Applying this procedure to state-of-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions.
Predicting the states of dynamic traffic actors into the future is important for autonomous systems to operate safelyand efficiently.
In this paper, we investigate the problem of anticipating future dynamics, particularly the future location of other vehicles and pedestrians, in the view of a moving vehicle.
Future prediction is a fundamental principle of intelligence that helps plan actions and avoid possible dangers.
The latter can be used as proxy-ground-truth to train a network on real-world data and to adapt it to specific domains of interest.
Optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative methodology.
We analyze the usage of optical flow for video super-resolution and find that common off-the-shelf image warping does not allow video super-resolution to benefit much from optical flow.