# Safety Metrics for Semantic Segmentation in Autonomous Driving

Source: [Safety Metrics for Semantic Segmentation in Autonomous Driving](<https://www.sota2.com/research/paper/68fa6ade1e209131cea2aecc>)

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

Data updated: 2025-10-25T23:35:16.571Z

Authors: Chih-Hong Cheng, Alois Knoll, Hsuan-Cheng Liao

Published: 2021-05-21T05:59:49.000Z

## Abstract

Within the context of autonomous driving, safety-related metrics for deep neural networks have been widely studied for image classification and object detection. In this paper, we further consider safety-aware correctness and robustness metrics specialized for semantic segmentation. The novelty of our proposal is to move beyond pixel-level metrics: Given two images with each having N pixels being class-flipped, the designed metrics should, depending on the clustering of pixels being class-flipped or the location of occurrence, reflect a different level of safety criticality. The result evaluated on an autonomous driving dataset demonstrates the validity and practicality of our proposed methodology.

## 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/2105.10142>)

[PDF](<https://arxiv.org/pdf/2105.10142.pdf>)