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

Source: [Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability](<https://www.sota2.com/research/paper/68fa6d1f1e209131cea37e93>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:26:36.450Z

Authors: Senay Cakir, Marcel Gau{\\ss}, Kai H\\"appeler, Yassine Ounajjar, Fabian Heinle, Reiner Marchthaler

Published: 2022-07-26T14:45:44.000Z

## Abstract

Environmental perception is an important aspect within the field of autonomous vehicles that provides crucial information about the driving domain, including but not limited to identifying clear driving areas and surrounding obstacles. Semantic segmentation is a widely used perception method for self-driving cars that associates each pixel of an image with a predefined class. In this context, several segmentation models are evaluated regarding accuracy and efficiency. Experimental results on the generated dataset confirm that the segmentation model FasterSeg is fast enough to be used in realtime on lowpower computational (embedded) devices in self-driving cars. A simple method is also introduced to generate synthetic training data for the model. Moreover, the accuracy of the first-person perspective and the bird's eye view perspective are compared. For a $320 \\times 256$ input in the first-person perspective, 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, the frame rate and the accuracy with respect to the arithmetic 16-bit Floating Point (FP16) and 32-bit Floating Point (FP32) of both perspectives are measured and compared on the target hardware.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2207.12939>)

[PDF](<https://arxiv.org/pdf/2207.12939.pdf>)