9 citations · 28 across the 30 of their papers we have counts for
8 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…
nuTruck: Benchmarking Autonomous Driving Planning for Distributed Electric-drive Trucks
Jinyu Miao, Pu Zhang, Yifei He +5
The dominance of traditional rule-based methods in autonomous driving has gradually been replaced by learning-based approaches. While learning-based planners have achieved consider…
MapAgent: An Industrial-Grade Agentic Framework for City-scale Lane-level Map Generation
Deguo Xia, Zihan Li, Haochen Zhao +6
Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities rem…
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…
UniT: Unified Geometry Learning with Group Autoregressive Transformer
Haotian Wang, Yusong Huang, Zhaonian Kuang +4
Recent feed-forward models have significantly advanced geometry perception for inferring dense 3D structure from sensor observations. However, its essential capabilities remain fra…
4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving
Kane Qian, Xin Zhao, Yining Shi +7
We present 4DLidarOpen, a large-scale open multi-modal dataset for autonomous driving, centered on 4D frequency-modulated continuous-wave (FMCW) Lidar sensing. Unlike conventional…
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…
Physics-informed Deep Mixture-of-Koopmans Vehicle Dynamics Model with Dual-branch Encoder for Distributed Electric-drive Trucks
Jinyu Miao, Pu Zhang, Rujun Yan +8
Advanced autonomous driving systems require accurate vehicle dynamics modeling. However, identifying a precise dynamics model remains challenging due to strong nonlinearities and t…