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

This White Paper explains the concept of performance in machine-learning systems, particularly focusing on key performance indicators (KPIs) used to assess the effectiveness of machine-learning models. It analyzes various KPIs, such as recall, precision, and specificity, within the context of perception systems for advanced driver assistance systems (ADAS). The document details essential concepts, including true positives, true negatives, false positives, false negatives, and confusion matrices. The White Paper emphasizes the importance of precision and recall as critical measures for evaluating classification models, highlighting the trade-offs between them. It also discusses the implications of false positives and negatives in real-world scenarios, particularly in relation to safety in ADAS. Furthermore, the document introduces sensor fusion and perception solutions, explaining how they contribute to creating a 3D environmental model for automated driving systems. The focus remains on evaluating performance metrics and their significance in enhancing the reliability of machine-learning applications in ADAS.