Cooperative Probabilistic Trajectory Forecasting under Occlusion
Anshul Nayak Azim Eskandarian
Perception and planning under occlusion is essential for safety-critical
tasks. Occlusion-aware planning often requires communicating the information of
the occluded object to the ego agent for safe navigation. However,
communicating rich sensor information under adverse conditions during
communication loss and limited bandwidth may not be always feasible. Further,
in GPS denied environments and indoor navigation, localizing and sharing of
occluded objects can be challenging. To overcome this, relative pose estimation
between connected agents sharing a common field of view can be a
computationally effective way of communicating information about surrounding
objects. In this paper, we design an end-to-end network that cooperatively
estimates the current states of occluded pedestrian in the reference frame of
ego agent and then predicts the trajectory with safety guarantees.
Experimentally, we show that the uncertainty-aware trajectory prediction of
occluded pedestrian by the ego agent is almost similar to the ground truth
trajectory assuming no occlusion. The current research holds promise for
uncertainty-aware navigation among multiple connected agents under occlusion.