  Prior parameter distributions provide an elegant way to represent prior
expert and world knowledge for informed learning. Previous work has shown that
using such informative priors to regularize probabilistic deep learning (DL)
models increases their performance and data-efficiency. However, commonly used
sampling-based approximations for probabilistic DL models can be
computationally expensive, requiring multiple inference passes and longer
training times. Promising alternatives are compute-efficient last layer kernel
approximations like spectral normalized Gaussian processes (SNGPs). We propose
a novel regularization-based continual learning method for SNGPs, which enables
the use of informative priors that represent prior knowledge learned from
previous tasks. Our proposal builds upon well-established methods and requires
no rehearsal memory or parameter expansion. We apply our informed SNGP model to
the trajectory prediction problem in autonomous driving by integrating prior
drivability knowledge. On two public datasets, we investigate its performance
under diminishing training data and across locations, and thereby demonstrate
an increase in data-efficiency and robustness to location-transfers over
non-informed and informed baselines.
