235 citations · 329 across the 17 of their papers we have counts for
17 papers
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…
Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersections
Zeyu Zhu, Huijing Zhao
In recent years, great efforts have been devoted to deep imitation learning for autonomous driving control, where raw sensory inputs are directly mapped to control actions. However…
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-…
An Image-based Approach of Task-driven Driving Scene Categorization
Shaochi Hu, Hanwei Fan, Biao Gao +2
Categorizing driving scenes via visual perception is a key technology for safe driving and the downstream tasks of autonomous vehicles. Traditional methods infer scene category by…
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…
A Survey of Deep RL and IL for Autonomous Driving Policy Learning
Zeyu Zhu, Huijing Zhao
Autonomous driving (AD) agents generate driving policies based on online perception results, which are obtained at multiple levels of abstraction, e.g., behavior planning, motion p…
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…
Scene Context Based Semantic Segmentation for 3D LiDAR Data in Dynamic Scene
Jilin Mei, Huijing Zhao
We propose a graph neural network(GNN) based method to incorporate scene context for the semantic segmentation of 3D LiDAR data. The problem is defined as building a graph to repre…
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…
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…
SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances
Yancheng Pan, Biao Gao, Jilin Mei +3
3D semantic segmentation is one of the key tasks for autonomous driving system. Recently, deep learning models for 3D semantic segmentation task have been widely researched, but th…
Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning
Zeyu Zhu, Nan Li, Ruoyu Sun +2
Terrain traversability analysis is a fundamental issue to achieve the autonomy of a robot at off-road environments. Geometry-based and appearance-based methods have been studied in…
Incorporating Human Domain Knowledge in 3D LiDAR-based Semantic Segmentation
Jilin Mei, Huijing Zhao
This work studies semantic segmentation using 3D LiDAR data. Popular deep learning methods applied for this task require a large number of manual annotations to train the parameter…
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…