235 citations · 329 across the 17 of their papers we have counts for
8 papers · 1 filter
Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Data
Yancheng Pan, Fan Xie, Huijing Zhao
3D semantic segmentation (3DSS) is an essential process in the creation of a safe autonomous driving system. However, deep learning models for 3D semantic segmentation often suffer…
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation
Biao Gao, Xijun Zhao, Huijing Zhao
Off-road semantic segmentation with fine-grained labels is necessary for autonomous vehicles to understand driving scenes, as the coarse-grained road detection can not satisfy off-…
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning
Biao Gao, Shaochi Hu, Xijun Zhao +1
Road detection or traversability analysis has been a key technique for a mobile robot to traverse complex off-road scenes. The problem has been mainly formulated in early works as…
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study
Biao Gao, Yancheng Pan, Chengkun Li +2
3D semantic segmentation is a fundamental task for robotic and autonomous driving applications. Recent works have been focused on using deep learning techniques, whereas developing…
Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving
Donghao Xu, Zhezhang Ding, Xu He +4
Driving in a human-like manner is important for an autonomous vehicle to be a smart and predictable traffic participant. To achieve this goal, parameters of the motion planning mod…
Driver Identification through Stochastic Multi-State Car-Following Modeling
Donghao Xu, Zhezhang Ding, Chenfeng Tu +4
Intra-driver and inter-driver heterogeneity has been confirmed to exist in human driving behaviors by many studies. In this study, a joint model of the two types of heterogeneity i…
Cross Scene Prediction via Modeling Dynamic Correlation using Latent Space Shared Auto-Encoders
Shaochi Hu, Donghao Xu, Huijing Zhao
This work addresses on the following problem: given a set of unsynchronized history observations of two scenes that are correlative on their dynamic changes, the purpose is to lear…
Off-Road Drivable Area Extraction Using 3D LiDAR Data
Biao Gao, Anran Xu, Yancheng Pan +3
We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application. A specific deep learning framework is designed to dea…