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Today, cameras mounted in vehicles are used to observe the driver as well as the objects around a vehicle. In this article, an outlineof a concept for image based recognition of dynamic traffic situations is shown. A dynamic traffic situation will be described by roadusers and their intentions. Images will be taken by a vehicle fleet and aggregated on a server. On these images, new strategies formachine learning will be applied iteratively when new data has arrived on the server. The results of the learning process will be modelsdescribing the traffic situation and will be transmitted back to the recording vehicles. The recognition will be performed as a standalonefunction in the vehicles and will use the received models. It can be expected, that this method can make the detection and classificationof objects around the vehicles more reliable. In addition, the prediction of their actions for the next seconds should be possible. Asone example how this concept is used, a method to recognize the illumination situation of a traffic scene is described. This allows tohandle different appearances of objects depending on the illumination of the scene. Different illumination classes will be defined todistinguish different illumination situations. Intensity based features are extracted from the images and used by a classifier to assign animage to an illumination class. This method is being tested for a real data set of daytime and nighttime images. It can be shown, thatthe illumination class can be classified correctly for more than 80% of the images

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

IMAGE BASED RECOGNITION OF DYNAMIC TRAFFIC SITUATIONS BY EVALUATING THE EXTERIOR SURROUNDING AND INTERIOR SPACE OF VEHICLES