# Self-Supervised Domain Mismatch Estimation for Autonomous Perception Source: [Self-Supervised Domain Mismatch Estimation for Autonomous Perception]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:20:05.971Z Authors: Jonas L\\"ohdefink, Justin Fehrling, Marvin Klingner, Fabian H\\"uger, Peter Schlicht, Nico M. Schmidt, Tim Fingscheidt Published: 2020-06-15T09:22:03.000Z ## Abstract Autonomous driving requires self awareness of its perception functions. Technically spoken, this can be realized by observers, which monitor the performance indicators of various perception modules. In this work we choose, exemplarily, a semantic segmentation to be monitored, and propose an autoencoder, trained in a self-supervised fashion on the very same training data as the semantic segmentation to be monitored. While the autoencoder's image reconstruction performance (PSNR) during online inference shows already a good predictive power w.r.t. semantic segmentation performance, we propose a novel domain mismatch metric DM as the earth mover's distance between a pre-stored PSNR distribution on training (source) data, and an online-acquired PSNR distribution on any inference (target) data. We are able to show by experiments that the DM metric has a strong rank order correlation with the semantic segmentation within its functional scope. We also propose a training domain-dependent threshold for the DM metric to define this functional scope. ## 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]() [PDF]()