Watch-and-Learn-Net: Self-supervised Online Learning for Probabilistic Vehicle Trajectory Prediction
Maximilian Geisslinger, Phillip Karle, Johannes Betz, Markus Lienkamp
Abstract
The prediction of other road users is an essential task in autonomous driving for preventing collisions and enabling dynamic trajectory planning. This task becomes even more complex because different road users have different driving behaviors. There are underlying intentions that cannot be predicted with certainty without direct communication. In the current state of the art, most promising pattern-based models are trained on a dataset and then applied in the real world. In this paper we present an algorithm for vehicle trajectory prediction that is using online learning. The algorithm uses observations during the inference to optimize the underlying neural network at runtime. We show that our model can adapt to an observed behavior and thus improve the predicted uncertainty of trajectory predictions. Furthermore, we emphasize that our online learning approach can be transferred to many problems in self-supervised learning. The code used in this research is available as open-source software: https://github.com/TUMFTM/Wale-Net