The DOI matches its Crossref record; 72 of the 75 references it cites were found in a registry or an index.
Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication
Open accessAbstract
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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“To alleviate the impact of lost information, Li et al. [76] proposed a novel approach, including an LC-aware repair network (LCRN) and a V2V-AM, to mitigate the detrimental influence and enhance the interaction between vehicles.”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [76]
In the citing paper
“However, most cooperative perception algorithms assume ideal communication and there is a lack of analysis on the impact of lossy communication on feature sharing [76].”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [76]
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Metadata
- 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)
- Venue
- IEEE Transactions on Intelligent Vehicles
- Published
- 2023-04
- Volume
- 8
- Issue
- 4
- Pages
- 2650-2660
- ISSN
- 2379-8858 (ISSN-L)
- 2379-8904
- 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
- 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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