108 citations · 402 across the 15 of their papers we have counts for
15 papers
Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning
Peide Cai, Sukai Wang, Hengli Wang +1
Autonomous driving in urban crowds at unregulated intersections is challenging, where dynamic occlusions and uncertain behaviors of other vehicles should be carefully considered. T…
Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms
Rui Fan, Hengli Wang, Yuan Wang +2
Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based. The former approaches typically employ 2-D image analysis/understan…
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…
Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning
Peide Cai, Hengli Wang, Huaiyang Huang +2
Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computati…
SCV-Stereo: Learning Stereo Matching from a Sparse Cost Volume
Hengli Wang, Rui Fan, Ming Liu
Convolutional neural network (CNN)-based stereo matching approaches generally require a dense cost volume (DCV) for disparity estimation. However, generating such cost volumes is c…
Co-Teaching: An Ark to Unsupervised Stereo Matching
Hengli Wang, Rui Fan, Ming Liu
Stereo matching is a key component of autonomous driving perception. Recent unsupervised stereo matching approaches have received adequate attention due to their advantage of not r…
SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection
Hengli Wang, Rui Fan, Peide Cai +1
Freespace detection is a fundamental component of autonomous driving perception. Recently, deep convolutional neural networks (DCNNs) have achieved impressive performance for this…
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…
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…
PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching
Hengli Wang, Rui Fan, Peide Cai +1
Supervised learning with deep convolutional neural networks (DCNNs) has seen huge adoption in stereo matching. However, the acquisition of large-scale datasets with well-labeled gr…
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…
Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation
Rui Fan, Hengli Wang, Peide Cai +4
Collision-free space detection is a critical component of autonomous vehicle perception. The state-of-the-art algorithms are typically based on supervised learning. The performance…
CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation
Hengli Wang, Rui Fan, Ming Liu
The interpretation of ego motion and scene change is a fundamental task for mobile robots. Optical flow information can be employed to estimate motion in the surroundings. Recently…
DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks
Peide Cai, Hengli Wang, Yuxiang Sun +1
Traditional decision and planning frameworks for self-driving vehicles (SDVs) scale poorly in new scenarios, thus they require tedious hand-tuning of rules and parameters to mainta…