1 code implementation • 14 Dec 2023 • Runwei Guan, Haocheng Zhao, Shanliang Yao, Ka Lok Man, Xiaohui Zhu, Limin Yu, Yong Yue, Jeremy Smith, Eng Gee Lim, Weiping Ding, Yutao Yue
Urban water-surface robust perception serves as the foundation for intelligent monitoring of aquatic environments and the autonomous navigation and operation of unmanned vessels, especially in the context of waterway safety.
2 code implementations • 8 Dec 2023 • Shanliang Yao, Runwei Guan, Zitian Peng, Chenhang Xu, Yilu Shi, Weiping Ding, Eng Gee Lim, Yong Yue, Hyungjoon Seo, Ka Lok Man, Jieming Ma, Xiaohui Zhu, Yutao Yue
This review focuses on exploring different radar data representations utilized in autonomous driving systems.
1 code implementation • 20 Aug 2023 • Runwei Guan, Shanliang Yao, Xiaohui Zhu, Ka Lok Man, Yong Yue, Jeremy Smith, Eng Gee Lim, Yutao Yue
In this paper, we focus on riverway panoptic perception based on USVs, which is a considerably unexplored field compared with road panoptic perception.
1 code implementation • 14 Jul 2023 • Runwei Guan, Shanliang Yao, Xiaohui Zhu, Ka Lok Man, Eng Gee Lim, Jeremy Smith, Yong Yue, Yutao Yue
Current perception models for different tasks usually exist in modular forms on Unmanned Surface Vehicles (USVs), which infer extremely slowly in parallel on edge devices, causing the asynchrony between perception results and USV position, and leading to error decisions of autonomous navigation.
Ranked #1 on 2D Semantic Segmentation on WaterScenes
2 code implementations • 13 Jul 2023 • Shanliang Yao, Runwei Guan, Zhaodong Wu, Yi Ni, Zile Huang, Zixian Zhang, Yong Yue, Weiping Ding, Eng Gee Lim, Hyungjoon Seo, Ka Lok Man, Xiaohui Zhu, Yutao Yue
This work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset for autonomous driving on water surfaces.
Ranked #1 on Object Detection on WaterScenes
2 code implementations • 20 Apr 2023 • Shanliang Yao, Runwei Guan, Xiaoyu Huang, Zhuoxiao Li, Xiangyu Sha, Yong Yue, Eng Gee Lim, Hyungjoon Seo, Ka Lok Man, Xiaohui Zhu, Yutao Yue
Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years, enabling vehicles to accurately detect and interpret surrounding environment for safe and efficient navigation.