Semantic segmentation for autonomous driving is an even more challenging task when faced with adverse driving conditions. Standard models trained on data recorded under ideal conditions show a deteriorated performance in unfavorable weather or illumination conditions. Fine-tuning on the new task or condition would lead to overwriting the previously learned information resulting in catastrophic forgetting. Adapting to the new conditions through traditional domain adaption methods improves the performance on the target domain at the expense of the source domain. Addressing these issues, we propose an architecture-based domain-incremental learning approach called Progressive Semantic Segmentation (PSS). PSS is a task-agnostic, dynamically growing collection of domain-specific segmentation models. The task of inferring the domain and subsequently selecting the appropriate module for segmentation is carried out using a collection of convolutional autoencoders. We extensively evaluate our proposed approach using several datasets at varying levels of granularity in the categorization of adverse driving conditions. Furthermore, we demonstrate the generalization of the proposed approach to similar and unseen domains.

本研究解决了在不利驾驶条件下自主驾驶的语义分割模型性能下降的问题。我们提出了逐步语义分割（PSS）方法，通过动态增长的领域特定分割模型集合，避免了传统方法中的灾难性遗忘和领域适应的缺陷。实验结果表明，该方法在不同数据集上表现出优越的泛化能力，提升了在恶劣天气和光照条件下的分割效果。

在不利驾驶条件下自主驾驶的领域增量语义分割