Zobrazeno 1 - 10
of 507
pro vyhledávání: '"Li, Guofa"'
Autor:
Liao, Haicheng, Li, Yongkang, Li, Zhenning, Wang, Chengyue, Tian, Chunlin, Huang, Yuming, Bian, Zilin, Zhu, Kaiqun, Li, Guofa, Pu, Ziyuan, Hu, Jia, Cui, Zhiyong, Xu, Chengzhong
Accurately and safely predicting the trajectories of surrounding vehicles is essential for fully realizing autonomous driving (AD). This paper presents the Human-Like Trajectory Prediction model (HLTP++), which emulates human cognitive processes to i
Externí odkaz:
http://arxiv.org/abs/2407.07020
Autor:
Kou, Wei-Bin, Lin, Qingfeng, Tang, Ming, Xu, Sheng, Ye, Rongguang, Leng, Yang, Wang, Shuai, Li, Guofa, Chen, Zhenyu, Zhu, Guangxu, Wu, Yik-Chung
Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization due to data heterogeneity in an ever domain-shifting environment. While Federated Learning (FL) could improve the generalization of an AD model (known as FedAD syste
Externí odkaz:
http://arxiv.org/abs/2405.04146
Autor:
Liao, Haicheng, Li, Zhenning, Wang, Chengyue, Shen, Huanming, Wang, Bonan, Liao, Dongping, Li, Guofa, Xu, Chengzhong
This paper introduces a trajectory prediction model tailored for autonomous driving, focusing on capturing complex interactions in dynamic traffic scenarios without reliance on high-definition maps. The model, termed MFTraj, harnesses historical traj
Externí odkaz:
http://arxiv.org/abs/2405.01266
Autor:
Li, Guofang
The purpose of this ethnographic research was to understand four Chinese immigrant children and their families' beliefs and uses of literacy in their intersecting worlds of home, school, and community in a Canadian context from a socio-cultural persp
Externí odkaz:
http://library.usask.ca/theses/available/etd-10212004-002000
Autor:
Liao, Haicheng, Li, Zhenning, Wang, Chengyue, Wang, Bonan, Kong, Hanlin, Guan, Yanchen, Li, Guofa, Cui, Zhiyong, Xu, Chengzhong
As autonomous driving technology progresses, the need for precise trajectory prediction models becomes paramount. This paper introduces an innovative model that infuses cognitive insights into trajectory prediction, focusing on perceived safety and d
Externí odkaz:
http://arxiv.org/abs/2404.17520
Autor:
Liao, Haicheng, Li, Zhenning, Shen, Huanming, Zeng, Wenxuan, Liao, Dongping, Li, Guofa, Li, Shengbo Eben, Xu, Chengzhong
The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that
Externí odkaz:
http://arxiv.org/abs/2312.06371
Autor:
Liao, Haicheng, Shen, Huanming, Li, Zhenning, Wang, Chengyue, Li, Guofa, Bie, Yiming, Xu, Chengzhong
In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge. This paper introduces a sophisticated encoder-decoder framework, developed t
Externí odkaz:
http://arxiv.org/abs/2312.03543
Publikováno v:
In Advanced Engineering Informatics October 2024 62 Part C
Publikováno v:
In Engineering Applications of Artificial Intelligence October 2024 136 Part A
Publikováno v:
In Engineering Applications of Artificial Intelligence July 2024 133 Part D