  Humans drive in a holistic fashion which entails, in particular,
understanding dynamic road events and their evolution. Injecting these
capabilities in autonomous vehicles can thus 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 an
active agent, the action(s) it performs and the corresponding scene locations.
ROAD comprises videos originally from the Oxford RobotCar Dataset annotated
with bounding boxes showing the location in the image plane of each road event.
We benchmark various detection tasks, proposing as a baseline a new incremental
algorithm for online road event awareness termed 3D-RetinaNet. We also report
the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as
that of the winners of the ICCV2021 ROAD challenge, which highlight the
challenges faced by situation awareness in autonomous driving. ROAD is designed
to allow scholars to investigate exciting tasks such as complex (road) activity
detection, future event anticipation and continual learning. The dataset is
available at https://github.com/gurkirt/road-dataset; the baseline can be found
at https://github.com/gurkirt/3D-RetinaNet.
