Section 'Prototype trajectories' introduces prototype trajectories and how they can be used for trajectory prediction.
Motion patterns are represented using prototype trajectories which are learned from sample (previously observed) trajectories.
They are based either on prototype trajectories or on Dynamic Bayesian Networks.
Maneuver-based motion models are either based on prototype trajectories or based on maneuver intention estimation.
Motion patterns are represented using prototype trajectories which are learned from data during a training phase.
When motion patterns are represented by a finite set of prototype trajectories, the distance of a partial trajectory to a motion pattern is measured by its similarity with the prototype trajectories.
For a long time, the main limitation of prototype trajectories was their strictly deterministic representation of time.
Another difficulty when using prototype trajectories is their adaptation to different road layouts, in particular when applied to road intersections.
Starting from the partial trajectory executed by a vehicle so far, prediction can be performed by comparing it with the learned motion patterns, selecting the most likely one(s) and using the prototype trajectories as a model for future motion.
Subsequently prediction can be performed online given a partial trajectory by finding the most likely motion pattern(s) and using the prototype trajectories as a model for future motion.
