Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving
This addresses the critical issue of domain adaptation for autonomous driving systems, enabling more robust transfer from simulated to real environments, though it is incremental as it builds on existing transfer learning and feature extraction techniques.
The paper tackles the problem of improving generalization for autonomous driving models across domain shifts, such as from simulation to real-world, by leveraging spatio-temporal features and data augmentation, resulting in up to +37.3% test accuracy and +40.8% steering angle prediction improvements over state-of-the-art methods.
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.