18 papers
BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving
Bing Zhan, Shuyao Shang, Shuo Lu +6
Autonomous driving requires planning under both semantic constraints and predictive dynamics. Existing end-to-end driving approaches, however, typically emphasize only one side of…
Closed Loop Dynamic Driving Data Mixture for Real-Synthetic Co-Training
Hongzhi Ruan, Pei Liu, Weiliang Ma +5
Data scaling is fundamental to modern deep learning, and grows increasingly critical as autonomous driving shifts to end-to-end learning. Real-world driving data is expensive to an…
Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images
Hongyuan Liu, Bochao Zou, Qiankun Liu +10
Creating realistic and simulation-ready 3D assets is crucial for autonomous driving research and virtual environment construction. However, existing 3D vehicle generation methods a…
StreetForward: Perceiving Dynamic Street with Feedforward Causal Attention
Zhongrui Yu, Zhao Wang, Yijia Xie +4
Feedforward reconstruction is crucial for autonomous driving applications, where rapid scene reconstruction enables efficient utilization of large-scale driving datasets in closed-…
DriveCombo: Benchmarking Compositional Traffic Rule Reasoning in Autonomous Driving
Enhui Ma, Jiahuan Zhang, Guantian Zheng +10
Multimodal Large Language Models (MLLMs) are rapidly becoming the intelligence brain of end-to-end autonomous driving systems. A key challenge is to assess whether MLLMs can truly…
InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields
Hao Yu, Haotong Lin, Jiawei Wang +7
Existing depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbitrary output resolut…
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…
LiSTAR: Ray-Centric World Models for 4D LiDAR Sequences in Autonomous Driving
Pei Liu, Songtao Wang, Lang Zhang +9
Synthesizing high-fidelity and controllable 4D LiDAR data is crucial for creating scalable simulation environments for autonomous driving. This task is inherently challenging due t…
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…
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…
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…
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…
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…
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…
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…
DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation
Guosheng Zhao, Chaojun Ni, Xiaofeng Wang +9
Closed-loop simulation is essential for advancing end-to-end autonomous driving systems. Contemporary sensor simulation methods, such as NeRF and 3DGS, rely predominantly on condit…