Cited by 161Autonomous Vehicle Technology and SafetyAdvanced Neural Network ApplicationsVehicular Ad Hoc Networks (VANETs)

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Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication

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Li, Jinlong, Xu, Runsheng, Liu, Xinyu, Ma, Jin, Chi, Zicheng, Ma, Jiaqi, Yu, Hongkai·2023·IEEE Transactions on Intelligent Vehicles

DOI 10.1109/tiv.2023.3260040

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Abstract

Deep learning has been widely used in intelligent vehicle driving perception systems, such as 3D object detection. One promising technique is Cooperative Perception, which leverages Vehicle-to-Vehicle (V2V) communication to share deep learning-based features among vehicles. However, most cooperative perception algorithms assume ideal communication and do not consider the impact of Lossy Communication (LC), which is very common in the real world, on feature sharing. In this paper, we explore the effects of LC on Cooperative Perception and propose a novel approach to mitigate these effects. Our approach includes an LC-aware Repair Network (LCRN) and a V2V Attention Module (V2VAM) with intra-vehicle attention and uncertainty-aware inter-vehicle attention. We demonstrate the effectiveness of our approach on the public OPV2V dataset (a digital-twin simulated dataset) using point cloud-based 3D object detection. Our results show that our approach improves detection performance under lossy V2V communication. Specifically, our proposed method achieves a significant improvement in Average Precision compared to the state-of-the-art cooperative perception algorithms, which proves the capability of our approach to effectively mitigate the negative impact of LC and enhance the interaction between the ego vehicle and other vehicles.

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Authors: Li, J., Xu, R., Liu, X., Ma, J., Chi, Z., Ma, J., & Yu, H.
Affiliations: Cleveland State University (US)
University of California, Los Angeles (US)
ORCID: Li, Jinlong
Xu, Runsheng
Liu, Xinyu
Ma, Jin
Chi, Zicheng
Ma, Jiaqi
Yu, Hongkai
Venue: IEEE Transactions on Intelligent Vehicles
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2023-04
Volume: 8
Issue: 4
Pages: 2650-2660
ISSN: 2379-8858 (ISSN-L)
2379-8904
DOI: 10.1109/tiv.2023.3260040
OpenAlex: W4352977781
Cited by: 161
References: 75
Citations by year: 2026: 26 · 2025: 55 · 2024: 57 · 2023: 23
FWCI: 11.68
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (am)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Intelligent Vehicles (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University) (cc-by)
Funders: NSF (2215388, CNS-2127881)
OpenCDA Ecosystem
Topics: Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
Keywords: vehicle-to-vehicle communication, cooperative perception, lossy communication, 3D object detection, point clouds, feature sharing, attention mechanism, intelligent vehicles

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Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication

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