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Even though end-to-end supervised learning has shown promising results forsensorimotor control of self-driving cars, its performance is greatly affectedby the weather conditions under which it was trained, showing poorgeneralization to unseen conditions. In this paper, we show how knowledge canbe transferred using semantic maps to new weather conditions without the needto obtain new ground truth data. To this end, we propose to divide the task ofvehicle control into two independent modules: a control module which is onlytrained on one weather condition for which labeled steering data is available,and a perception module which is used as an interface between new weatherconditions and the fixed control module. To generate the semantic data neededto train the perception module, we propose to use a generative adversarialnetwork (GAN)-based model to retrieve the semantic information for the newconditions in an unsupervised manner. We introduce a master-servantarchitecture, where the master model (semantic labels available) trains theservant model (semantic labels not available). We show that our proposed methodtrained with ground truth data for a single weather condition is capable ofachieving similar results on the task of steering angle prediction as anend-to-end model trained with ground truth data of 15 different weatherconditions.

Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs