11 citations · 22 across the 11 of their papers we have counts for
11 papers
Label-efficient Semantic Scene Completion with Scribble Annotations
Song Wang, Jiawei Yu, Wentong Li +4
Semantic scene completion aims to infer the 3D geometric structures with semantic classes from camera or LiDAR, which provide essential occupancy information in autonomous driving.…
HVOFusion: Incremental Mesh Reconstruction Using Hybrid Voxel Octree
Shaofan Liu, Junbo Chen, Jianke Zhu
Incremental scene reconstruction is essential to the navigation in robotics. Most of the conventional methods typically make use of either TSDF (truncated signed distance functions…
Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation
Song Wang, Jiawei Yu, Wentong Li +4
Semantic scene completion, also known as semantic occupancy prediction, can provide dense geometric and semantic information for autonomous vehicles, which attracts the increasing…
MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction
Xiaolu Liu, Song Wang, Wentong Li +3
Currently, high-definition (HD) map construction leans towards a lightweight online generation tendency, which aims to preserve timely and reliable road scene information. However,…
FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving
Tengju Ye, Wei Jing, Chunyong Hu +11
Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving. However, leveraging such…
OG: Equip vision occupancy with instance segmentation and visual grounding
Zichao Dong, Hang Ji, Weikun Zhang +2
Occupancy prediction tasks focus on the inference of both geometry and semantic labels for each voxel, which is an important perception mission. However, it is still a semantic seg…
CCIL: Context-conditioned imitation learning for urban driving
Ke Guo, Wei Jing, Junbo Chen +1
Imitation learning holds great promise for addressing the complex task of autonomous urban driving, as experienced human drivers can navigate highly challenging scenarios with ease…
Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flow
Dongkun Zhang, Jintao Xue, Yuxiang Cui +6
Acquiring driving policies that can transfer to unseen environments is challenging when driving in dense traffic flows. The design of traffic flow is essential and previous studies…
FLYOVER: A Model-Driven Method to Generate Diverse Highway Interchanges for Autonomous Vehicle Testing
Yuan Zhou, Gengjie Lin, Yun Tang +6
It has become a consensus that autonomous vehicles (AVs) will first be widely deployed on highways. However, the complexity of highway interchanges becomes the bottleneck for deplo…
Automatic Map Generation for Autonomous Driving System Testing
Yun Tang, Yuan Zhou, Kairui Yang +5
High-definition (HD) maps are essential in testing autonomous driving systems (ADSs). HD maps essentially determine the potential diversity of the testing scenarios. However, the c…
Domain Generalization for Vision-based Driving Trajectory Generation
Yunkai Wang, Dongkun Zhang, Yuxiang Cui +5
One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for visi…