23 citations · 37 across the 23 of their papers we have counts for
25 papers
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…
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…
CogDriver: Integrating Cognitive Inertia for Temporally Coherent Planning in Autonomous Driving
Pei Liu, Qingtian Ning, Xinyan Lu +6
The pursuit of autonomous agents capable of temporally coherent planning is hindered by a fundamental flaw in current vision-language models (VLMs): they lack cognitive inertia. Op…
MagicRoad: Semantic-Aware 3D Road Surface Reconstruction via Obstacle Inpainting
Xingyue Peng, Yuandong Lyu, Lang Zhang +8
Road surface reconstruction is essential for autonomous driving, supporting centimeter-accurate lane perception and high-definition mapping in complex urban environments.While rece…
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…
DriveAction: A Benchmark for Exploring Human-like Driving Decisions in VLA Models
Yuhan Hao, Zhengning Li, Lei Sun +7
Vision-Language-Action (VLA) models have advanced autonomous driving, but existing benchmarks still lack scenario diversity, reliable action-level annotation, and evaluation protoc…
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…
TokenFLEX: Unified VLM Training for Flexible Visual Tokens Inference
Junshan Hu, Jialiang Mao, Zhikang Liu +3
Conventional Vision-Language Models(VLMs) typically utilize a fixed number of vision tokens, regardless of task complexity. This one-size-fits-all strategy introduces notable ineff…
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…
Other Vehicle Trajectories Are Also Needed: A Driving World Model Unifies Ego-Other Vehicle Trajectories in Video Latent Space
Jian Zhu, Zhengyu Jia, Tian Gao +6
Advanced end-to-end autonomous driving systems predict other vehicles' motions and plan ego vehicle's trajectory. The world model that can foresee the outcome of the trajectory has…
GaussianAD: Gaussian-Centric End-to-End Autonomous Driving
Wenzhao Zheng, Junjie Wu, Yao Zheng +8
Vision-based autonomous driving shows great potential due to its satisfactory performance and low costs. Most existing methods adopt dense representations (e.g., bird's eye view) o…
UniPLV: Towards Label-Efficient Open-World 3D Scene Understanding by Regional Visual Language Supervision
Yuru Wang, Pei Liu, Songtao Wang +7
Open-world 3D scene understanding is a critical challenge that involves recognizing and distinguishing diverse objects and categories from 3D data, such as point clouds, without re…
StreetCrafter: Street View Synthesis with Controllable Video Diffusion Models
Yunzhi Yan, Zhen Xu, Haotong Lin +8
This paper aims to tackle the problem of photorealistic view synthesis from vehicle sensor data. Recent advancements in neural scene representation have achieved notable success in…
Data Scaling Laws for Imitation Learning-Based End-to-End Autonomous Driving
Yupeng Zheng, Pengxuan Yang, Zhongpu Xia +9
The end-to-end autonomous driving paradigm has recently attracted lots of attention due to its scalability. However, existing methods are constrained by the limited scale of real-w…
ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration
Chaojun Ni, Guosheng Zhao, Xiaofeng Wang +13
Closed-loop simulation is crucial for end-to-end autonomous driving. Existing sensor simulation methods (e.g., NeRF and 3DGS) reconstruct driving scenes based on conditions that cl…
Generalizing Motion Planners with Mixture of Experts for Autonomous Driving
Qiao Sun, Huimin Wang, Jiahao Zhan +7
Large real-world driving datasets have sparked significant research into various aspects of data-driven motion planners for autonomous driving. These include data augmentation, mod…
Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation
Enhui Ma, Lijun Zhou, Tao Tang +9
Using generative models to synthesize new data has become a de-facto standard in autonomous driving to address the data scarcity issue. Though existing approaches are able to boost…
PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning
Yupeng Zheng, Zebin Xing, Qichao Zhang +8
Vehicle motion planning is an essential component of autonomous driving technology. Current rule-based vehicle motion planning methods perform satisfactorily in common scenarios bu…
S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking
Tao Tang, Lijun Zhou, Pengkun Hao +9
3D multiple object tracking (MOT) plays a crucial role in autonomous driving perception. Recent end-to-end query-based trackers simultaneously detect and track objects, which have…
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
Xiaoyu Tian, Junru Gu, Bailin Li +7
A primary hurdle of autonomous driving in urban environments is understanding complex and long-tail scenarios, such as challenging road conditions and delicate human behaviors. We…
Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting
Yunzhi Yan, Haotong Lin, Chenxu Zhou +6
This paper aims to tackle the problem of modeling dynamic urban streets for autonomous driving scenes. Recent methods extend NeRF by incorporating tracked vehicle poses to animate…