# SUNSET - A Sensor-fUsioN based semantic SegmEnTation exemplar for ROS-based self-adaptation Source: [SUNSET - A Sensor-fUsioN based semantic SegmEnTation exemplar for ROS-based self-adaptation]() Collection index: [llms.txt]() Data updated: 2026-08-01T05:00:13.650Z Authors: Andreas Wiedholz, Rafael Paintner, Alwin Hoffmann, Carlos Hernandez, Tobias Huber Published: 2026-01-20T08:40:57.000Z ## Abstract The fact that robots are getting deployed more often in dynamic environments, together with the increasing complexity of their software systems, raises the need for self-adaptive approaches. In these environments robotic software systems increasingly encounter (1) failures whose symptoms are easy to observe but root causes might be ambiguous or (2) multiple failures appearing concurrently. We present SUNSET, a ROS2-based exemplar that enables rigorous, repeatable evaluation of architecture-based self-adaptation in such conditions. It implements a sensor fusion semantic-segmentation pipeline driven by a trained Machine Learning (ML) model whose input preprocessing can be perturbed to induce realistic performance degradations. The exemplar exposes five observable failures, each of which can be caused by different faults and supports concurrent failures spanning self-healing and self-optimisation. SUNSET includes the segmentation pipeline, a trained ML model, fault-injection scripts, a baseline controller for further comparisons, and step-by-step integration and evaluation documentation to facilitate reproducible studies. The code is available at https://github.com/XITASO/sunset. ## 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]()