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Self-driving vehicles are the future of transportation. With currentadvancements in this field, the world is getting closer to safe roads withalmost zero probability of having accidents and eliminating human errors.However, there is still plenty of research and development necessary to reach alevel of robustness. One important aspect is to understand a scene fullyincluding all details. As some characteristics (attributes) of objects in ascene (drivers' behavior for instance) could be imperative for correct decisionmaking. However, current algorithms suffer from low-quality datasets with suchrich attributes. Therefore, in this paper, we present a new dataset forattributes recognition -- Cityscapes Attributes Recognition (CAR). The newdataset extends the well-known dataset Cityscapes by adding an additional yetimportant annotation layer of attributes of objects in each image. Currently,we have annotated more than 32k instances of various categories (Vehicles,Pedestrians, etc.). The dataset has a structured and tailored taxonomy whereeach category has its own set of possible attributes. The tailored taxonomyfocuses on attributes that is of most beneficent for developing betterself-driving algorithms that depend on accurate computer vision and scenecomprehension. We have also created an API for the dataset to ease the usage ofCAR. The API can be accessed through https://github.com/kareem-metwaly/CAR-API.

CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles