84 citations · 107 across the 16 of their papers we have counts for
18 papers
WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving
Xinlin Wang, Yujiao Xiang, Yuheng Zhou +11
Video Joint Embedding Predictive Architecture (V-JEPA) learns powerful spatiotemporal representations from video through self-supervised latent feature prediction. However, V-JEPA…
BehaviorWorldGen: Closing the Loop between Action Models and World Simulators via Controllable Behavior-Aware Structured World Generation
Jiaqi Wang, Zhuo Zhang, Haining Guan +15
Modern driving action models are increasingly improved in a self-improvement loop, where a learned world simulator imagines future observations and the resulting data is fed back t…
Walking in the Implicit: Interactive World Exploration via Neural Scene Representation
Zhiqi Li, Chengrui Dong, Zhenhua Du +6
Interactive video generation systems for camera-controlled world exploration roll out growing sequences of latent video frames, entangling state transition with high-frequency obse…
ChainFlow-VLA: Causal Flow Planning with Vision-Language Models
Xiyang Wang, Xinlin Wang, Tingguang Zhou +7
Current end-to-end autonomous driving systems are fundamentally limited by a mismatch between temporal causal reasoning and global trajectory consistency. Autoregressive (AR) model…
PVRF: All-in-one Adverse Weather Removal via Prior-modulated and Velocity-constrained Rectified Flow
Wei Dong, Han Zhou, Terry Ji +6
Adverse weather removal (AWR) in real-world images remains challenging due to heterogeneous and unseen degradations, while distortion-driven training often yields overly smooth res…
CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving
Minqing Huang, Yujiao Xiang, Zihan Liang +8
Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing reasoning mechanisms still struggle to provide plannin…
Any 3D Scene is Worth 1K Tokens: 3D-Grounded Representation for Scene Generation at Scale
Dongxu Wei, Qi Xu, Zhiqi Li +6
Scene-level 3D generation has long been dominated by 2D multi-view or video diffusion models. Typically, these 2D-based approaches perform scene generation in a 2D latent space, wh…
Autoregressive End-to-End Planning with Time-Invariant Spatial Alignment and Multi-Objective Policy Refinement
Jianbo Zhao, Taiyu Ban, Xiangjie Li +5
The inherent sequential modeling capabilities of autoregressive models make them a formidable baseline for end-to-end planning in autonomous driving. Nevertheless, their performanc…
Autoregressive Meta-Actions for Unified Controllable Trajectory Generation
Jianbo Zhao, Taiyu Ban, Xiyang Wang +6
Controllable trajectory generation guided by high-level semantic decisions, termed meta-actions, is crucial for autonomous driving systems. A significant limitation of existing fra…
DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling
Jianbo Zhao, Taiyu Ban, Zhihao Liu +7
Accurate and efficient modeling of agent interactions is essential for trajectory generation, the core of autonomous driving systems. Existing methods, scene-centric, agent-centric…
MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving
Xiyang Wang, Shouzheng Qi, Jieyou Zhao +8
This paper introduces MCTrack, a new 3D multi-object tracking method that achieves state-of-the-art (SOTA) performance across KITTI, nuScenes, and Waymo datasets. Addressing the ga…
KiGRAS: Kinematic-Driven Generative Model for Realistic Agent Simulation
Jianbo Zhao, Jiaheng Zhuang, Qibin Zhou +7
Trajectory generation is a pivotal task in autonomous driving. Recent studies have introduced the autoregressive paradigm, leveraging the state transition model to approximate futu…
StreamMOTP: Streaming and Unified Framework for Joint 3D Multi-Object Tracking and Trajectory Prediction
Jiaheng Zhuang, Guoan Wang, Siyu Zhang +5
3D multi-object tracking and trajectory prediction are two crucial modules in autonomous driving systems. Generally, the two tasks are handled separately in traditional paradigms a…
SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving
Diankun Zhang, Guoan Wang, Runwen Zhu +15
End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system. Despite simplicity and clarity, the performance of end-to-end autonomous driv…
MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction
Chen Feng, Hangning Zhou, Huadong Lin +5
Predicting the future behavior of agents is a fundamental task in autonomous vehicle domains. Accurate prediction relies on comprehending the surrounding map, which significantly r…
You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking
Xiyang Wang, Chunyun Fu, Jiawei He +6
In the classical tracking-by-detection (TBD) paradigm, detection and tracking are separately and sequentially conducted, and data association must be properly performed to achieve…
A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval
Yilan Zhang, Fengying Xie, Xuedong Song +4
The computer-aided diagnosis (CAD) system can provide a reference basis for the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs) can not only extract visua…
TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction
Yuting Wang, Hangning Zhou, Zhigang Zhang +10
This technical report presents an effective method for motion prediction in autonomous driving. We develop a Transformer-based method for input encoding and trajectory prediction.…