Similar to autonomous driving on the road, automated and autonomous train operation also offers many advantages. These include relieving the burden on train drivers, as well as a possible increase in line capacity or the redevelopment of previously unprofitable route sections. One of the most important tasks of an autonomous train control system is to monitor the surroundings and, above all, the route to be traveled. This must be continuously monitored for possible obstacles in the train’s path, just as a human train driver does. In order to perform this task, sensors are required that record data about the train’s surroundings. Such sensors in autonomous systems are usually cameras, radar or lidar sensors. To detect obstacles on the track, the critical zone must first be identified. For trains, this area is called the clearance gauge and describes the space that the train occupies when traveling on a track. In complex scenes with switches, the section of track that the train travels through – the ego track – must be determined depending on the status of the switches. This paper presents an image-based approach for embedded on-board ego track determination, combining track and switch information in order to achieve a more robust egotrack prediction.