CoPEM: Cooperative Perception Error Models for Autonomous Driving

• URL: http://arxiv.org/abs/2211.11175v2
• Date: Tue, 22 Nov 2022 03:22:19 GMT
• Title: CoPEM: Cooperative Perception Error Models for Autonomous Driving
• Authors: Andrea Piazzoni, Jim Cherian, Roshan Vijay, Lap-Pui Chau, Justin Dauwels
• Abstract summary: We focus on the (onboard) perception of Autonomous Vehicles (AV), which can manifest as misdetection errors on the occluded objects. We introduce the notion of Cooperative Perception Error Models (coPEMs) towards achieving an effective integration of V2X solutions within a virtual test environment.
• Score: 20.60246432605745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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