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

arXiv: https://arxiv.org/abs/2106.04146 · HTML: https://mlsanity.com/paper/2106.04146/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Agent Prioritization for Autonomous Navigation](https://mlsanity.com/paper/1909.08792/) (2019)
- [Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning](https://mlsanity.com/paper/1806.07635/) (2018)

## Built on by (most cited first, 10 of 10)

- [On Offline Evaluation of 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2308.12779/) (2023)
- [Evaluating Object (mis)Detection from a Safety and Reliability Perspective: Discussion and Measures](https://mlsanity.com/paper/2203.02205/) (2022)
- [Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System through Distributed Database and Multimodal Perception: Demonstrated in Crossroads](https://mlsanity.com/paper/2410.17576/) (2024)
- [LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving](https://mlsanity.com/paper/2111.09799/) (2021)
- [Verifiable Obstacle Detection](https://mlsanity.com/paper/2208.14403/) (2022)
- [A Safety-Adapted Loss for Pedestrian Detection in Automated Driving](https://mlsanity.com/paper/2402.02986/) (2024)
- [Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults](https://mlsanity.com/paper/2209.01710/) (2022)
- [Object criticality for safer navigation](https://mlsanity.com/paper/2406.10232/) (2024)
- [Checklist to Define the Identification of TP, FP, and FN Object Detections in Automated Driving](https://mlsanity.com/paper/2308.07106/) (2023)
- [A Machine Learning Perspective on Automated Driving Corner Cases](https://mlsanity.com/paper/2510.10653/) (2025)

## Abstract (arXiv)

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.