Annotated driving scenario trajectories are crucial for verification and validation of autonomous vehicles. However, annotation of such trajectories based only on explicit rules (i.e. knowledge-based methods) may be prone to errors, such as false positive/negative classification of scenarios that lie on the border of two scenario classes, missing unknown scenario classes, or even failing to detect anomalies. On the other hand, verification of labels by annotators is not cost-efficient. For this purpose, active learning (AL) could potentially improve the annotation procedure by including an annotator/expert in an efficient way. In this study, we develop a generic active learning framework to annotate driving trajectory time series data. We first compute an embedding of the trajectories into a latent space in order to extract the temporal nature of the data. Given such an embedding, the framework becomes task agnostic since active learning can be performed using any classification method and any query strategy, regardless of the structure of the original time series data. Furthermore, we utilize our active learning framework to discover unknown driving scenario trajectories. This will ensure that previously unknown trajectory types can be effectively detected and included in the labeled dataset. We evaluate our proposed framework in different settings on novel real-world datasets consisting of driving trajectories collected by Volvo Cars Corporation. We observe that active learning constitutes an effective tool for labelling driving trajectories as well as for detecting unknown classes. Expectedly, the quality of the embedding plays an important role in the success of the proposed framework.
翻译:附加说明的驾驶场景轨迹对于自动车辆的核查和验证至关重要。 但是,仅依据明确规则(即基于知识的方法)的这种轨迹的注释可能容易出现错误,例如对位于两种情景类别边界的假设情景进行虚假的正/负分类,缺少未知情景类,甚至未能检测异常。另一方面,由说明者核查标记不是成本效率高的。为此,积极学习(AL)可能会通过有效的方式纳入一个说明/专家来改进批注程序。在本研究中,我们开发了一个通用的主动学习框架来说明驱动轨迹时间序列数据。我们首先将轨迹嵌入一个潜在空间,以便提取数据的时间性质。鉴于这种嵌入,该框架将变得任务不可知性,因为可以使用任何分类方法和任何查询策略来进行积极学习,而不论原始时间序列的结构如何。此外,我们可以利用我们的积极学习框架来发现未知的驱动场景/专家,在运行轨迹时间序列中,我们开发了一个通用的质量学习框架,从而有效地将未知的轨迹嵌嵌入了我们所收集的轨迹,在新数据库中,从而测量了我们所收集的轨迹。