  Although deep neural networks enable impressive visual perception performance
for autonomous driving, their robustness to varying weather conditions still
requires attention. When adapting these models for changed environments, such
as different weather conditions, they are prone to forgetting previously
learned information. This catastrophic forgetting is typically addressed via
incremental learning approaches which usually re-train the model by either
keeping a memory bank of training samples or keeping a copy of the entire model
or model parameters for each scenario. While these approaches show impressive
results, they can be prone to scalability issues and their applicability for
autonomous driving in all weather conditions has not been shown. In this paper
we propose DISC -- Domain Incremental through Statistical Correction -- a
simple online zero-forgetting approach which can incrementally learn new tasks
(i.e weather conditions) without requiring re-training or expensive memory
banks. The only information we store for each task are the statistical
parameters as we categorize each domain by the change in first and second order
statistics. Thus, as each task arrives, we simply 'plug and play' the
statistical vectors for the corresponding task into the model and it
immediately starts to perform well on that task. We show the efficacy of our
approach by testing it for object detection in a challenging domain-incremental
autonomous driving scenario where we encounter different adverse weather
conditions, such as heavy rain, fog, and snow.
