13 citations · 23 across the 17 of their papers we have counts for
19 papers
TC-ADA: One-Shot Active Domain Adaptation for Semantic Segmentation
Weihao Yan, Yeqiang Qian, Yueyuan Li +3
Manual dense annotation remains a major obstacle to deploying semantic segmentation models in new driving environments. Active domain adaptation (ADA) seeks label-efficient transfe…
Beyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentation
Weihao Yan, Yeqiang Qian, Yi Dong +1
Precise medical image segmentation is crucial for clinical diagnosis and treatment planning, yet relies heavily on expensive expert annotations. Semi-supervised medical image segme…
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…
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…
Zero-Shot UAV Navigation in Forests via Relightable 3D Gaussian Splatting
Zinan Lv, Yeqian Qian, Chen Sang +3
UAV navigation in unstructured outdoor environments using passive monocular vision is hindered by the substantial visual domain gap between simulation and reality. While 3D Gaussia…
End-to-end Autonomous Vehicle Following System using Monocular Fisheye Camera
Jiale Zhang, Yeqiang Qian, Tong Qin +3
The increase in vehicle ownership has led to increased traffic congestion, more accidents, and higher carbon emissions. Vehicle platooning is a promising solution to address these…
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…
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…
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…
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…
LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization
Mingrui Liu, Xinyang Tang, Yeqiang Qian +2
Precise and long-term stable localization is essential in parking lots for tasks like autonomous driving or autonomous valet parking, \textit{etc}. Existing methods rely on a fixed…
SUNet: Scale-aware Unified Network for Panoptic Segmentation
Weihao Yan, Yeqiang Qian, Chunxiang Wang +1
Panoptic segmentation combines the advantages of semantic and instance segmentation, which can provide both pixel-level and instance-level environmental perception information for…
Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation
Weihao Yan, Yeqiang Qian, Chunxiang Wang +1
Semantic segmentation is an important task for intelligent vehicles to understand the environment. Current deep learning methods require large amounts of labeled data for training.…
BAANet: Learning Bi-directional Adaptive Attention Gates for Multispectral Pedestrian Detection
Xiaoxiao Yang, Yeqian Qiang, Huijie Zhu +2
Thermal infrared (TIR) image has proven effectiveness in providing temperature cues to the RGB features for multispectral pedestrian detection. Most existing methods directly injec…
Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios
Xiaoliang Wang, Yeqiang Qian, Chunxiang Wang +1
As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios. However, eg…
Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward
Chenchen Zhao, Yeqiang Qian, Ming Yang
Accurate pedestrian orientation estimation of autonomous driving helps the ego vehicle obtain the intentions of pedestrians in the related environment, which are the base of safety…
Joint Calibration of Panoramic Camera and Lidar Based on Supervised Learning
Mingwei Cao, Ming Yang, Chunxiang Wang +2
In view of contemporary panoramic camera-laser scanner system, the traditional calibration method is not suitable for panoramic cameras whose imaging model is extremely nonlinear.…