Evaluating Perception Systems: A Guide to Precision, Recall and Specificity

This White Paper discusses the performance evaluation of machine-learning systems, particularly in the context of advanced driver assistance systems (ADAS). It outlines key performance indicators (KPIs) such as precision, recall, and specificity, which are essential for assessing the effectiveness of machine-learning models. The document explains the concepts of true positives, true negatives, false positives, and false negatives, and introduces the confusion matrix as a tool for visualizing these outcomes. The White Paper also details the importance of minimizing false positives and negatives in perception systems to enhance safety and reliability in real-world driving scenarios. Additionally, it highlights the role of sensor fusion in improving object detection and classification by combining data from multiple sensors. The document aims to provide a comprehensive understanding of how these metrics can influence the performance of perception systems and the implications for ADAS technology.