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Multilevel Graph Reinforcement Learning for Consistent Cognitive Decision-making in Heterogeneous Mixed Autonomy

Autor: Gao, Xin, Ma, Zhaoyang, Li, Xueyuan, Meng, Xiaoqiang, Li, Zirui

In the realm of heterogeneous mixed autonomy, vehicles experience dynamic spatial correlations and nonlinear temporal interactions in a complex, non-Euclidean space. These complexities pose significant challenges to traditional decision-making framew

Externí odkaz: http://arxiv.org/abs/2408.08516

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A Nested Graph Reinforcement Learning-based Decision-making Strategy for Eco-platooning

Autor: Gao, Xin, Li, Xueyuan, Liu, Hao, Li, Ao, Ma, Zhaoyang, Li, Zirui

Platooning technology is renowned for its precise vehicle control, traffic flow optimization, and energy efficiency enhancement. However, in large-scale mixed platoons, vehicle heterogeneity and unpredictable traffic conditions lead to virtual bottle

Externí odkaz: http://arxiv.org/abs/2408.07578

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V2X-Real: a Large-Scale Dataset for Vehicle-to-Everything Cooperative Perception

Autor: Xiang, Hao, Zheng, Zhaoliang, Xia, Xin, Xu, Runsheng, Gao, Letian, Zhou, Zewei, Han, Xu, Ji, Xinkai, Li, Mingxi, Meng, Zonglin, Jin, Li, Lei, Mingyue, Ma, Zhaoyang, He, Zihang, Ma, Haoxuan, Yuan, Yunshuang, Zhao, Yingqian, Ma, Jiaqi

Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled autonomous vehicles to share sensing information to see through occlusions, greatly boosting the perception capability. However, there are no real-world datasets to facilita

Externí odkaz: http://arxiv.org/abs/2403.16034

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Zero-Shot Digital Rock Image Segmentation with a Fine-Tuned Segment Anything Model

Autor: Ma, Zhaoyang, He, Xupeng, Sun, Shuyu, Yan, Bicheng, Kwak, Hyung, Gao, Jun

Accurate image segmentation is crucial in reservoir modelling and material characterization, enhancing oil and gas extraction efficiency through detailed reservoir models. This precision offers insights into rock properties, advancing digital rock ph

Externí odkaz: http://arxiv.org/abs/2311.10865

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Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks

Autor: Ma, Zhaoyang, He, Xupeng, Kwak, Hyung, Gao, Jun, Sun, Shuyu, Yan, Bicheng

In digital rock physics, analysing microstructures from CT and SEM scans is crucial for estimating properties like porosity and pore connectivity. Traditional segmentation methods like thresholding and CNNs often fall short in accurately detailing ro

Externí odkaz: http://arxiv.org/abs/2311.06079

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