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Autonomous driving has attracted significant attention from both academia and industries, which is expected to offer a safer and more efficient driving system.
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R. Xu, H. Xiang, Z. Tu, X. Xia, M.-H. Yang, and J. Ma, “V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer,” in
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Cited alongside, same era.
Z. Lei, S. Ren, Y. Hu, W. Zhang, and S. Chen, “Latency-Aware Collaborative Perception,” in
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Y. Han, H. Zhang, H. Li, Y. Jin, C. Lang, and Y. Li, “Collaborative Perception in Autonomous Driving: Methods, Datasets, and Challenges,”
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2023
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R. Xu, Z. Tu, H. Xiang, W. Shao, B. Zhou, and J. Ma, “CoBEVT: Cooperative Bird’s Eye View Semantic Segmentation with Sparse Transformers,” in
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Z. Fang, S. Hu, H. An, Y. Zhang, J. Wang, H. Cao, X. Chen, and Y. Fang, “ PACP: Priority-Aware Collaborative Perception for Connected and Autonomous Vehicles ,”
2024
Closest in time.
Y. Hu, S. Fang, Z. Lei, Y. Zhong, and S. Chen, “Where2comm: communication-efficient collaborative perception via spatial confidence maps,” in
2024
Closest in time.
S. Hu, Z. Fang, H. An, G. Xu, Y. Zhou, X. Chen, and Y. Fang, “Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving,” in
2024
Closest in time.
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R. Xu, X. Xia, J. Li, H. Li, S. Zhang, Z. Tu, Z. Meng, H. Xiang, X. Dong, R. Song, H. Yu, B. Zhou, and J. Ma, “V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception.”
Cited in the paper.