most citedLearning from Naturalistic Driving Data for Human-like Autonomous Highway Driving

8 citations · 9 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV20208 cited

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…

cs.CV20201 cited

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…

cs.CV2020

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…

cs.RO2020

Scene-Aware Error Modeling of LiDAR/Visual Odometry for Fusion-based Vehicle Localization

Xiaoliang Ju, Donghao Xu, Huijing Zhao

Localization is an essential technique in mobile robotics. In a complex environment, it is necessary to fuse different localization modules to obtain more robust results, in which…

cs.RO2018

Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning

Jilin Mei, Biao Gao, Donghao Xu +3

This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications. A system of semantic segmentation using 3D LiDAR data, including…