Safety is of utmost importance for perception in automated driving (AD). However, a prime safety concern in state-of-the art object detection is that standard evaluation schemes utilize safety-agnostic metrics to argue sufficient detection performance. Hence, it is imperative to leverage supplementary domain knowledge to accentuate safety-critical misdetections during evaluation tasks. To tackle the underspecification, this paper introduces a novel credibility metric, called c-flow, for pedestrian bounding boxes. To this end, c-flow relies on a complementary optical flow signal from image sequences and enhances the analyses of safety-critical misdetections without requiring additional labels. We implement and evaluate c-flow with a state-of-the-art pedestrian detector on a large AD dataset. Our analysis demonstrates that c-flow allows developers to identify safety-critical misdetections.
@article{arxiv.2402.07642,
title = {A Flow-based Credibility Metric for Safety-critical Pedestrian Detection},
author = {Maria Lyssenko and Christoph Gladisch and Christian Heinzemann and Matthias Woehrle and Rudolph Triebel},
journal= {arXiv preprint arXiv:2402.07642},
year = {2024}
}