arXiv Computer Vision By Fengcheng Yu, Dhruv Parikh, Junjie Ye, Maulik Bhatt, Thang Vu, Igor Vasiljevic, Vitor Guizilini, Yue Wang

DriveReferee: Geometric Safety Verdicts Need Not Be Learned for Driving World-Action Models

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Hugging Face Trending Papers
Sep 3

SV-WAM: An Efficient Surround-View World-Action Model for End-to-End Autonomous Driving

SV-WAM is a surround‑view world‑action model that keeps all six camera views for autonomous driving while enabling efficient inference by discarding the video branch during deployment. It uses future‑video prediction as dense training supervision and introduces an action‑centered causal mask to prevent future‑video tokens from influencing action tokens during joint denoising. A differentiable drivable‑area compliance regularizer further improves safety by penalizing vehicle‑footprint corners that approach or cross drivable boundaries. Experiments on NAVSIMv2 and nuScenes show state‑of‑the‑art planning performance with low latency and strong zero‑shot transfer.

arXiv Computer Vision
Sep 4

SV-WAM: An Efficient Surround-View World-Action Model for End-to-End Autonomous Driving

SV-WAM is a surround‑view world‑action model that keeps all six camera views while enabling efficient inference by discarding the video branch at deployment. It uses future‑video prediction as dense training supervision and an action‑centered causal mask to prevent action tokens from attending to future‑video tokens during joint denoising. A differentiable drivable‑area compliance regularizer penalizes vehicle‑footprint corners near or crossing drivable boundaries, improving safety and boundary awareness. Experiments on NAVSIMv2 and nuScenes show state‑of‑the‑art planning performance with low latency and strong zero‑shot transfer.

By Jinyang Wang, Shiwei Li, Junjian Wang, Zhiqiang Deng, Jianbin Gao, Yihang Zhao, Liu Liu, Yongjia Zhao, Jinlong Chen, Huirui Xu, Yifeng Pan, Kangwei Liu, Fan Ren, Ji Tao, Minghao Yang
arXiv Computer Vision
1d ago

RoXDrive: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving via Action-Faithful Rollouts

arXiv:2609.36851v1 Announce Type: new Abstract: End-to-end autonomous driving policies are commonly trained via imitation learning on logged demonstrations without observing the consequences of their...

By Hongbin Lin, Chaoda Zheng, Yiming Yang, Xiangyu Li, Shijia Chen, Jinhao Deng, Kangjie Chen, Dongbin Zhang, Jie Feng, Yu Zhang, Xianming Liu, Shuguang Cui, Boyang Wang, Zhen Li
arXiv AI
Jun 3

NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation

arXiv:2606. 03159v1 Announce Type: cross Abstract: As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck.

By NVIDIA, :, Aarti Basant, Amlan Kar, Despoina Paschalidou, Fangyin Wei, Francesco Ferroni, Guillermo Garcia Cobo, Haithem Turki, Huan Ling, Jaewoo Seo, James Lucas, Jay Zhangjie Wu, Jialiang Wang, Jonathan Lorraine, Jun Gao, Kai He, Katarina Tothova, Kevin Xie, Micha{\l} Tyszkiewicz, Qi Wu, Riccardo de Lutio, Ruilong Li, Sanja Fidler, Seung Wook Kim, Tianchang Shen, Tianshi Cao, Tobias Pfaff, William Lew, Xindi Wu, Xuanchi Ren, Yifan Lu, Yuxuan Zhang, Zan Gojcic, Zian Wang
arXiv AI
Aug 12

Threat-guided Policy-aware Scene Perturbation for Safe Autonomous Driving with Online Reinforcement Learning

arXiv:2608. 10403v1 Announce Type: new Abstract: Reinforcement learning (RL) has shown promising performance in autonomous driving, yet ensuring the safety of online RL policies remains challenging due to insufficient exposure to safety-critical driving scenes.

By Xincong Hu (Nanjing University), Lei Ou (Nanjing University), Maosen Li (Yinwang Intelligent Technology Co., Ltd), Jingtao Zhang (Yinwang Intelligent Technology Co., Ltd), Liguo Hou (Yinwang Intelligent Technology Co., Ltd), Zongzhang Zhang (Nanjing University)