Automated driving systems(ADS) are major trend and the safety of such critical system has become one of the most important research topics. We usually use scenarios in order to define the specifications of ADS. In these scenarios, graphical diagrams are often used to represent abstractly the positioning and behavior of vehicles. However, such diagrams are not suitable for the development of high-reliability systems, because they are informal and may cause discrepancies among different engineers. In this paper, we propose a formal speci-fication language called Bounding Box Specification Language (BBSL) which allows us to write rigorous specifications of ADS. BBSL describe multiple types of objects in a driving environment, such as vehicles and pedestrians, as bounding boxes defined as two-dimensional interval, and describe positional relationships between them in mathematical notation. It is capable of strictly delineating many positional relationships while being also capable of expressing specifications that are concise enough to be read and written manually. Therefore, BBSL is suitable for describing the specification of Object and Event Detection and Response (OEDR) among the tasks of ADS. In this paper, we describe what kind of description BBSL enables, and describe its operations. Then, we show examples of specifications of ADS written in BBSL and discuss the advantages of specifications written in BBSL.

We focus on image description and a corresponding assessment system for language learners. To achieve automatic assessment of image description, we construct a novel dataset, the Language Learner Image Description (LLID) dataset, which consists of images, their descriptions, and assessment annotations. Then, we propose a novel task of automatic error correction for image description, and we develop a baseline model that encodes multimodal information from a learner sentence with an image and accurately decodes a corrected sentence. Our experimental results show that the developed model can revise errors that cannot be revised without an image.