2 citations · 4 across the 10 of their papers we have counts for
12 papers
CTS-PLL: A Robust and Anytime Framework for Collaborative Task Sequencing and Multi-Agent Path Finding
Junkai Jiang, Yitao Xu, Ruochen Li +2
The Collaborative Task Sequencing and Multi-Agent Path Finding (CTS-MAPF) problem requires agents to accomplish sequences of tasks while avoiding collisions, posing significant cha…
RaFD: Flow-Guided Radar Detection for Robust Autonomous Driving
Shuocheng Yang, Zikun Xu, Jiahao Wang +3
Radar has shown strong potential for robust perception in autonomous driving; however, raw radar images are frequently degraded by noise and "ghost" artifacts, making object detect…
Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks
Zeyu Han, Shuocheng Yang, Minghan Zhu +4
Autonomous vehicles and robots rely on accurate odometry estimation in GPS-denied environments. While LiDARs and cameras struggle under extreme weather, 4D mmWave radar emerges as…
ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots
Junkai Jiang, Yihe Chen, Yibin Yang +3
Multi-vehicle trajectory planning (MVTP) is one of the key challenges in multi-robot systems (MRSs) and has broad applications across various fields. This paper presents ESCoT, an…
CTS-CBS: A New Approach for Multi-Agent Collaborative Task Sequencing and Path Finding
Junkai Jiang, Ruochen Li, Yibin Yang +4
This paper addresses a generalization problem of Multi-Agent Pathfinding (MAPF), called Collaborative Task Sequencing - Multi-Agent Pathfinding (CTS-MAPF), where agents must plan c…
Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark
Jiahao Wang, Xiangyu Cao, Jiaru Zhong +7
While cooperative perception can overcome the limitations of single-vehicle systems, the practical implementation of vehicle-to-vehicle and vehicle-to-infrastructure systems is oft…
RINO: Accurate, Robust Radar-Inertial Odometry with Non-Iterative Estimation
Shuocheng Yang, Yueming Cao, Shengbo Eben Li +2
Odometry in adverse weather conditions, such as fog, rain, and snow, presents significant challenges, as traditional vision and LiDAR-based methods often suffer from degraded perfo…
DenserRadar: A 4D millimeter-wave radar point cloud detector based on dense LiDAR point clouds
Zeyu Han, Junkai Jiang, Xiaokang Ding +4
The 4D millimeter-wave (mmWave) radar, with its robustness in extreme environments, extensive detection range, and capabilities for measuring velocity and elevation, has demonstrat…
CSDO: Enhancing Efficiency and Success in Large-Scale Multi-Vehicle Trajectory Planning
Yibin Yang, Shaobing Xu, Xintao Yan +3
This paper presents an efficient algorithm, naming Centralized Searching and Decentralized Optimization (CSDO), to find feasible solution for large-scale Multi-Vehicle Trajectory P…
PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network
Yuning Wang, Zhiyuan Liu, Haotian Lin +3
Scene understanding, defined as learning, extraction, and representation of interactions among traffic elements, is one of the critical challenges toward high-level autonomous driv…
MergeOcc: Bridge the Domain Gap between Different LiDARs for Robust Occupancy Prediction
Zikun Xu, Jianqiang Wang, Shaobing Xu
LiDAR-based 3D occupancy prediction evolved rapidly alongside the emergence of large datasets. Nevertheless, the potential of existing diverse datasets remains underutilized as the…
A Risk-aware Planning Framework of UGVs in Off-Road Environment
Junkai Jiang, Zhenhua Hu, Zihan Xie +7
Planning module is an essential component of intelligent vehicle study. In this paper, we address the risk-aware planning problem of UGVs through a global-local planning framework…