Abstract: A system for trajectory prediction using a predicted endpoint conditioned network includes one or more processors and a memory that includes a sensor input module, an endpoint distribution module, and a future trajectory module. The modules cause the one or more processors to the one or more processors to obtain sensor data of a scene having a plurality of pedestrians, determine endpoint distributions of the plurality of pedestrians within the scene, the endpoint distributions representing desired end destinations of the plurality of pedestrians from the scene, and determine future trajectory points for at least one of the plurality of pedestrians based on prior trajectory points of the plurality of pedestrians and the endpoint distributions of the plurality of pedestrians. The future trajectory points may be conditioned not only on the pedestrian and their immediate neighbors' histories (observed trajectories) but also on all the other pedestrian's estimated endpoints.

Abstract: A system and related method for predicting movement of a plurality of pedestrians may include one or more processors and a memory. The memory includes an initial trajectory module, an exit point prediction module, a path planning module, and an adjustment module. The modules include instructions that when executed by the one or more processors cause the one or more processors to obtain trajectories of the plurality of pedestrians, predict future exit points for the plurality of pedestrians from a scene based on the trajectories of the plurality of pedestrians, determine trajectory paths of the plurality of pedestrians based on the future exit points and at least one scene element of a map, and adjust the trajectory paths based on at least one predicted interaction between at least two of the plurality of pedestrians.

Abstract: A system for trajectory prediction using a predicted endpoint conditioned network includes one or more processors and a memory that includes a sensor input module, an endpoint distribution module, and a future trajectory module. The modules cause the one or more processors to the one or more processors to obtain sensor data of a scene having a plurality of pedestrians, determine endpoint distributions of the plurality of pedestrians within the scene, the endpoint distributions representing desired end destinations of the plurality of pedestrians from the scene, and determine future trajectory points for at least one of the plurality of pedestrians based on prior trajectory points of the plurality of pedestrians and the endpoint distributions of the plurality of pedestrians. The future trajectory points may be conditioned not only on the pedestrian and their immediate neighbors' histories (observed trajectories) but also on all the other pedestrian's estimated endpoints.

Abstract: A system and related method for predicting movement of a plurality of pedestrians may include one or more processors and a memory. The memory includes an initial trajectory module, an exit point prediction module, a path planning module, and an adjustment module. The modules include instructions that when executed by the one or more processors cause the one or more processors to obtain trajectories of the plurality of pedestrians, predict future exit points for the plurality of pedestrians from a scene based on the trajectories of the plurality of pedestrians, determine trajectory paths of the plurality of pedestrians based on the future exit points and at least one scene element of a map, and adjust the trajectory paths based on at least one predicted interaction between at least two of the plurality of pedestrians.

Abstract: A method for predicting spatial positions of several key points on a human body in the near future in an egocentric setting is described. The method includes generating a frame-level supervision for human poses. The method also includes suppressing noise and filling missing joints of the human body using a pose completion module. The method further includes splitting the poses into a global stream and a local stream. Furthermore, the method includes combining the global stream and the local stream to forecast future human locomotion.

Abstract: A method for predicting spatial positions of several key points on a human body in the near future in an egocentric setting is described. The method includes generating a frame-level supervision for human poses. The method also includes suppressing noise and filling missing joints of the human body using a pose completion module. The method further includes splitting the poses into a global stream and a local stream. Furthermore, the method includes combining the global stream and the local stream to forecast future human locomotion.