Prior parameter distributions provide an elegant way to represent prior expert 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 forward 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.