  Considering the functionality of situational awareness in safety-critical
automation systems, the perception of risk in driving scenes and its
explainability is of particular importance for autonomous and cooperative
driving. Toward this goal, this paper proposes a new research direction of
joint risk localization in driving scenes and its risk explanation as a natural
language description. Due to the lack of standard benchmarks, we collected a
large-scale dataset, DRAMA (Driving Risk Assessment Mechanism with A captioning
module), which consists of 17,785 interactive driving scenarios collected in
Tokyo, Japan. Our DRAMA dataset accommodates video- and object-level questions
on driving risks with associated important objects to achieve the goal of
visual captioning as a free-form language description utilizing closed and
open-ended responses for multi-level questions, which can be used to evaluate a
range of visual captioning capabilities in driving scenarios. We make this data
available to the community for further research. Using DRAMA, we explore
multiple facets of joint risk localization and captioning in interactive
driving scenarios. In particular, we benchmark various multi-task prediction
architectures and provide a detailed analysis of joint risk localization and
risk captioning. The data set is available at https://usa.honda-ri.com/drama
