面向智能网联汽车的车路协同感知技术及发展趋势

张新钰; 卢毅果; 高鑫; 黄雨宁; 刘华平; 王云鹏; 李骏

doi:10.16383/j.aas.c230575

面向智能网联汽车的车路协同感知技术及发展趋势

doi: 10.16383/j.aas.c230575 cstr: 32138.14.j.aas.c230575

张新钰^{1, 2,},
卢毅果^{1, 2, 3,},
高鑫^{1, 2, 4,},
黄雨宁^{1, 2, 3,},
刘华平^5,,
王云鹏⁶,
李骏^{1, 2,}

1.
清华大学汽车安全与节能国家重点实验室北京 100084
2.
清华大学车辆与运载学院北京 100084
3.
新疆大学软件学院乌鲁木齐 830000
4.
中国矿业大学 (北京)人工智能学院北京 100083
5.
清华大学计算机科学与技术系北京 100084
6.
北京航空航天大学交通科学与工程学院北京 100083

基金项目: 国家重点研发计划 (2018YFE0204300), 国家自然科学基金 (62273198, U1964203) 资助

详细信息

作者简介:
张新钰：清华大学车辆与运载学院高级工程师. 主要研究方向为智能驾驶和多模态信息融合. E-mail: xyzhang@tsinghua.edu.cn

卢毅果：新疆大学软件学院硕士研究生. 主要研究方向为计算机视觉和语义分割. E-mail: yiguolu@stu.xju.edu.cn

高鑫：中国矿业大学 (北京) 人工智能学院博士研究生. 主要研究方向为模式识别, 多模态融合和图像处理. 本文通信作者.E-mail: bqt2000405024@student.cumtb.edu.cn

黄雨宁：新疆大学软件学院硕士研究生. 主要研究方向为目标检测及其在计算机视觉中的应用. E-mail: 107552204759@stu.xju.edu.cn

刘华平：清华大学计算机科学与技术系教授. 主要研究方向为智能机器人感知, 智能机器人学习与控制. E-mail: hpliu@tsinghua.edu.cn

王云鹏：中国工程院院士, 北京航空航天大学交通科学与工程学院教授. 主要研究方向为协同车辆基础设施系统和智能交通控制

李骏：中国工程院院士, 清华大学车辆与运载学院教授. 主要研究方向为智能网联汽车, 自动驾驶, 发动机结构设计和智能化参数设计. E-mail: junliqh@163.com

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出版历程
- 收稿日期: 2023-09-14
- 录用日期: 2024-03-29
- 网络出版日期: 2024-06-03

Vehicle-road Collaborative Perception Technology and Development Trend for Intelligent Connected Vehicles

ZHANG Xin-Yu^{1, 2
,},
LU Yi-Guo^{1, 2, 3
,},
GAO Xin^{1, 2, 4
,},
HUANG Yu-Ning^{1, 2, 3
,},
LIU Hua-Ping^5
,,
WANG Yun-Peng⁶,
LI Jun^{1, 2
,}

1.
State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084
2.
School of Vehicle and Mobility, Tsinghua University, Beijing 100084
3.
School of Software, Xinjiang University, Urumqi 830000
4.
School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083
5.
Department of Computer Science and Technology, Tsinghua University, Beijing 100084
6.
School of Transportation Science and Engineering, Beihang University, Beijing 100083

Funds: Supported by National Key Research and Development Program of China (2018YFE0204300) and National Natural Science Foundation of China (62273198, U1964203)

More Information

Author Bio:
ZHANG Xin-Yu　Senior engineer at the School of Vehicle and Mobility, Tsinghua University. His research interest covers intelligent driving and multimodal information fusion

LU Yi-Guo　Master student at School of Software, Xinjiang University. His research interest covers computer vision and semantic segmentation

GAO Xin　Ph.D. candidate at School of Artificial Intelligence, China Universityof Mining and Technology-Beijing. His research interest covers pattern recognition, multimodal fusion and image processing. Corresponding author of this paper

HUANG Yu-Ning　Master student at School of Software, Xinjiang University. Her research interest covers object detection and its applications in computer vision

LIU Hua-Ping　Professor at the Department of Computer Science and Technology, Tsinghua University. His research interest coversintelligent robot perception, intelligent robot learning and control

WANG Yun-Peng　Academician of Chinese Academy of Engineering, professor at the School of Transportation Science and Engineering, Beihang University. His research interests covers cooperative vehicle infrastructure systems and intelligent transportation control

LI Jun　 Academician of Chinese Academy of Engineering, professor at the School of Vehicle and Mobility, Tsinghua University. His research interest covers intelligent connected vehicles, autonomous driving, engine structure design and intelligent parameter design

摘要

摘要: 随着感知技术的不断发展以及智能交通基础设施的完善, 智能网联汽车应用在自动驾驶领域的地位逐渐提升. 自动驾驶感知从单车智能向车路协同迈进, 近年来涌现出一批新的协同感知技术与方法. 本文旨在全面阐述面向智能网联汽车的车路协同感知技术, 并总结相关可利用数据及该方向的发展趋势. 首先对智能网联汽车的协同感知策略进行划分, 并总结了不同感知策略具备的优势与不足; 其次, 对智能网联汽车协同感知的关键技术进行阐述, 包括车路协同感知过程中的感知技术与通信技术; 然后对车路协同感知方法进行归纳, 总结了近年来解决协同感知中感知融合(Perception fusion, PF)、感知信息选择与压缩(Perception selection and compression, SC)等问题的相关研究; 最后对车路协同感知的大规模数据集进行整理, 并对智能网联汽车协同感知的发展趋势进行分析.
- 智能网联汽车 /
- 车路协同 /
- 协同感知 /
- 安全通信 /
- 自动驾驶
Abstract: With the continuous development of perception technology and the improvement of intelligent transportation infrastructure, the status of intelligent connected vehicle applications in the field of autonomous driving has been gradually improved. Autonomous driving perception has progressed from single-vehicle intelligence to vehicle-road collaboration, and several new collaborative perception technologies and methods have emerged in recent years. The purpose of this paper is to comprehensively describe the vehicle-road collaborative perception technology for intelligent connected vehicles, and summarize the relevant available data and the development trend in this direction. Firstly, the collaborative perception strategies for intelligent connected vehicles are divided, and the advantages and shortcomings of different perception strategies are summarized; Secondly, the key technologies of collaborative perception for intelligent connected vehicles are elaborated, including the perception technology and communication technology in the process of vehicle-road collaborative perception; Then the vehicle-road collaborative perception methods are summarized, and the research related to solving the problems of perception fusion (PF), perception selection and compression (SC) in collaborative perception in recent years are summarized; Finally, the large-scale dataset of vehicle-road collaborative perception is organized, and the development trend of collaborative perception of intelligent connected vehicles is analyzed.
- Intelligent connected vehicles /
- vehicle-road collaboration /
- collaborative perception /
- secure communication /
- autonomous driving

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图 1 车路协同示意图

Fig. 1 Schematic diagram of vehicle-road collaboration

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图 2 协同感知策略对比图

Fig. 2 Comparison chart of collaborative perception strategies

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图 3 基于点云数据的协同感知方法

Fig. 3 Collaborative perception method based on point cloud data

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图 4 基于相机图像的协同感知方法

Fig. 4 Collaborative perception method based on camera image

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图 5 通信交互示意图

Fig. 5 Schematic diagram of communication interactions

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图 6 感知信息选择与压缩

Fig. 6 Perceptual selection and compression

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图 7 协同感知中的安全性问题

Fig. 7 Security issues in collaborative perception

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表 1 不同协同策略传输性能分析

Table 1 Analysis on transmission performance of different collaborative strategies

策略	指标
策略	带宽 (传输速率) 需求	精度/AP@50		算力评估
早期协同	20 Mbps ~ 60 Mbps^{[19, 28]}	60.8%^[14]		FPS	2.63 ~ 3.45^[19]
				GPU	Nvidia Quadro M4000
				MACs	31.45 G^[14] on V2Xset
中期协同	10 Mbps ~ 20 Mbps^[20]	V2VNet^[22]	57.8%^[14]	FPS	17.54 ~ 35.71^[16]
		V2X-ViT^[16]	58.3%^[14]	GPU	Tesla V100
		Where2comm^[29]	59.1%^[14]	MACs	60 ~ 200 G^[14] on V2Xset
后期协同	3 Mbps ~ 5 Mbps^[15]	56.8%^[14]		FPS	2.56 ~ 3.23^[20]
				GPU	GeForce GTX 1080 Ti
				MACs	31.34 G^[14] on V2Xset

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表 2 智能网联汽车所具备的通信带宽

Table 2 Communication bandwidth of intelligent connected vehicles

通信方式	性能
通信方式	车载通信传输带宽 (速率)	通信延迟
Wi-Fi	6 Mbps ~ 54 Mbps^[54]	—
DSRC	3 Mbps ~ 27 Mbps^[54−55]	< 5 ms^[55]
5G	290 Mbps ~ 350 Mbps^[56]	6 ms ~ 13 ms^[56]

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表 3 车路协同感知方法汇总表

Table 3 Summary table of vehicle-road collaboration perception methods

方法	年份	感知/ 通信	方法类型			方法特点	协同对象	图像/点云/ 融合	任务
方法	年份	感知/ 通信	PF	SC	SP	方法特点	协同对象	图像/点云/ 融合	任务
Cooper^[17]	2019	感知	√			稀疏点云检测	V2V	点云	检测
Who2com^[85]	2020	通信		√		低带宽需求, 无监督学习	—	—	通信任务
When2com^[86]	2020	通信		√		动态减少带宽需求, 无监督学习	—	—	通信任务
FRLCP^[87]	2022	通信		√		低带宽需求, 强化学习	—	—	感知信息选择
MMW-RCSF^[49]	2022	通信	√			传感器融合, 时空同步	—	—	标定任务
FPV-RCNN^[24]	2022	感知	√			损失优化, 基于关键点	V2V	传感器融合	检测
Coopernaut^[59]	2022	感知	√			端到端框架	V2V	点云	控制决策
CoBEVT^[41]	2022	感知	√			注意力机制	V2V	图像	BEV分割
V2XP-ASG^[81]	2023	感知			√	场景生成, 对抗攻击	V2X	点云	检测
V2X-ViT^[16]	2022	感知	√			位姿误差, 注意力机制, 自适应信息融合, 多尺度	V2X	点云	检测
MMVR^[52]	2022	感知	√			多尺度, 图神经网络, 注意力机制	V2X	传感器融合	检测
DAIR-V2X^[15]	2022	感知	√			时间补偿延迟融合, 时间异步鲁棒性	V2X	点云图像	检测
CO^3^[35]	2023	感知	√			无监督学习	V2X	点云	检测
RCP-MSF^[53]	2022	感知	√			鲁棒性增强, 低成本点云处理	V2X	传感器融合	检测
3D-Harmonic-Loss^[88]	2023	感知				损失函数优化, 点云稀疏检测	V2X	点云	检测
Where2comm^[29]	2022	通信		√		图神经网络, 低带宽需求, 特征压缩	—	点云、图像	检测
PCP6G^[89]	2022	通信		√		新的数据传输类型, 特征压缩	—	点云	检测
H2-FED^[90]	2022	通信			√	连接中断鲁棒性, 隐私保护计算, 联邦学习	V2X	—	通信任务
CoPEM^[91]	2022	通信			√	感知错误建模	V2X	—	—
CAP-V2V^[92]	2022	通信	√			多车协同路径规划	V2V	点云	路径规划
ERCP^[58]	2023	通信			√	位姿误差鲁棒性, 基于迭代最近点, 基于最佳传输	V2V	—	—
PCG-SF^[93]	2022	通信	√			参数化协方差, 定位误差鲁棒性, 传感器融合	—	—	定位任务
VIMI^[43]	2023	感知	√	√		多尺度, 注意力机制, 特征压缩	V2I	图像	检测
FFNet^[37]	2023	感知			√	特征流预测, 延迟, 自监督学习	V2I	点云	检测
VICOD^[50]	2022	感知	√			低延迟感知, 减少通信成本	V2I	传感器融合	检测
LCCP^[57]	2023	感知			√	注意力机制, 不确定性感知, 有损通信下感知	V2V	点云	检测
UMC^[94]	2023	感知	√	√		多尺度, 图神经网络, 新的协同感知评价指标	V2X	点云	检测
DeepAccident^[95]	2024	感知				Transformer 架构, 端到端框架	V2X	图像	事故预测
CoCa3D^[42]	2023	感知	√			仅相机协作	V2X	图像	检测
GevBEV^[96]	2023	感知			√	不确定性感知, 空间高斯	—	点云	BEV分割
CCPAV^[66]	2023	通信		√		新的评分函数, 基站拥塞网络的优化方法	V2X	—	感知信息选择
SDVN-V2X^[97]	2023	通信				路侧设备中心化	V2X	—	通信任务
Among Us^[80]	2023	通信			√	对抗攻击抵御	—	点云	检测

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表 4 车路协同感知数据集汇总表

Table 4 Summary of vehicle-road collaboration perception datasets

数据集	年份	制作单位	场景	传感器	支持任务	数据量
DAIR-V2X^[15]	2022	清华大学人工智能产业研究院、百度公司、清华大学计算机科学与技术系和中国科学院大学	城市道路、高速公路(包含多种天气场景)、十字路口	相机、雷达	检测、跟踪	71 254帧
V2X-Sim^[104]	2022	纽约大学AI4CE实验室 & 上海交通大学MediaBrain团队	交叉路口	相机、雷达	检测、跟踪、分割	47 200帧
CoopInfo^[19]	2022	英国华威大学华威制造集团智能汽车小组	T 型路口	相机	检测	20 000帧
CODD^[32]	2022	英国华威大学华威制造集团智能汽车小组	路口场景、环岛场景	雷达	检测、跟踪	5 000帧
IPS300+^[101]	2022	清华大学 & 北京万集科技	交叉路口	相机、雷达	检测、跟踪	14 198帧
OPV2V^[23]	2022	加州大学洛杉矶分校移动实验室	T 型路口、交叉路口	相机、雷达	检测、跟踪、分割	11 464帧
V2XSet^[16]	2022	加州大学洛杉矶分校 & 德克萨斯大学奥斯汀分校 & 谷歌研究院 & 加州大学默塞德分校	十字路口、街区中段和入口坡道	雷达	检测	11 447帧
DOLPHINS^[105]	2022	清华大学电子工程系 & 北京交通大学电子信息工程学院	十字路口、丁字路口、陡坡道、高速公路入口匝道和山路(包含多种天气场景)	相机、雷达	检测、跟踪	42 376帧
V2X-Seq^[103]	2023	清华大学智能产业研究院 & 百度公司	城市道路、十字路口	相机、雷达	跟踪、轨迹预测	225 000帧
V2V4Real^[102]	2023	加州大学洛杉矶分校	高速公路、城市道路	相机、雷达	检测、跟踪、域自适应	60 000帧

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