论文标题
传感器可见性估计：系统性能评估和改进的指标和方法
Sensor Visibility Estimation: Metrics and Methods for Systematic Performance Evaluation and Improvement
论文作者
论文摘要
传感器的可见性对于汽车，机器人技术，智能基础设施和其他方面的安全至关重要的应用至关重要：除了对象检测和占用映射外，可见性还描述了传感器可以在何处进行测量或盲目的镜头。这些知识可以增强功能安全性和感知算法或优化传感器拓扑。 尽管它具有重要意义，但据我们所知，尚不存在对可见性和性能指标的共同定义。我们缩小了这一差距，并提供了可见性的定义，该定义是从用例审查中得出的。我们介绍指标和一个框架来评估可见性估计器的性能。 我们的指标通过标记的现实世界和基础设施雷达和相机的仿真数据进行了验证：该框架很容易识别出误解或虚假的隐形估计，这些估计是安全至关重要的。 应用我们的指标，我们通过对传感器和物体的3D高程进行建模来增强雷达和摄像机的可见性估计器。这种改进优于传统的平面2D方法，因此可以安全。
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.
