Centralized priority-based coordination of connected vehicles reduces waiting time and increases throughput in mixed urban traffic with as few as 40% cooperative vehicles.
openDD: A Large-Scale Roundabout Drone Dataset
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abstract
Analyzing and predicting the traffic scene around the ego vehicle has been one of the key challenges in autonomous driving. Datasets including the trajectories of all road users present in a scene, as well as the underlying road topology are invaluable to analyze the behavior of the different traffic participants. The interaction between the various traffic participants is especially high in intersection types that are not regulated by traffic lights, the most common one being the roundabout. We introduce the openDD dataset, including 84,774 accurately tracked trajectories and HD map data of seven different roundabouts. The openDD dataset is annotated using images taken by a drone in 501 separate flights, totalling in over 62 hours of trajectory data. As of today, openDD is by far the largest publicly available trajectory dataset recorded from a drone perspective, while comparable datasets span 17 hours at most. The data is available, for both commercial and noncommercial use, at: http://www.l3pilot.eu/openDD.
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Generalized Coordination of Partially Cooperative Urban Traffic
Centralized priority-based coordination of connected vehicles reduces waiting time and increases throughput in mixed urban traffic with as few as 40% cooperative vehicles.