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Environmental perception is an important aspect within the field ofautonomous vehicles that provides crucial information about the driving domain,including but not limited to identifying clear driving areas and surroundingobstacles. Semantic segmentation is a widely used perception method forself-driving cars that associates each pixel of an image with a predefinedclass. In this context, several segmentation models are evaluated regardingaccuracy and efficiency. Experimental results on the generated dataset confirmthat the segmentation model FasterSeg is fast enough to be used in realtime onlowpower computational (embedded) devices in self-driving cars. A simple methodis also introduced to generate synthetic training data for the model. Moreover,the accuracy of the first-person perspective and the bird's eye viewperspective are compared. For a $320 \times 256$ input in the first-personperspective, FasterSeg achieves $65.44\,\%$ mean Intersection over Union(mIoU), and for a $320 \times 256$ input from the bird's eye view perspective,FasterSeg achieves $64.08\,\%$ mIoU. Both perspectives achieve a frame rate of$247.11$ Frames per Second (FPS) on the NVIDIA Jetson AGX Xavier. Lastly, theframe rate and the accuracy with respect to the arithmetic 16-bit FloatingPoint (FP16) and 32-bit Floating Point (FP32) of both perspectives are measuredand compared on the target hardware.

Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability