73 citations · 96 across the 15 of their papers we have counts for
15 papers
LOGOS: LiDAR-Only Gaussian Elevation Splatting for Unified Tiny Obstacle Segmentation
Nan Ming, Yeqiang Qian, Chunxiang Wang +1
Robust obstacle segmentation is essential for the safety of intelligent robots, where LiDAR-based perception systems play a fundamental role in the robot-environment interaction. W…
Generating Roadside LiDAR Datasets from Vehicle-Side Datasets via Novel View Synthesis
Yuhan Xia, Runxin Zhao, Hanyang Zhuang +2
Intelligent Transportation Systems (ITS) require reliable environmental perception to support safe and efficient transportation. With the rapid development of Vehicle-to-everything…
RadarXFormer: Robust Object Detection via Cross-Dimension Fusion of 4D Radar Spectra and Images for Autonomous Driving
Yue Sun, Yeqiang Qian, Zhe Wang +3
Reliable perception is essential for autonomous driving systems to operate safely under diverse real-world traffic conditions. However, camera- and LiDAR-based perception systems s…
Which LiDAR scanning pattern is better for roadside perception: Repetitive or Non-repetitive?
Zhiqi Qi, Runxin Zhao, Hanyang Zhuang +2
LiDAR-based roadside perception is a cornerstone of advanced Intelligent Transportation Systems (ITS). While considerable research has addressed optimal LiDAR placement for infrast…
Bench-RNR: Dataset for Benchmarking Repetitive and Non-repetitive Scanning LiDAR for Infrastructure-based Vehicle Localization
Runxin Zhao, Chunxiang Wang, Hanyang Zhuang +1
Vehicle localization using roadside LiDARs can provide centimeter-level accuracy for cloud-controlled vehicles while simultaneously serving multiple vehicles, enhanc-ing safety and…
Weather-Magician: Reconstruction and Rendering Framework for 4D Weather Synthesis In Real Time
Chen Sang, Yeqiang Qian, Jiale Zhang +2
For tasks such as urban digital twins, VR/AR/game scene design, or creating synthetic films, the traditional industrial approach often involves manually modeling scenes and using v…
Depth-aware Fusion Method based on Image and 4D Radar Spectrum for 3D Object Detection
Yue Sun, Yeqiang Qian, Chunxiang Wang +1
Safety and reliability are crucial for the public acceptance of autonomous driving. To ensure accurate and reliable environmental perception, intelligent vehicles must exhibit accu…
Cross-Modal Visual Relocalization in Prior LiDAR Maps Utilizing Intensity Textures
Qiyuan Shen, Hengwang Zhao, Weihao Yan +3
Cross-modal localization has drawn increasing attention in recent years, while the visual relocalization in prior LiDAR maps is less studied. Related methods usually suffer from in…
From Imitation to Exploration: End-to-end Autonomous Driving based on World Model
Yueyuan Li, Mingyang Jiang, Songan Zhang +3
In recent years, end-to-end autonomous driving architectures have gained increasing attention due to their advantage in avoiding error accumulation. Most existing end-to-end autono…
SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation
Weihao Yan, Yeqiang Qian, Yueyuan Li +3
Semantic segmentation plays an important role in intelligent vehicles, providing pixel-level semantic information about the environment. However, the labeling budget is expensive a…
HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios
Mingyang Jiang, Yueyuan Li, Songan Zhang +3
Automated parking stands as a highly anticipated application of autonomous driving technology. However, existing path planning methodologies fall short of addressing this need due…
AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian Detection
Zizhao Chen, Yeqiang Qian, Xiaoxiao Yang +2
Multispectral pedestrian detection has been shown to be effective in improving performance within complex illumination scenarios. However, prevalent double-stream networks in multi…
SAM4UDASS: When SAM Meets Unsupervised Domain Adaptive Semantic Segmentation in Intelligent Vehicles
Weihao Yan, Yeqiang Qian, Xingyuan Chen +3
Semantic segmentation plays a critical role in enabling intelligent vehicles to comprehend their surrounding environments. However, deep learning-based methods usually perform poor…
Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving
Yueyuan Li, Wei Yuan, Songan Zhang +4
Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings. Over the past few years, the number of simulators for autonomous driving h…
Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making
Yueyuan Li, Songan Zhang, Mingyang Jiang +4
Simulation is a prospective method for generating diverse and realistic traffic scenarios to aid in the development of driving decision-making systems. However, existing simulators…