Vehicle-road Collaborative Perception Technology and Development Trend for Intelligent Connected Vehicle
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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, 近年来涌现了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、感知信息选择与压缩等问题相关研究；最后对车路协同感知的大规模数据集进行了整理, 并对智能网联汽车协同感知的发展趋势进行了分析.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, and 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, perception information selection and compression 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.
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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 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 TiMACs 31.34 G[14] on V2Xset 
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表 3 车路协同感知方法汇总表
Table 3 Summary table of vehicle-road cooperative 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 PF SC SP 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] 2021 通信 √ 低带宽需求, 强化学习 — — RB 分配, CPM 内容选择 MMW-RCSF[49] 2021 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] 2022 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] 2022 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] 2022 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] 2022 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] 2023 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] 2023 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, BS 拥塞网络的优化方法 V2X — RB 分配 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 dataset
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 清华大学智能产业研究院&北京市高级别自动驾驶示范区 城市道路、高速公路(包含多种天气场景) 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] 2021 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] 2023 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 (UCLA mobility lab) 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 33 081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、T型路口、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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