Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns. Based on these predictions - and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users - the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.
翻译:高度自动化的车辆(HAV)必须能够可靠和安全地完成分配给它的任务,例如从A点到B点。理想的情况是,HAV安全地穿过其环境,正如我们所期望的那样。但是,如果出现异常的轨迹,所谓的轨迹角案例,人类驾驶员通常能够应付好,但HAV可能很快陷入麻烦。在轨迹角案例的定义中,我们将在这项工作中提供这种案例,我们将考虑异常的轨迹与手头任务的相关性。基于这一点,我们将提出不同轨迹角案例的分类。将用实例和根据我们的原因和需要的数据源来显示对角案例的分类。为了说明目前机器学习链式(ML)模型和转角案例之间的复杂程度,我们将用实例和根据我们的原因和需要的数据源来显示对转角案例的分类。