TY - GEN
T1 - CoPEM
T2 - 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
AU - Piazzoni, Andrea
AU - Cherian, Jim
AU - Vijay, Roshan
AU - Chau, Lap Pui
AU - Dauwels, Justin
N1 - Funding Information:
This work was supported in part by the Centre of Excellence for Testing & Research of AVs - NTU (CETRAN), under the Connected Smart Mobility (COSMO) programme. 1ERI@N, Interdisciplinary Graduate Programme, Singapore [email protected] 2Centre of Excellence for Testing & Research of AVs, Nanyang Technological University, Singapore [email protected], [email protected] 3School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore [email protected] 4TU Delft, Dept. of Microelectronics, Fac. EEMCS, Mekelweg 4 2628 CD, Delft. [email protected]
Publisher Copyright:
© 2022 IEEE.
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85141853737&partnerID=8YFLogxK
U2 - 10.1109/ITSC55140.2022.9921807
DO - 10.1109/ITSC55140.2022.9921807
M3 - Conference article published in proceeding or book
AN - SCOPUS:85141853737
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 3934
EP - 3939
BT - 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 October 2022 through 12 October 2022
ER -