[1]宫彦,王乃棒,张新钰,等.面向智能网联汽车的 BEV 感知技术与发展趋势[J].智能系统学报,2026,21(1):41-59.[doi:10.11992/tis.202505027]
　GONG Yan,WANG Naibang,ZHANG Xinyu,et al.BEV perception technologies and development trends for intelligent connected vehicles[J].CAAI Transactions on Intelligent Systems,2026,21(1):41-59.[doi:10.11992/tis.202505027]
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面向智能网联汽车的 BEV 感知技术与发展趋势

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 21 期数: 2026年第1期 页码: 41-59 栏目: 综述 出版日期: 2026-03-05
Title:
BEV perception technologies and development trends for intelligent connected vehicles
作者:
宫彦1,2,3, 王乃棒1,2, 张新钰1,2, 苏纳宇1,2,4, 赵红飞1,2, 袁云1,2, 鲁建丽1,2, 胡小溪1,2, 刘华平5
1. 清华大学 智能绿色车辆与交通全国重点实验室, 北京 100084;
2. 清华大学 车辆与运载学院, 北京 100084;
3. 哈尔滨工业大学 机器人技术与系统国家重点实验室, 黑龙江 哈尔滨 150001;
4. 燕山大学 电气工程学院, 河北 秦皇岛 066004;
5. 清华大学 计算机科学与技术系, 北京 100084
Author(s):
GONG Yan1,2,3, WANG Naibang1,2, ZHANG Xinyu1,2, SU Nayu1,2,4, ZHAO Hongfei1,2, YUAN Yun1,2, LU Jianli1,2, HU Xiaoxi1,2, LIU Huaping5
1. State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China;
2. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China;
3. the State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China;
4. Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
5. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
关键词:
智能网联汽车; 车路协同; 协同感知; 鸟瞰视图; 自动驾驶; 数据集; 车联万物; 多模态融合
Keywords:
intelligent connected vehicles; vehicle-infrastructure cooperation; cooperative perception; BEV; autonomous driving; dataset; vehicle-to-everything (V2X); multimodal fusion
分类号:
TP391.41；U463.6；U495
DOI:
10.11992/tis.202505027
摘要:
鸟瞰视图(bird’s eye view, BEV)感知因其统一且可解释的空间表达能力，已成为自动驾驶环境理解的核心技术。本文旨在全面阐述面向智能网联汽车的 BEV 感知技术，总结相关公开数据集，探讨相关挑战及发展趋势，为该领域提供系统的理论支持与实践指导。本文系统梳理了 BEV 感知技术在自动驾驶中的研究进展，围绕路端及车路协同应用场景，构建了涵盖纯视觉、纯点云与多模态融合的技术框架，深入分析了代表性方法的核心思想与实现机制。本文首次在数据层面进行系统整理，并比较了现有 BEV 感知相关的数据集，包括规模、传感器配置与标注类型。本文聚焦 BEV 感知在开放类别识别、大规模无监督数据利用、传感器不确定性等关键挑战，并探讨其与端到端自动驾驶、具身智能、大模型协同感知架构的融合趋势。
Abstract:
Bird’s eye view (BEV) perception has become a fundamental technique for environmental understanding in autonomous driving, due to its unified and interpretable spatial representation. This survey provides a comprehensive review of BEV perception technologies tailored for intelligent connected vehicles. It systematically categorizes existing approaches based on sensor modality and deployment configuration, covering vehicle-side, infrastructure-side, and vehicle-infrastructure cooperative scenarios. The review introduces a multi-dimensional framework encompassing vision-only, LiDAR-only, and multimodal fusion methods, and analyzes representative techniques in terms of their design principles and implementation strategies. In addition, this work presents the first consolidated comparison of BEV-related datasets, detailing their sensor setups, task types, and annotation schemes to support standardized benchmarking. Finally, the survey outlines key challenges—such as open-category recognition, unsupervised learning from large-scale data, and robustness under sensor uncertainty—and explores future directions involving end-to-end autonomous driving, embodied intelligence, and large-model-based cooperative BEV perception systems.
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备注/Memo

收稿日期:2025-5-27。
基金项目:国家自然科学基金项目(62273198)；北京市自然科学基金项目(L241017).
作者简介:宫彦，副研究员，博士，主要研究方向为计算机视觉、自动驾驶和多模态信息融合。获国家发明专利授权6项，发表学术论文20余篇。E-mail：gongyan2020@foxmail.com。;张新钰，副研究员，清华猛狮智能车团队负责人，主要研究方向为智能驾驶和多模态信息融合。担任国家重点研发计划项目负责人。多次在国内无人驾驶顶级赛事获得冠亚军，获2019年吴文俊人工智能科技进步二等奖。发表学术论文100余篇，入选ESI(Essential Science Indicators)高被引论文1篇。E-mail：xyzhang@tsinghua.edu.cn。;刘华平，教授，博士生导师，中国人工智能学会理事、中国人工智能学会认知系统与信息处理专业委员会副主任，主要研究方向为具身感知与学习，获吴文俊人工智能科学技术奖。主持国家自然科学基金重点项目2项，发表学术论文100余篇。E-mail： hpliu@tsinghua.edu.cn。
通讯作者:张新钰. E-mail：xyzhang@tsinghua.edu.cn

更新日期/Last Update: 2026-01-05
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