no code implementations • 2 May 2024 • Haicheng Liao, Zhenning Li, Chengyue Wang, Huanming Shen, Bonan Wang, Dongping Liao, Guofa Li, Chengzhong Xu
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.
no code implementations • 5 Mar 2024 • Yanchen Guan, Haicheng Liao, Zhenning Li, Jia Hu, Runze Yuan, Yunjian Li, Guohui Zhang, Chengzhong Xu
In the rapidly evolving landscape of autonomous driving, the capability to accurately predict future events and assess their implications is paramount for both safety and efficiency, critically aiding the decision-making process.
1 code implementation • 29 Feb 2024 • Haicheng Liao, Yongkang Li, Zhenning Li, Chengyue Wang, Zhiyong Cui, Shengbo Eben Li, Chengzhong Xu
In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency.
1 code implementation • 11 Dec 2023 • Haicheng Liao, Zhenning Li, Huanming Shen, Wenxuan Zeng, Dongping Liao, Guofa Li, Shengbo Eben Li, Chengzhong Xu
The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles.
1 code implementation • 6 Dec 2023 • Haicheng Liao, Huanming Shen, Zhenning Li, Chengyue Wang, Guofa Li, Yiming Bie, Chengzhong Xu
In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge.
1 code implementation • 20 Jul 2021 • Vincent Wilmet, Sauraj Verma, Tabea Redl, Håkon Sandaker, Zhenning Li
Anomaly detection in images plays a significant role for many applications across all industries, such as disease diagnosis in healthcare or quality assurance in manufacturing.
no code implementations • 13 Jul 2021 • Zhenning Li, Chengzhong Xu, Guohui Zhang
Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy.
no code implementations • 20 Apr 2021 • Zhenning Li, Hao Yu, Guohui Zhang, Shangjia Dong, Cheng-Zhong Xu
Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste.
Multi-agent Reinforcement Learning reinforcement-learning +1