22 citations · 26 across the 11 of their papers we have counts for
13 papers
ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Driving
Qiyu Ruan, Yuxuan Wang, He Li +2
Safety-critical scenarios are central to evaluating autonomous driving systems, yet their rarity in naturalistic logs makes simulation-based stress testing indispensable. Most scen…
CogOmniControl: Reasoning-Driven Controllable Video Generation via Creative Intent Cognition
Hongji Yang, Songlian Li, Yucheng Zhou +4
Recent diffusion models achieve strong photorealism and fluency in video generation, yet remain fragile under abstract, sparse or complex conditions, leading to poor performance in…
OccDirector: Language-Guided Behavior and Interaction Generation in 4D Occupancy Space
Zhuding Liang, Tianyi Yan, Dubing Chen +6
Generative world models increasingly rely on 4D occupancy for realistic autonomous driving simulation. However, existing generation frameworks depend on rigid geometric conditions…
SignNav: Leveraging Signage for Semantic Visual Navigation in Large-Scale Indoor Environments
Jian Sun, Yuming Huang, He Li +6
Humans routinely leverage semantic hints provided by signage to navigate to destinations within novel Large-Scale Indoor (LSI) environments, such as hospitals and airport terminals…
Bridging Scene Generation and Planning: Driving with World Model via Unifying Vision and Motion Representation
Xingtai Gui, Meijie Zhang, Tianyi Yan +5
End-to-end autonomous driving aims to generate safe and plausible planning policies from raw sensor input. Driving world models have shown great potential in learning rich represen…
TrajDiff: End-to-end Autonomous Driving without Perception Annotation
Xingtai Gui, Jianbo Zhao, Wencheng Han +5
End-to-end autonomous driving systems directly generate driving policies from raw sensor inputs. While these systems can extract effective environmental features for planning, rely…
RLGF: Reinforcement Learning with Geometric Feedback for Autonomous Driving Video Generation
Tianyi Yan, Wencheng Han, Xia Zhou +4
Synthetic data is crucial for advancing autonomous driving (AD) systems, yet current state-of-the-art video generation models, despite their visual realism, suffer from subtle geom…
Breaking Down Monocular Ambiguity: Exploiting Temporal Evolution for 3D Lane Detection
Huan Zheng, Wencheng Han, Tianyi Yan +2
Monocular 3D lane detection aims to estimate the 3D position of lanes from frontal-view (FV) images. However, existing methods are fundamentally constrained by the inherent ambigui…
OLiDM: Object-aware LiDAR Diffusion Models for Autonomous Driving
Tianyi Yan, Junbo Yin, Xianpeng Lang +3
To enhance autonomous driving safety in complex scenarios, various methods have been proposed to simulate LiDAR point cloud data. Nevertheless, these methods often face challenges…
DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation
Tianyi Yan, Dongming Wu, Wencheng Han +5
Autonomous driving evaluation requires simulation environments that closely replicate actual road conditions, including real-world sensory data and responsive feedback loops. Howev…
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving
Wencheng Han, Dongqian Guo, Cheng-Zhong Xu +1
In the field of autonomous driving, two important features of autonomous driving car systems are the explainability of decision logic and the accuracy of environmental perception.…
DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection
Li Xiang, Junbo Yin, Wei Li +3
Vehicle-to-Everything (V2X) collaborative perception has recently gained significant attention due to its capability to enhance scene understanding by integrating information from…
Language Prompt for Autonomous Driving
Dongming Wu, Wencheng Han, Yingfei Liu +4
A new trend in the computer vision community is to capture objects of interest following flexible human command represented by a natural language prompt. However, the progress of u…