Sensor Visibility Estimation: Metrics and Methods for Systematic Performance Evaluation and Improvement
Joachim B\"orger, Marc Patrick Zapf, Marat Kopytjuk, Xinrun Li 2, and, Claudius Gl\"aser

TL;DR
This paper defines sensor visibility, introduces metrics and a framework for evaluation, and demonstrates how modeling 3D elevation improves visibility estimation safety and accuracy in automotive and infrastructure sensors.
Contribution
It provides the first formal definition of sensor visibility, along with metrics and a framework for systematic performance assessment and enhancement.
Findings
Metrics effectively identify false visibility estimations.
3D elevation modeling improves estimator trustworthiness.
Framework applies to real-world and simulation data.
Abstract
Sensor visibility is crucial for safety-critical applications in automotive, robotics, smart infrastructure and others: In addition to object detection and occupancy mapping, visibility describes where a sensor can potentially measure or is blind. This knowledge can enhance functional safety and perception algorithms or optimize sensor topologies. Despite its significance, to the best of our knowledge, neither a common definition of visibility nor performance metrics exist yet. We close this gap and provide a definition of visibility, derived from a use case review. We introduce metrics and a framework to assess the performance of visibility estimators. Our metrics are verified with labeled real-world and simulation data from infrastructure radars and cameras: The framework easily identifies false visible or false invisible estimations which are safety-critical. Applying our…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
