# Semantic Label Reduction Techniques for Autonomous Driving

Source: [Semantic Label Reduction Techniques for Autonomous Driving](<https://www.sota2.com/research/paper/68fa65531e209131cea09af6>)

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

Data updated: 2025-10-25T21:24:52.728Z

Authors: Qadeer Khan, Torsten Sch\\"on, Patrick Wenzel

Published: 2019-02-11T08:38:26.000Z

## Abstract

Semantic segmentation maps can be used as input to models for maneuvering the controls of a car. However, not all labels may be necessary for making the control decision. One would expect that certain labels such as road lanes or sidewalks would be more critical in comparison with labels for vegetation or buildings which may not have a direct influence on the car's driving decision. In this appendix, we evaluate and quantify how sensitive and important the different semantic labels are for controlling the car. Labels that do not influence the driving decision are remapped to other classes, thereby simplifying the task by reducing to only labels critical for driving of the vehicle.

## 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/1902.03777>)

[PDF](<https://arxiv.org/pdf/1902.03777.pdf>)