Search Results for author: Zhengru Fang

Found 6 papers, 1 papers with code

AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learning

no code implementations9 Apr 2024 Senkang Hu, Zhengru Fang, Zihan Fang, Yiqin Deng, Xianhao Chen, Yuguang Fang

In addition, the single-vehicle autonomous driving systems lack of the ability of collaboration and negotiation with other vehicles, which is crucial for the safety and efficiency of autonomous driving systems.

Autonomous Driving Language Modelling +1

SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and Judger Mechanism

no code implementations1 Feb 2024 Yuang Zhang, Haonan An, Zhengru Fang, Guowen Xu, Yuan Zhou, Xianhao Chen, Yuguang Fang

Moreover, in the context of collaborative perception, it is important to recognize that not all CAVs contribute valuable data, and some CAV data even have detrimental effects on collaborative perception.

Autonomous Driving

Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities

no code implementations3 Jan 2024 Senkang Hu, Zhengru Fang, Yiqin Deng, Xianhao Chen, Yuguang Fang

Autonomous driving has attracted significant attention from both academia and industries, which is expected to offer a safer and more efficient driving system.

Autonomous Driving

Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving

1 code implementation28 Nov 2023 Senkang Hu, Zhengru Fang, Xianhao Chen, Yuguang Fang, Sam Kwong

To address these challenges, we propose a unified domain generalization framework applicable in both training and inference stages of collaborative perception.

Autonomous Driving Domain Generalization

Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving

no code implementations15 Sep 2023 Senkang Hu, Zhengru Fang, Haonan An, Guowen Xu, Yuan Zhou, Xianhao Chen, Yuguang Fang

To address these issues, we propose ACC-DA, a channel-aware collaborative perception framework to dynamically adjust the communication graph and minimize the average transmission delay while mitigating the side effects from the data heterogeneity.

Autonomous Driving

Underwater Differential Game: Finite-Time Target Hunting Task with Communication Delay

no code implementations1 Feb 2022 Wei Wei, Jingjing Wang, Jun Du, Zhengru Fang, Chunxiao Jiang, Yong Ren

Simulations show that underwater disturbances have a large impact on the system considering communication delay.

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