  Practical learning-based autonomous driving models must be capable of
generalizing learned behaviors from simulated to real domains, and from
training data to unseen domains with unusual image properties. In this paper,
we investigate transfer learning methods that achieve robustness to domain
shifts by taking advantage of the invariance of spatio-temporal features across
domains. In this paper, we propose a transfer learning method to improve
generalization across domains via transfer of spatio-temporal features and
salient data augmentation. Our model uses a CNN-LSTM network with Inception
modules for image feature extraction. Our method runs in two phases: Phase 1
involves training on source domain data, while Phase 2 performs training on
target domain data that has been supplemented by feature maps generated using
the Phase 1 model. Our model significantly improves performance in unseen test
cases for both simulation-to-simulation transfer as well as simulation-to-real
transfer by up to +37.3\% in test accuracy and up to +40.8\% in steering angle
prediction, compared to other SOTA methods across multiple datasets.
