be
also used in offline mode to screen vast amounts of data and select only the
relevant situations for storing and (re)training machine learning algorithms.
So far, the approaches for corner case detection have been limited to videos
recorded from a fixed camera, mostly for security surveillance. In this paper,
we provide a formal definition of a corner case and propose a system framework
for both the online and the offline use case that can handle video signals from
front cameras of a naturally moving vehicle and can output a corner case score.

该研究论文提供了 “corner case” 的定义，并提出了一种既可在线使用，也可脱机筛选、重复训练机器学习算法的系统框架，该系统能够处理自然移动车辆前置摄像头的视频信号，并输出 “corner case” 分数。

自动驾驶角落案例检测

Towards Corner Case Detection for Autonomous Driving

Network embedding assigns nodes in a network to low-dimensional
representations and effectively preserves the network structure. Recently, a
significant amount of progresses have been made toward this emerging network
analysis paradigm. In this survey, we focus on categorizing and then reviewing
the current development on network embedding methods, and point out its future
research directions. We first summarize the motivation of network embedding. We
discuss the classical graph embedding algorithms and their relationship with
network embedding. Afterwards and primarily, we provide a comprehensive
overview of a large number of network embedding methods in a systematic manner,
covering the structure- and property-preserving network embedding methods, the
network embedding methods with side information and the advanced information
preserving network embedding methods. Moreover, several evaluation approaches
for network embedding and some useful online resources, including the network
data sets and softwares, are reviewed, too. Finally, we discuss the framework
of exploiting these network embedding methods to build an effective system and
point out some potential future directions.

本文介绍了网络嵌入方法的发展和分类，包括结构、属性、有监督、非监督等多种方法，并讨论了其评价指标和未来发展方向以及在构建有效系统中的应用。