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视觉与机器人

自动驾驶

自动驾驶感知、规划、BEV、占用预测、激光雷达和仿真评测。

2026-03-26 至 2026-03-26 共收录 2 信号源:cs.RO, cs.CV, eess.IV, cs.AI

1. 端到端驾驶 2 篇

2603.24581 2026-03-26 cs.CV cs.RO 84%

Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving

潜在世界动作建模:端到端自动驾驶的潜在世界建模

Linbo Wang, Yupeng Zheng, Qiang Chen, Shiwei Li, Yichen Zhang, Zebin Xing, Qichao Zhang, Xiang Li, Deheng Qian, Pengxuan Yang, Yihang Dong, Ce Hao, Xiaoqing Ye, Junyu han, Yifeng Pan, Dongbin Zhao

机构 * Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所) Chongqing Chang’an Technology Co., Ltd(重庆长安科技有限公司) School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院) College of AI, Tsinghua University(清华大学人工智能学院) Zhongguancun Academy(中关村学院)

专题命中 端到端驾驶 :autonomous driving(title,abstract);trajectory planning(abstract);分类 cs.RO、cs.CV

AI总结 本文提出Latent-WAM框架,通过空间感知和动态感知的潜在世界表示实现高效端到端自动驾驶,实验显示在NAVSIM v2和HUGSIM上取得新的SOTA结果。

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2512.18128 2026-03-26 cs.CV 57%

SERA-H: Beyond Native Sentinel Spatial Limits for High-Resolution Canopy Height Mapping

SERA-H:超越原生Sentinel空间限制的高分辨率冠层高度制图

Thomas Boudras, Martin Schwartz, Rasmus Fensholt, Martin Brandt, Ibrahim Fayad, Jean-Pierre Wigneron, Gabriel Belouze, Fajwel Fogel, Philippe Ciais

机构 * organization= Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA, CNRS, UVSQ, Université Paris-Saclay , city= Gif-sur-Yvette , country= France organization= CNRS \& Département d'Informatique, École Normale Supérieure -- PSL , addressline= 45 Rue d'Ulm , postcode= 75005 , city= Paris , country= France organization= Department of Geography Geology, University of Copenhagen , addressline= Øster Voldgade 10 , city= Copenhagen , postcode= DK-1350 , country= Denmark organization= INRAE, Bordeaux Aquitaine Center , addressline= 71 avenue E. Bourlaux, CS 20032 , postcode= 33882 , city= Villenave d'Ornon , country= France organization= Department of Information Systems, University of M\"unster , city= M\"unster , country= Germany

专题命中 端到端驾驶 :LiDAR(abstract);分类 cs.CV

AI总结 SERA-H结合超分辨率模块和时序注意力编码,利用高密度LiDAR生成的冠层高度模型,从Sentinel-1和Sentinel-2时间序列数据中生成2.5米分辨率的高精度冠层高度制图,性能优于传统Sentinel-1/2方法,接近甚至优于商业高分辨率影像方法。

Comments 17 pages, 8 figures, 3 tables

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