Graph-based Trajectory Prediction with Cooperative Information
Jan Strohbeck Sebastian Maschke Max Mertens Michael Buchholz
For automated driving, predicting the future trajectories of other road users
in complex traffic situations is a hard problem. Modern neural networks use the
past trajectories of traffic participants as well as map data to gather hints
about the possible driver intention and likely maneuvers. With increasing
connectivity between cars and other traffic actors, cooperative information is
another source of data that can be used as inputs for trajectory prediction
algorithms. Connected actors might transmit their intended path or even
complete planned trajectories to other actors, which simplifies the prediction
problem due to the imposed constraints. In this work, we outline the benefits
of using this source of data for trajectory prediction and propose a
graph-based neural network architecture that can leverage this additional data.
We show that the network performance increases substantially if cooperative
data is present. Also, our proposed training scheme improves the network's
performance even for cases where no cooperative information is available. We
also show that the network can deal with inaccurate cooperative data, which
allows it to be used in real automated driving environments.