Evaluating the performance of a motion prediction algorithm
requires choosing appropriate testing scenarios and
accuracy metrics, as well as studying the method’s robustness
against various variables, such as the number of
interacting agents or amount of maneuvering in the data.
Depending on the application area, the testing scenario
maybe an intersection, a highway, a pedestrian crossing,
shared urban street with heterogeneous agents, a home environment,
or a crowded public space. Existing datasets, summarized
in this comparison
ORKG Comparisons have changed. We have added new features and improved the user interface. Comparisons might look slightly different, but the comparison data itself remains unchanged.
Rudenko, Andrey, Luigi Palmieri, Michael Herman, Kris M. Kitani, Dariu M. Gavrila, and Kai O. Arras. "Human motion trajectory prediction: A survey." The International Journal of Robotics Research 39, no. 8 (2020): 895-935.