  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.
