V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer
نویسندگان
چکیده
AbstractIn this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve perception performance autonomous vehicles. We present a robust cooperative framework with V2X using novel vision Transformer. Specifically, build holistic attention model, namely V2X-ViT, effectively fuse information across on-road agents (i.e., vehicles and infrastructure). V2X-ViT consists alternating layers heterogeneous multi-agent self-attention multi-scale window self-attention, which captures inter-agent interaction per-agent spatial relationships. These key modules are designed in unified Transformer architecture handle common challenges, including asynchronous sharing, pose errors, heterogeneity components. To validate our approach, create large-scale dataset CARLA OpenCDA. Extensive experimental results demonstrate that sets new state-of-the-art for 3D object detection achieves even under harsh, noisy environments. The code is available at https://github.com/DerrickXuNu/v2x-vit.KeywordsV2XVehicle-to-EverythingCooperative perceptionAutonomous drivingTransformer
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2022
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-19842-7_7