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V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction

Autor: Zhou, Zewei, Xiang, Hao, Zheng, Zhaoliang, Zhao, Seth Z., Lei, Mingyue, Zhang, Yun, Cai, Tianhui, Liu, Xinyi, Liu, Johnson, Bajji, Maheswari, Pham, Jacob, Xia, Xin, Huang, Zhiyu, Zhou, Bolei, Ma, Jiaqi

Vehicle-to-everything (V2X) technologies offer a promising paradigm to mitigate the limitations of constrained observability in single-vehicle systems. Prior work primarily focuses on single-frame cooperative perception, which fuses agents' informati

Externí odkaz: http://arxiv.org/abs/2412.01812

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Driving with Regulation: Interpretable Decision-Making for Autonomous Vehicles with Retrieval-Augmented Reasoning via LLM

Autor: Cai, Tianhui, Liu, Yifan, Zhou, Zewei, Ma, Haoxuan, Zhao, Seth Z., Wu, Zhiwen, Ma, Jiaqi

This work presents an interpretable decision-making framework for autonomous vehicles that integrates traffic regulations, norms, and safety guidelines comprehensively and enables seamless adaptation to different regions. While traditional rule-based

Externí odkaz: http://arxiv.org/abs/2410.04759

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Akademický článek

RESEARCH ON STRAIGHTENING METHOD OF STEPPED PIPE

Autor: GAO Feng, ZHOU ZeWei, WANG Long, GUO ZhengLu, LI Yan

Publikováno v: Jixie qiangdu, Vol 43, Pp 982-987 (2021)

The three-point bending pressure straightening method is employed to deal with the problem of single curvature bent stepped pipe. Based on the theory of elastoplastic mechanics the analysis of deformation process for the stepped pipe with bilinear st

Externí odkaz: https://doaj.org/article/6fce139e4e1542379a735819922a97d2

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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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