Zobrazeno 1 - 10
of 224
pro vyhledávání: '"Wang Chengyue"'
Autor:
Liao, Haicheng, Li, Yongkang, Wang, Chengyue, Lai, Songning, Li, Zhenning, Bian, Zilin, Lee, Jaeyoung, Cui, Zhiyong, Zhang, Guohui, Xu, Chengzhong
The primary goal of traffic accident anticipation is to foresee potential accidents in real time using dashcam videos, a task that is pivotal for enhancing the safety and reliability of autonomous driving technologies. In this study, we introduce an
Externí odkaz:
http://arxiv.org/abs/2409.01256
Autor:
Liao, Haicheng, Sun, Haoyu, Shen, Huanming, Wang, Chengyue, Tam, Kahou, Tian, Chunlin, Li, Li, Xu, Chengzhong, Li, Zhenning
Accurately and promptly predicting accidents among surrounding traffic agents from camera footage is crucial for the safety of autonomous vehicles (AVs). This task presents substantial challenges stemming from the unpredictable nature of traffic acci
Externí odkaz:
http://arxiv.org/abs/2407.17757
Autor:
Liao, Haicheng, Li, Yongkang, Wang, Chengyue, Guan, Yanchen, Tam, KaHou, Tian, Chunlin, Li, Li, Xu, Chengzhong, Li, Zhenning
As autonomous driving systems increasingly become part of daily transportation, the ability to accurately anticipate and mitigate potential traffic accidents is paramount. Traditional accident anticipation models primarily utilizing dashcam videos ar
Externí odkaz:
http://arxiv.org/abs/2407.16277
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:
Liao, Haicheng, Li, Xuelin, Li, Yongkang, Kong, Hanlin, Wang, Chengyue, Wang, Bonan, Guan, Yanchen, Tam, KaHou, Li, Zhenning, Xu, Chengzhong
Trajectory prediction is a cornerstone in autonomous driving (AD), playing a critical role in enabling vehicles to navigate safely and efficiently in dynamic environments. To address this task, this paper presents a novel trajectory prediction model
Externí odkaz:
http://arxiv.org/abs/2405.02145
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:
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, Yongkang, Li, Zhenning, Wang, Chengyue, Cui, Zhiyong, Li, Shengbo Eben, Xu, Chengzhong
In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency. Incorporating human decision-making insights enables AVs to more effectively a
Externí odkaz:
http://arxiv.org/abs/2402.19251
Autor:
Liao, Haicheng, Liu, Shangqian, Li, Yongkang, Li, Zhenning, Wang, Chengyue, Li, Yunjian, Li, Shengbo Eben, Xu, Chengzhong
In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often rely on computational methods such as time-series analysis. Our resear
Externí odkaz:
http://arxiv.org/abs/2402.04318
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