自动驾驶

Occupancy Grid Based Reactive Planner

Benjamin Hall, Andrew Goeden, Sahan Reddy, Timothy Gallion, Charles Koduru, M. Hassan Tanveer

专题命中 BEV与占用 ：occupancy(title,abstract)；LiDAR(abstract)；分类 cs.RO

Comments 5 pages

Surround-view Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-insensitive Multi-task Framework

Zizhang Wu, Yuanzhu Gan, Xianzhi Li, Yunzhe Wu, Xiaoquan Wang, Tianhao Xu, Fan Wang

专题命中 BEV与占用 ：BEV(title,abstract)；autonomous driving(abstract)；分类 cs.CV

Comments 12 pages, 11 figures

Journal ref IEEE Transactions on Intelligent Vehicles 2022

Data-Driven Occupancy Grid Mapping using Synthetic and Real-World Data

Raphael van Kempen, Bastian Lampe, Lennart Reiher, Timo Woopen, Till Beemelmanns, Lutz Eckstein

专题命中 BEV与占用 ：occupancy(title,abstract)；LiDAR(abstract)；分类 cs.RO

Comments Accepted to be published as part of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Maldives, November 16-18, 2022

Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?

Adam W. Harley, Zhaoyuan Fang, Jie Li, Rares Ambrus, Katerina Fragkiadaki

专题命中 BEV与占用 ：BEV(title,abstract)；LiDAR(abstract)；分类 cs.CV

Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer

Shaoyu Chen, Tianheng Cheng, Xinggang Wang, Wenming Meng, Qian Zhang, Wenyu Liu

专题命中 BEV与占用 ：BEV(title,abstract)；autonomous driving(abstract)；分类 cs.CV

Comments Tech report. Work in progress

Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks

Marcel Schreiber, Vasileios Belagiannis, Claudius Glaeser, Klaus Dietmayer

专题命中 BEV与占用 ：occupancy(title,abstract)；LiDAR(abstract)；分类 cs.RO

Journal ref 2020 IEEE International Conference on Robotics and Automation (ICRA), May 31 - June 4, 2020, Paris, France, pp. 8587-8593

Vision-based Autonomous Driving for Unstructured Environments Using Imitation Learning

Joonwoo Ahn, Minsoo Kim, Jaeheung Park

专题命中 BEV与占用 ：autonomous driving(title,abstract)；occupancy(abstract)；分类 cs.RO

Comments 16 pages

A Simulation-based End-to-End Learning Framework for Evidential Occupancy Grid Mapping

Raphael van Kempen, Bastian Lampe, Timo Woopen, Lutz Eckstein

专题命中 BEV与占用 ：occupancy(title,abstract)；LiDAR(abstract)；分类 cs.RO

Comments Accepted to be published as part of the 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan, July 11-15, 2021

3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds

Le Hui, Lingpeng Wang, Mingmei Cheng, Jin Xie, Jian Yang

专题命中 BEV与占用 ：BEV(title,abstract)；LiDAR(abstract)；分类 cs.CV

Comments Accepted by NeurIPS 2021

Learning Spatiotemporal Occupancy Grid Maps for Lifelong Navigation in Dynamic Scenes

Hugues Thomas, Matthieu Gallet de Saint Aurin, Jian Zhang, Timothy D. Barfoot

专题命中 BEV与占用 ：occupancy(title,abstract)；LiDAR(abstract)；分类 cs.RO

Learning Occupancy Priors of Human Motion from Semantic Maps of Urban Environments

Andrey Rudenko, Luigi Palmieri, Johannes Doellinger, Achim J. Lilienthal, Kai O. Arras

专题命中 BEV与占用 ：occupancy(title,abstract)；autonomous driving(abstract)；分类 cs.RO

Comments 8 pages, final pre-print version of the IEEE RA-L letter

Deep Inverse Sensor Models as Priors for evidential Occupancy Mapping

Daniel Bauer, Lars Kuhnert, Lutz Eckstein

专题命中 BEV与占用 ：occupancy(title,abstract)；autonomous driving(abstract)；分类 cs.RO

Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps

Nícolas Rosa, Vitor Guizilini, Valdir Grassi

专题命中 BEV与占用 ：occupancy(title,abstract)；LiDAR(abstract)；分类 cs.CV

Comments Accepted. (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environment

Vitor Guizilini, Ransalu Senanayake, Fabio Ramos

专题命中 BEV与占用 ：occupancy(title,abstract)；LiDAR(abstract)；分类 cs.RO

Comments International Conference on Robotics and Automation (ICRA), Montreal, 2019

The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition

Lingdong Kong, Shaoyuan Xie, Hanjiang Hu, Yaru Niu, Wei Tsang Ooi, Benoit R. Cottereau, Lai Xing Ng, Yuexin Ma, Wenwei Zhang, Liang Pan, Kai Chen, Ziwei Liu, Weichao Qiu, Wei Zhang, Xu Cao, Hao Lu, Ying-Cong Chen, Caixin Kang, Xinning Zhou, Chengyang Ying, Wentao Shang, Xingxing Wei, Yinpeng Dong, Bo Yang, Shengyin Jiang, Zeliang Ma, Dengyi Ji, Haiwen Li, Xingliang Huang, Yu Tian, Genghua Kou, Fan Jia, Yingfei Liu, Tiancai Wang, Ying Li, Xiaoshuai Hao, Yifan Yang, Hui Zhang, Mengchuan Wei, Yi Zhou, Haimei Zhao, Jing Zhang, Jinke Li, Xiao He, Xiaoqiang Cheng, Bingyang Zhang, Lirong Zhao, Dianlei Ding, Fangsheng Liu, Yixiang Yan, Hongming Wang, Nanfei Ye, Lun Luo, Yubo Tian, Yiwei Zuo, Zhe Cao, Yi Ren, Yunfan Li, Wenjie Liu, Xun Wu, Yifan Mao, Ming Li, Jian Liu, Jiayang Liu, Zihan Qin, Cunxi Chu, Jialei Xu, Wenbo Zhao, Junjun Jiang, Xianming Liu, Ziyan Wang, Chiwei Li, Shilong Li, Chendong Yuan, Songyue Yang, Wentao Liu, Peng Chen, Bin Zhou, Yubo Wang, Chi Zhang, Jianhang Sun, Hai Chen, Xiao Yang, Lizhong Wang, Dongyi Fu, Yongchun Lin, Huitong Yang, Haoang Li, Yadan Luo, Xianjing Cheng, Yong Xu

专题命中 BEV与占用 ：autonomous driving(abstract)；BEV(abstract)；occupancy(abstract)；driving perception(abstract)

Comments ICRA 2024; 32 pages, 24 figures, 5 tables; Code at https://robodrive-24.github.io/

O3N: Omnidirectional Open-Vocabulary Occupancy Prediction for Urban Autonomous Agents

O3N: 全向开放词汇占用预测

Mengfei Duan, Hao Shi, Fei Teng, Guoqiang Zhao, Yuheng Zhang, Zhiyong Li, Kailun Yang

机构 * Hunan University（湖南大学） ； Zhejiang University（浙江大学） 纠错

专题命中 BEV与占用 ：occupancy(title,abstract)；分类 cs.RO、cs.CV、eess.IV

AI总结 O3N通过全向感知和开放词汇方法，实现三维空间的连续表示和长距离上下文建模，提升具身智能在开放世界中的感知与建模能力。

Comments The source code will be made publicly available at https://github.com/MengfeiD/O3N

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise

我们能信任不可靠的体素吗？在标签噪声下的3D语义占用预测探索

Wenxin Li, Kunyu Peng, Di Wen, Junwei Zheng, Jiale Wei, Mengfei Duan, Yuheng Zhang, Rui Fan, Kailun Yang

机构 * School of Artificial Intelligence and Robotics（人工智能与机器人学院） ； National Engineering Research Center of Robot Visual Perception and Control Technology（机器人视觉感知与控制技术国家工程研究中心） ； Institute for Anthropomatics and Robotics（人机一体化与机器人研究所） ； Karlsruhe Institute of Technology（卡尔斯鲁厄技术大学） ； INSAIT ； College of Electronic and Information Engineering（电子与信息学院） ； Shanghai Institute of Intelligent Science and Technology（智能科学与技术上海研究院） ； Shanghai Research Institute for Intelligent Autonomous Systems（智能自主系统上海研究院） ； Shanghai Key Laboratory of Intelligent Autonomous Systems（智能自主系统上海重点实验室） ； State Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室） ； Frontiers Science Center for Intelligent Autonomous Systems（智能自主系统前沿科学中心） 纠错

专题命中 BEV与占用 ：occupancy(title,abstract)；分类 cs.RO、cs.CV、eess.IV

AI总结 本文提出DPR-Occ框架，通过双源部分标签推理解决3D语义占用预测中的标签噪声问题，实验表明其在高噪声环境下仍能保持性能。

Comments Accepted to IROS 2026. The benchmark and source code will be made publicly available at https://github.com/mylwx/OccNL

A BEV-Fusion Based Framework for Sequential Multi-Modal Beam Prediction in mmWave Systems

基于BEV融合的毫米波系统中顺序多模束预测框架

Jiaming Zeng, Cunhua Pan, Haoyang Weng, Ruijing Liu, Hong Ren, Jiangzhou Wang

专题命中 BEV与占用 ：BEV(title,abstract)；LiDAR(abstract)

AI总结 本文提出基于BEV融合的框架，通过统一相机、激光雷达、雷达和GPS模态，在共享鸟瞰图表示中实现空间一致的多模融合，提升毫米波系统中束预测的准确性。

Comments 13pages,7figures

Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic

用于道路交通中预测占用网格估计的机器学习架构

Parthasarathy Nadarajan, Michael Botsch, Sebastian Sardina

专题命中 BEV与占用 ：occupancy(title,abstract)；autonomous driving(abstract)

AI总结 本文提出了一种新颖的机器学习架构，用于高效估计道路交通中的预测占用网格，通过模拟验证其在准确性和计算时间上的性能。

Comments Journal of Advances in Information Technology

Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety Applications Based on Machine Learning

基于机器学习的车辆安全应用中预测占用网格的概率估计

Parthasarathy Nadarajan, Michael Botsch

机构 * Technische Hochschule Ingolstadt（图宾根工业大学） 纠错

专题命中 BEV与占用 ：occupancy(title,abstract)；trajectory planning(abstract)

AI总结 本文提出基于随机森林算法的机器学习方法，用于高效计算车辆安全应用中的预测占用网格，以提升关键性估计和轨迹规划能力。

Comments 2016 IEEE Intelligent Vehicles Symposium

Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps

Rabbia Asghar, Manuel Diaz-Zapata, Lukas Rummelhard, Anne Spalanzani, Christian Laugier

专题命中 BEV与占用 ：occupancy(title,abstract)；分类 cs.RO、cs.CV、cs.AI

Comments Accepted to the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)

OVO: Open-Vocabulary Occupancy

Zhiyu Tan, Zichao Dong, Cheng Zhang, Weikun Zhang, Hang Ji, Hao Li

专题命中 BEV与占用 ：occupancy(title,abstract)；分类 cs.RO、cs.CV、cs.AI

CMRNet: Camera to LiDAR-Map Registration

Daniele Cattaneo, Matteo Vaghi, Augusto Luis Ballardini, Simone Fontana, Domenico Giorgio Sorrenti, Wolfram Burgard

专题命中 BEV与占用 ：LiDAR(title,abstract)；分类 cs.RO、cs.CV、eess.IV

Comments Accepted for presentation at IEEE ITSC2019

Journal ref 2019 IEEE Intelligent Transportation Systems Conference (ITSC) pp. 1283-1289

Generative Occupancy Fields for 3D Surface-Aware Image Synthesis

Xudong Xu, Xingang Pan, Dahua Lin, Bo Dai

专题命中 BEV与占用 ：occupancy(title,abstract)；分类 cs.CV、cs.AI

Comments Accepted to NeurIPS2021. We propose Generative Occupancy Fields(GOF), a 3D-aware generative model which could synthesize realistic images with 3D consistency and simultaneously learn compact object surfaces

Efficient Dense Frontier Detection for 2D Graph SLAM Based on Occupancy Grid Submaps

Juraj Oršulić, Damjan Miklić, Zdenko Kovačić

专题命中 BEV与占用 ：occupancy(title,abstract)；分类 cs.RO、eess.IV

Journal ref J. Orsulic, D. Miklic and Z. Kovacic, "Efficient Dense Frontier Detection for 2D Graph SLAM Based on Occupancy Grid Submaps," in IEEE Robotics and Automation Letters, 2019

MVDGC: Joint 3D and 2D Multi-view Pedestrian Detection via Dual Geometric Constraints

MVDGC: 通过双重几何约束的联合3D和2D多视角行人检测

Thinh Phan, Hao Vo, Khoa Vo, Thanh Ngo, Cuong Pham, Ngan Le

机构 * University of Arkansas（阿肯色大学） ； University of Information Technology（信息技术大学） ； Posts and Telecommunications Institute of Technology (PTIT)（邮电技术学院） 纠错

专题命中 BEV与占用 ：BEV(summary_cn,abstract)；分类 cs.CV

AI总结 提出MVDGC框架，利用3D圆柱查询联合优化BEV平面定位和2D边界框，通过双重几何约束解决透视变换导致的特征畸变和密集区域定位模糊问题。

BridgeTA: Bridging the Representation Gap in Knowledge Distillation via Teacher Assistant for Bird's Eye View Map Segmentation

BridgeTA: 通过教师助手弥合知识蒸馏中表示差距的鸟瞰图分割

Beomjun Kim, Suhan Woo, Sejong Heo, Euntai Kim

机构 * Yonsei University（延世大学） ； Hyundai Motor Company（现代汽车公司） ； Korea Institute of Science and Technology（韩国科学技术院） 纠错

专题命中 BEV与占用 ：BEV(abstract,abstract_cn)；autonomous driving(abstract)；LiDAR(abstract)；分类 cs.CV

AI总结 提出BridgeTA框架，利用教师助手网络在保持学生模型架构和推理成本不变的情况下，弥合激光雷达-相机融合与纯相机模型之间的表示差距，并通过Young不等式推导蒸馏损失实现稳定优化，在nuScenes数据集上mIoU提升4.2%。

Comments Accepted at ICRA 2026 (8 pages, 6 figures)

Rapid Forest Fuel Load Estimation via Virtual Remote Sensing and Metric-Scale Feed-Forward 3D Reconstruction

通过虚拟遥感和度量尺度前馈3D重建快速估算森林燃料负荷

Quanyun Wu, Kyle Gao, Wentao Sun, Zhengsen Xu, Hudson Sun, Linlin Xu, Yuhao Chen, David A. Clausi, Jonathan Li

机构 * University of Waterloo（滑铁卢大学） ； University of Calgary（卡尔加里大学） 纠错

专题命中 BEV与占用 ：BEV(abstract,abstract_cn)；LiDAR(abstract,abstract_cn)；分类 cs.CV

AI总结 本文提出一种自动化 pipeline，利用 Google Earth Studio 生成低空轨道影像和相机姿态，结合 Pi-Long 模型进行密集3D重建，并通过度量恢复模块和 watershed 分割算法估算森林燃料负荷。

Comments Accepted for publication at IEEE IGARSS 2026

SpatialGrammar: A Domain-Specific Language for LLM-Based 3D Indoor Scene Generation

SpatialGrammar: 一种面向领域的语言用于基于LLM的3D室内场景生成

Song Tang, Kaiyong Zhao, Yuliang Li, Qingsong Yan, Penglei Sun, Junyi Zou, Qiang Wang, Xiaowen Chu

机构 * The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）） ； Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）） 纠错

专题命中 BEV与占用 ：BEV(summary_cn,abstract)；分类 cs.AI

AI总结 本文提出SpatialGrammar，一种领域特定语言，通过BEV网格放置确定性编译生成有效3D几何，提升空间准确性和物理合理性。SG-Agent通过编译器反馈迭代优化场景，SG-Mini在单次生成场景中表现优异。

BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chonghao Sima, Tong Lu, Qiao Yu, Jifeng Dai

专题命中 BEV与占用 ：autonomous driving(abstract)；BEV(abstract)；LiDAR(abstract)；driving perception(abstract)

Comments Accepted to ECCV 2022