CoPEM: Cooperative Perception Error Models for Autonomous Driving

TL;DR

This paper proposes Cooperative Perception Error Models (coPEMs) to efficiently simulate and analyze the impact of cooperative perception in autonomous driving, especially addressing occlusion-related perception errors in virtual environments.

Contribution

The paper introduces coPEMs as a novel extension of perception error models to simulate cooperative perception in virtual testing of autonomous vehicles.

Findings

coPEMs effectively model cooperative perception errors

Simulation shows improved safety with cooperative perception

Occlusion handling reduces perception errors in AV scenarios

Abstract

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.