REIN

Abstract

In recent years, neural network has shown its great potential in autonomous driving systems. However, the theoretically well-train neural networks usually fail their performance when facing real-world examples with unexpected physical variations. As the current neural networks still suffer from limited generalization ability, those unexpected variations would cause considerable accuracy degradation and critical safety issues. Therefore, the generalization ability of neural networks becomes one of the most critical challenges for autonomous driving system design. In this work, we propose a robust training method to enhance neural network's generalization ability in various practical autonomous driving scenarios. Based on detailed practical variation modeling and neural network generation ability analysis, the proposed training method could consistently improve model classification accuracy by at most 25% in various scenarios (e.g. raining/fogy, dark lighting, and camera discrepancy). Even with adversarial corner cases, our model could still achieve at most 40% accuracy improvement over natural model.