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20242026
most citedGenAD: Generative End-to-End Autonomous Driving

3 citations · 3 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

PhysFlow: Physics-Aware Optical Flow for Motion Controllable Video Generation

Cong Wang, Hanxin Zhu, Yonglin Tian +5

Video generation models have recently attracted substantial attention for their ability to generate visually compelling videos, yet ensuring physically consistent and plausible dyn…

cs.CV2026

OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model

Junjie Cheng, Ruiqi Song, Ye Wu +3

Autonomous driving systems are steadily moving toward end-to-end paradigms to mitigate the limited adaptability of rule-based pipelines in complex traffic environments. However, mo…

cs.CV2026

UnsOcc: 3D Semantic Occupancy Prediction in Unstructured Scene via Rendering Fusion

Ye Wu, Ruiqi Song, Baiyong Ding +3

Unstructured scenes present unique challenges for autonomous driving, as irregular obstacles and sparse scene layouts undermine the effectiveness of traditional perception methods…

cs.CV2025

DriveSplat: Unified Neural Gaussian Reconstruction for Dynamic Driving Scenes

Cong Wang, Ruiqi Song, Wei Tian +3

Reconstructing large-scale dynamic driving scenes remains challenging due to the coexistence of static environments with extreme depth variation and diverse dynamic actors exhibiti…

cs.CV2025

InsightDrive: Insight Scene Representation for End-to-End Autonomous Driving

Ruiqi Song, Xianda Guo, Yanlun Peng +3

Conventional end-to-end autonomous driving methods often rely on explicit global scene representations, which typically consist of 3D object detection, online mapping, and motion p…

cs.CV2024★ 3 cited

GenAD: Generative End-to-End Autonomous Driving

Wenzhao Zheng, Ruiqi Song, Xianda Guo +2

Directly producing planning results from raw sensors has been a long-desired solution for autonomous driving and has attracted increasing attention recently. Most existing end-to-e…