# CoPEM: Cooperative Perception Error Models for Autonomous Driving

arXiv: https://arxiv.org/abs/2211.11175 · HTML: https://mlsanity.com/paper/2211.11175/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving](https://mlsanity.com/paper/2005.03778/) (2020)
- [Baidu Apollo EM Motion Planner](https://mlsanity.com/paper/1807.08048/) (2018)
- [Fast and Robust Registration of Partially Overlapping Point Clouds](https://mlsanity.com/paper/2112.09922/) (2021)
- [Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations](https://mlsanity.com/paper/2011.08581/) (2020)
- [ViSTA: a Framework for Virtual Scenario-based Testing of Autonomous Vehicles](https://mlsanity.com/paper/2109.02529/) (2021)
- [Optimal Placement of Roadside Infrastructure Sensors towards Safer Autonomous Vehicle Deployments](https://mlsanity.com/paper/2110.01251/) (2021)
- [Optimized sensor placement for dependable roadside infrastructures](https://mlsanity.com/paper/1910.05314/) (2019)

## Built on by (most cited first, 2 of 2)

- [Vehicle-to-Everything Cooperative Perception for Autonomous Driving](https://mlsanity.com/paper/2310.03525/) (2023)
- [mmCooper: A Multi-agent Multi-stage Communication-efficient and Collaboration-robust Cooperative Perception Framework](https://mlsanity.com/paper/2501.12263/) (2025)

## Abstract (arXiv)

In this paper, we introduce the notion of Cooperative Perception Error Models (coPEMs) towards achieving an effective and efficient integration of V2X solutions within a virtual test environment. We focus our analysis on the occlusion problem in the (onboard) perception of Autonomous Vehicles (AV), which can manifest as misdetection errors on the occluded objects. Cooperative perception (CP) solutions based on Vehicle-to-Everything (V2X) communications aim to avoid such issues by cooperatively leveraging additional points of view for the world around the AV. This approach usually requires many sensors, mainly cameras and LiDARs, to be deployed simultaneously in the environment either as part of the road infrastructure or on other traffic vehicles. However, implementing a large number of sensor models in a virtual simulation pipeline is often prohibitively computationally expensive. Therefore, in this paper, we rely on extending Perception Error Models (PEMs) to efficiently implement such cooperative perception solutions along with the errors and uncertainties associated with them. We demonstrate the approach by comparing the safety achievable by an AV challenged with a traffic scenario where occlusion is the primary cause of a potential collision.