6 citations · 18 across the 37 of their papers we have counts for
19 papers · 1 filter
Leveraging Visual and Geometric Priors for Metric-scale and Complete Vehicle Gaussian Reconstruction from Limited Views
Jinyu Miao, Jiusi Li, Yifei He +4
High-fidelity vehicle assets are essential for controllable traffic scene generation, particularly for synthesizing rare and safety-critical long-tail scenarios. However, reconstru…
Envision4D: Envisioning Visual Futures via Feed-forward 4D Gaussian Splatting for Autonomous Driving
Qi Song, Yifei He, Chi Zhang +6
Forecasting the future evolution of dynamic scenes is crucial in autonomous driving. However, existing feed-forward paradigms are primarily designed for interpolation. When extende…
CAVE: A Structured Credit Assignment Approach for Fragmented Visual Evidence Reasoning
Tengda Guo, Jie Leng, Hanlei Li +6
Vision-Language Models (VLMs) have achieved strong performance on general multimodal reasoning, yet remain challenged in integrating nonlocal visual information to support semantic…
XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments
Kangan Qian, ChuChu Xie, Yang Zhong +13
Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from complex environments. Current cloud…
Realistic and Controllable 3D Gaussian-Guided Object Editing for Driving Video Generation
Jiusi Li, Jackson Jiang, Jinyu Miao +10
Corner cases are crucial for training and validating autonomous driving systems, yet collecting them from the real world is often costly and hazardous. Editing objects within captu…
PriorFusion: Unified Integration of Priors for Robust Road Perception in Autonomous Driving
Xuewei Tang, Mengmeng Yang, Tuopu Wen +7
With the growing interest in autonomous driving, there is an increasing demand for accurate and reliable road perception technologies. In complex environments without high-definiti…
A Survey on Vision-Language-Action Models for Autonomous Driving
Sicong Jiang, Zilin Huang, Kangan Qian +17
The rapid progress of multimodal large language models (MLLM) has paved the way for Vision-Language-Action (VLA) paradigms, which integrate visual perception, natural language unde…
COME: Adding Scene-Centric Forecasting Control to Occupancy World Model
Yining Shi, Kun Jiang, Qiang Meng +6
World models are critical for autonomous driving to simulate environmental dynamics and generate synthetic data. Existing methods struggle to disentangle ego-vehicle motion (perspe…
CleanMAP: Distilling Multimodal LLMs for Confidence-Driven Crowdsourced HD Map Updates
Ankit Kumar Shaw, Kun Jiang, Tuopu Wen +5
The rapid growth of intelligent connected vehicles (ICVs) and integrated vehicle-road-cloud systems has increased the demand for accurate, real-time HD map updates. However, ensuri…
POD: Predictive Object Detection with Single-Frame FMCW LiDAR Point Cloud
Yining Shi, Kun Jiang, Xin Zhao +5
LiDAR-based 3D object detection is a fundamental task in the field of autonomous driving. This paper explores the unique advantage of Frequency Modulated Continuous Wave (FMCW) LiD…
LEGO-Motion: Learning-Enhanced Grids with Occupancy Instance Modeling for Class-Agnostic Motion Prediction
Kangan Qian, Jinyu Miao, Ziang Luo +7
Accurate and reliable spatial and motion information plays a pivotal role in autonomous driving systems. However, object-level perception models struggle with handling open scenari…
Advancing Autonomous Vehicle Intelligence: Deep Learning and Multimodal LLM for Traffic Sign Recognition and Robust Lane Detection
Chandan Kumar Sah, Ankit Kumar Shaw, Xiaoli Lian +5
Autonomous vehicles (AVs) require reliable traffic sign recognition and robust lane detection capabilities to ensure safe navigation in complex and dynamic environments. This paper…
LDMapNet-U: An End-to-End System for City-Scale Lane-Level Map Updating
Deguo Xia, Weiming Zhang, Xiyan Liu +6
An up-to-date city-scale lane-level map is an indispensable infrastructure and a key enabling technology for ensuring the safety and user experience of autonomous driving systems.…
How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic
Kangan Qian, Jinyu Miao, Xinyu Jiao +6
Understanding the spatial dynamics of cars within urban systems is essential for optimizing infrastructure management and resource allocation. Recent empirical approaches for analy…
Enhancing Lane Segment Perception and Topology Reasoning with Crowdsourcing Trajectory Priors
Peijin Jia, Ziang Luo, Tuopu Wen +4
In autonomous driving, recent advances in lane segment perception provide autonomous vehicles with a comprehensive understanding of driving scenarios. Moreover, incorporating prior…
DuMapNet: An End-to-End Vectorization System for City-Scale Lane-Level Map Generation
Deguo Xia, Weiming Zhang, Xiyan Liu +5
Generating city-scale lane-level maps faces significant challenges due to the intricate urban environments, such as blurred or absent lane markings. Additionally, a standard lane-l…
PanoSSC: Exploring Monocular Panoptic 3D Scene Reconstruction for Autonomous Driving
Yining Shi, Jiusi Li, Kun Jiang +4
Vision-centric occupancy networks, which represent the surrounding environment with uniform voxels with semantics, have become a new trend for safe driving of camera-only autonomou…
EFFOcc: Learning Efficient Occupancy Networks from Minimal Labels for Autonomous Driving
Yining Shi, Kun Jiang, Jinyu Miao +8
3D occupancy prediction (3DOcc) is a rapidly rising and challenging perception task in the field of autonomous driving. Existing 3D occupancy networks (OccNets) are both computatio…
DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model
Peijin Jia, Tuopu Wen, Ziang Luo +9
Constructing high-definition (HD) maps is a crucial requirement for enabling autonomous driving. In recent years, several map segmentation algorithms have been developed to address…