Humans approach driving in a holistic fashion which entails, in particular, understanding road events and their evolution. Injecting these capabilities in an autonomous vehicle has thus the potential to take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by a moving agent, the action(s) it performs and the corresponding scene locations. ROAD comprises 22 videos, originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event. We also provide as baseline a new incremental algorithm for online road event awareness, based on inflating RetinaNet along time, which achieves a mean average precision of 16.8% and 6.1% for frame-level and video-level event detection, respectively, at 50% overlap. Though promising, these figures highlight the challenges faced by situation awareness in autonomous driving. Finally, ROAD allows scholars to investigate exciting tasks such as complex (road) activity detection, future road event anticipation and the modelling of sentient road agents in terms of mental states. Dataset can be obtained from https://github.com/gurkirt/road-dataset and baseline code from https://github.com/gurkirt/3D-RetinaNet.
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