# Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles

Source: [Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles](<https://www.sota2.com/research/paper/68fa6a581e209131cea27994>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:22:37.170Z

Authors: Ayoosh Bansal, Jayati Singh, Micaela Verucchi, Marco Caccamo, Lui Sha

Published: 2021-06-08T07:30:43.000Z

## 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 ($R^3$) metrics for object detection systems. The $R^3$ 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2106.04146>)

[PDF](<https://arxiv.org/pdf/2106.04146.pdf>)