most citedGraph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms

108 citations · 400 across the 12 of their papers we have counts for

collaborators

14 papers

cs.RO20217 cited

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…

cs.CV2021108 cited

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…

cs.RO20216 cited

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…

cs.CV2021

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…

cs.CV2021

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…

cs.CV20213 cited

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…