23 citations · 53 across the 69 of their papers we have counts for
20 papers · 1 filter
WorldRFT: Latent World Model Planning with Reinforcement Fine-Tuning for Autonomous Driving
Pengxuan Yang, Ben Lu, Zhongpu Xia +7
Latent World Models enhance scene representation through temporal self-supervised learning, presenting a perception annotation-free paradigm for end-to-end autonomous driving. Howe…
OmniGen: Unified Multimodal Sensor Generation for Autonomous Driving
Tao Tang, Enhui Ma, xia zhou +9
Autonomous driving has seen remarkable advancements, largely driven by extensive real-world data collection. However, acquiring diverse and corner-case data remains costly and inef…
DriveLiDAR4D: Sequential and Controllable LiDAR Scene Generation for Autonomous Driving
Kaiwen Cai, Xinze Liu, Xia Zhou +7
The generation of realistic LiDAR point clouds plays a crucial role in the development and evaluation of autonomous driving systems. Although recent methods for 3D LiDAR point clou…
CorrectAD: A Self-Correcting Agentic System to Improve End-to-end Planning in Autonomous Driving
Enhui Ma, Lijun Zhou, Tao Tang +11
End-to-end planning methods are the de facto standard of the current autonomous driving system, while the robustness of the data-driven approaches suffers due to the notorious long…
SparseWorld-TC: Trajectory-Conditioned Sparse Occupancy World Model
Jiayuan Du, Yiming Zhao, Zhenglong Guo +5
This paper introduces a novel architecture for trajectory-conditioned forecasting of future 3D scene occupancy. In contrast to methods that rely on variational autoencoders (VAEs)…
AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models
Tianyi Yan, Tao Tang, Xingtai Gui +11
End-to-end models for autonomous driving hold the promise of learning complex behaviors directly from sensor data, but face critical challenges in safety and handling long-tail eve…
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…
Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving
Pengxiang Li, Yinan Zheng, Yue Wang +6
End-to-End (E2E) solutions have emerged as a mainstream approach for autonomous driving systems, with Vision-Language-Action (VLA) models representing a new paradigm that leverages…
The Better You Learn, The Smarter You Prune: Towards Efficient Vision-language-action Models via Differentiable Token Pruning
Titong Jiang, Xuefeng Jiang, Yuan Ma +7
We present LightVLA, a simple yet effective differentiable token pruning framework for vision-language-action (VLA) models. While VLA models have shown impressive capability in exe…
Adversarial-Guided Diffusion for Multimodal LLM Attacks
Chengwei Xia, Fan Ma, Ruijie Quan +2
This paper addresses the challenge of generating adversarial image using a diffusion model to deceive multimodal large language models (MLLMs) into generating the targeted response…
DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking
Weicheng Zheng, Xiaofei Mao, Nanfei Ye +4
The advent of Vision-Language Models (VLMs) has significantly advanced end-to-end autonomous driving, demonstrating powerful reasoning abilities for high-level behavior planning ta…
HiNeuS: High-fidelity Neural Surface Mitigating Low-texture and Reflective Ambiguity
Yida Wang, Xueyang Zhang, Kun Zhan +2
Neural surface reconstruction faces persistent challenges in reconciling geometric fidelity with photometric consistency under complex scene conditions. We present HiNeuS, a unifie…
RoboPearls: Editable Video Simulation for Robot Manipulation
Tao Tang, Likui Zhang, Youpeng Wen +9
The development of generalist robot manipulation policies has seen significant progress, driven by large-scale demonstration data across diverse environments. However, the high cos…
GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control
Anthony Chen, Wenzhao Zheng, Yida Wang +5
Recent advancements in world models have revolutionized dynamic environment simulation, allowing systems to foresee future states and assess potential actions. In autonomous drivin…
TransDiffuser: Diverse Trajectory Generation with Decorrelated Multi-modal Representation for End-to-end Autonomous Driving
Xuefeng Jiang, Yuan Ma, Pengxiang Li +7
In recent years, diffusion models have demonstrated remarkable potential across diverse domains, from vision generation to language modeling. Transferring its generative capabiliti…
PosePilot: Steering Camera Pose for Generative World Models with Self-supervised Depth
Bu Jin, Weize Li, Baihan Yang +9
Recent advancements in autonomous driving (AD) systems have highlighted the potential of world models in achieving robust and generalizable performance across both ordinary and cha…
DiVE: Efficient Multi-View Driving Scenes Generation Based on Video Diffusion Transformer
Junpeng Jiang, Gangyi Hong, Miao Zhang +4
Collecting multi-view driving scenario videos to enhance the performance of 3D visual perception tasks presents significant challenges and incurs substantial costs, making generati…
StyledStreets: Multi-style Street Simulator with Spatial and Temporal Consistency
Yuyin Chen, Yida Wang, Xueyang Zhang +4
Urban scene reconstruction requires modeling both static infrastructure and dynamic elements while supporting diverse environmental conditions. We present \textbf{StyledStreets}, a…
Learning Personalized Driving Styles via Reinforcement Learning from Human Feedback
Derun Li, Changye Li, Yue Wang +9
Generating human-like and adaptive trajectories is essential for autonomous driving in dynamic environments. While generative models have shown promise in synthesizing feasible tra…
BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities
Zhibo Tian, Ruijie Quan, Fan Ma +2
Reconstructing perceived images from human brain activity forms a crucial link between human and machine learning through Brain-Computer Interfaces. Early methods primarily focused…