@inproceedings{4b5a848daae6430f901049202eb68998,
title = "Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles",
abstract = "Commonly used metrics for evaluation of object detection systems (precision, recall, mAP) do not give complete information about their suitability of use in safety critical tasks, like obstacle detection for collision avoidance in Autonomous Vehicles (AV). This work introduces the Risk Ranked Recall (R3) metrics for object detection systems. The R3 metrics categorize objects within three ranks. Ranks are assigned based on an objective cyber-physical model for the risk of collision. Recall is measured for each rank.",
keywords = "Autonomous CPS, Dependable CPS, Safety",
author = "Ayoosh Bansal and Jayati Singh and Micaela Verucchi and Marco Caccamo and Lui Sha",
note = "Funding Information: ACKNOWLEDGMENT The material presented in this paper is based upon work supported by the Office of Naval Research (ONR) under grant number N00014-17-1-2783 and by the National Science Foundation (NSF) under grant numbers CNS 1646383, CNS 1932529 and CNS 1815891. Marco Caccamo was also supported by an Alexander von Humboldt Professorship endowed by the German Federal Ministry of Education and Research. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsors. Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th Mediterranean Conference on Embedded Computing, MECO 2021 ; Conference date: 07-06-2021 Through 10-06-2021",
year = "2021",
month = jun,
day = "7",
doi = "10.1109/MECO52532.2021.9460196",
language = "English (US)",
series = "2021 10th Mediterranean Conference on Embedded Computing, MECO 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 10th Mediterranean Conference on Embedded Computing, MECO 2021",
address = "United States",
}