Currently, big players in the automotive sector are racing each other for developing the first full autonomous vehicle. The most well-known among them are Tesla Inc., VW AG and even non-automotive companies, such as Alphabet Inc. and Apple Inc. are taking part. However, there are still many pitfalls and major challenges they are faced with. Specifically unexpected situations in everyday road traffic have to be considered. Nissan recently even plans to install a remote call center for providing human assistance for dealing with those siutations and even has lost the hope of eventually developing a full autonomous vehicle one day. One type of these critical siutations is given by road construction sites. A road construction site is characterized by a broad range of unforeseeable temporal changes to the infrastructure that can invalidate any preexisting map information for navigation purposes. Furthermore, an undefined behavior or slow reactions on infrastructure changes can directly affect the road safety or obstruct the traffic-flow. The present work specifically focuses on this problem domain, and examines how deep learning can be a part of a solution towards a fully autonomous handling of those situations. For this purpose, a dataset of hundreds of road construction site images has been created. Only images of construction sites that comply with german rules and regulations and images taken at daytime are considered. Further, a deep convolutional neural network (CNN) has been designed and trained to detect road construction sites as well as the corresponding changes to the actual driving lanes on a pixel level. The final outcome of this work basically demonstrates the powerfulness of deep learning with respect to the domain of construction sites and thus provides a proof of concept for future developments in autonomous driving.