Sensor Visibility Estimation: Metrics and Methods for Systematic Performance Evaluation and Improvement

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 metrics, we enhance the radar and camera visibility estimators by modeling the 3D elevation of sensor and objects. This refinement outperforms the conventional planar 2D approach in trustfulness and thus safety.