Domain Adaptation for Semantic Segmentation of Autonomous Driving with Contrastive Learning

Semantic segmentation is a critical component of autonomous driving perception systems and has gained increasing attention in recent advancements. Autonomous vehicles frequently encounter diverse environmental conditions, highlighting the significance of research into domain adaptation for semantic segmentation. We established a domain adversarial framework for enhancing the cross-domain perception of autonomous vehicles. However, previous works have shown that the general adversarial training-based methods can lead to indistinguishable features, resulting in a decline in the robustness of perception. In this regard, we adopted contrastive learning to guarantee the proximity of similar samples, and the principle of different classes of samples to a certain extent. This ensures the closeness of similar samples and upholds the feature distinction between different classes of samples to a significant degree. Thus, we proposed a novel domain adversarial training framework incorporating the contrastive learning method to enhance cross-domain feature recognition for autonomous driving systems. We empirically evaluate the proposed method against several recent baselines showing improved benchmark performances, confirming the effectiveness of the proposed method.