154 citations · 712 across the 31 of their papers we have counts for
13 papers · 1 filter
csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps
Zhenhua Xu, Yuxuan Liu, Lu Gan +4
High-Definition (HD) maps can provide precise geometric and semantic information of static traffic environments for autonomous driving. Road-boundary is one of the most important i…
Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning
Yuxiang Sun, Bo Yuan, Yufan Xue +3
Researchers are increasingly focusing on intelligent games as a hot research area.The article proposes an algorithm that combines the multi-attribute management and reinforcement l…
On Bundle Adjustment for Multiview PointCloud Registration
Huaiyang Huang, Yuxiang Sun, Jin Wu +5
Multiview registration is used to estimate Rigid Body Transformations (RBTs) from multiple frames and reconstruct a scene with corresponding scans. Despite the success of pairwise…
Incorporating Learnt Local and Global Embeddings into Monocular Visual SLAM
Huaiyang Huang, Haoyang Ye, Yuxiang Sun +2
Traditional approaches for Visual Simultaneous Localization and Mapping (VSLAM) rely on low-level vision information for state estimation, such as handcrafted local features or the…
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks
Peide Cai, Hengli Wang, Yuxiang Sun +1
Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of road users are uncertain and are hard to model explicitly, and the ego-vehicle should a…
CP-loss: Connectivity-preserving Loss for Road Curb Detection in Autonomous Driving with Aerial Images
Zhenhua Xu, Yuxiang Sun, Lujia Wang +1
Road curb detection is important for autonomous driving. It can be used to determine road boundaries to constrain vehicles on roads, so that potential accidents could be avoided. M…
Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation
Hengli Wang, Peide Cai, Yuxiang Sun +2
Recently, deep-learning based approaches have achieved impressive performance for autonomous driving. However, end-to-end vision-based methods typically have limited interpretabili…
End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving
Hengli Wang, Peide Cai, Rui Fan +2
With the recent advancement of deep learning technology, data-driven approaches for autonomous car prediction and planning have achieved extraordinary performance. Nevertheless, mo…
iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous Driving
Zhenhua Xu, Yuxiang Sun, Ming Liu
Detection of road curbs is an essential capability for autonomous driving. It can be used for autonomous vehicles to determine drivable areas on roads. Usually, road curbs are dete…
S2P2: Self-Supervised Goal-Directed Path Planning Using RGB-D Data for Robotic Wheelchairs
Hengli Wang, Yuxiang Sun, Rui Fan +1
Path planning is a fundamental capability for autonomous navigation of robotic wheelchairs. With the impressive development of deep-learning technologies, imitation learning-based…
Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms
Hengli Wang, Rui Fan, Yuxiang Sun +1
Joint detection of drivable areas and road anomalies is very important for mobile robots. Recently, many semantic segmentation approaches based on convolutional neural networks (CN…
Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving
Zhenhua Xu, Yuxiang Sun, Ming Liu
Road-boundary detection is important for autonomous driving. It can be used to constrain autonomous vehicles running on road areas to ensure driving safety. Compared with online ro…
Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars
Xiaodong Mei, Yuxiang Sun, Yuying Chen +2
In autonomous driving, navigation through unsignaled intersections with many traffic participants moving around is a challenging task. To provide a solution to this problem, we pro…