The accurate detection and anticipation of actions performed by multiple road agents (pedestrians, vehicles, cyclists and so on) is a crucial task to address for enabling autonomous vehicles to make autonomous decisions in a safe, reliable way. While the task of teaching an autonomous vehicle how to drive can be tackled in a brute-force fashion through direct reinforcement learning, a sensible and attractive alternative is to first provide the vehicle with situation awareness capabilities, to then feed the resulting semantically meaningful representations of road scenarios (in terms of agents, events and scene configuration) to a suitable decision-making strategy. In perspective, this has also the advantage of allowing the modelling of the reasoning process of road agents in a theory-of-mind approach, inspired by the behaviour of the human mind in similar contexts.
Accordingly, the goal of this Challenge is to put to the forefront of the research in autonomous driving the topic of situation awareness, intended as the ability to create semantically useful representations of dynamic road scenes, in terms of the notion of a road event.
*Best teams in the challenge are introduced!To allow the research community to thoroughly investigate situation awareness for autonomous driving, this workshop introduces ROAD, the first ROad event Awareness in Autonomous Driving Dataset, built upon (a fraction of) the Oxford RobotCar Dataset (https://robotcar-dataset.robots.ox.ac.uk/).
ROAD is the result of annotating 22 carefully selected, relatively long-duration (ca 8 minutes each) videos from the RobotCar dataset in terms of what we call road events (REs), as seen from the point of view of the autonomous vehicle capturing the video. REs are defined as triplets E = (Ag;Ac; Loc) composed by a moving agent Ag, the action Ac it performs, and the location Loc in which this takes place. Agent, action and location are all classes in a finite list compiled by surveying the content of the 22 videos.
More information about the dataset can be found the the dataset tab or directly from here (https://github.com/gurkirt/road-dataset)
Waabi
Toyota Research Institute (TRI)
MIT
Argo AI- CMU
ETH
Oxford Brookes University
University of Naples Federico II
University of Science and Technology of Mazandaran
Swiss Federal Institute of Technology in Zurich
Oxford Brookes University
University of Naples Federico II
University of Oxford
Oxford Brookes University