Designing or learning an autonomous driving policy is undoubtedly a challenging task as the policy has to maintain its safety in all corner cases. In order to secure safety in autonomous driving, the ability to detect hazardous situations, which can be seen as an out-of-distribution (OOD) detection problem, becomes crucial. However, most conventional datasets only provide expert driving demonstrations, although some non-expert or uncommon driving behavior data are needed to implement a safety guaranteed autonomous driving platform. To this end, we present a novel dataset called the R3 Driving Dataset, composed of driving data with different qualities. The dataset categorizes abnormal driving behaviors into eight categories and 369 different detailed situations. The situations include dangerous lane changes and near-collision situations. To further enlighten how these abnormal driving behaviors can be detected, we utilize different uncertainty estimation and anomaly detection methods to the proposed dataset. From the results of the proposed experiment, it can be inferred that by using both uncertainty estimation and anomaly detection, most of the abnormal cases in the proposed dataset can be discriminated. The dataset of this paper can be downloaded from https://rllab-snu.github.io/projects/R3-Driving-Dataset/doc.html.
翻译:设计或学习自主驾驶政策无疑是一项具有挑战性的任务,因为该政策必须在所有转角情况中保持其安全性。为了确保自主驾驶的安全,发现危险情况的能力变得至关重要,因为危险情况可被视为分配外(OOOD)检测问题。然而,大多数常规数据集仅提供专家驾驶演示,尽管需要一些非专家或异常的驾驶行为数据来执行安全保障自主驾驶平台。为此,我们提出了一个新型数据集,称为R3驾驶数据集,由具有不同品质的驾驶数据组成。数据集将不正常驾驶行为分为8类和369种不同的详细情况。这些情形包括危险航道变化和接近collisiion的情况。为了进一步说明如何发现这些不正常驾驶行为,我们使用不同的不确定性估计和异常检测方法来向拟议的数据集提供。根据拟议的试验结果,可以推断,通过使用不确定性估计和异常检测,可以对拟议数据集中的大多数异常情况加以区分。本文的数据集可以从 https://rllab-snu.givat-Drgius3.io/proismas下载。