Trajectory Prediction for ADAS

A novel pipeline is presented for unsupervised trajectory prediction. As part of this research, numerous techniques are investigated for trajectory prediction of dynamic obstacles from an egocentric perspective (driver’s per- spective). The algorithm takes images from a calibrated stereo camera as input or data from a laser scanner and outputs a heat map that describes all possible future locations of that specific 3D object for the next few frames. This research has many applications, most notably for autonomous cars as it allows them to make better driving decisions if they are able to anticipate where another moving object is going to be in the future.