面向自动驾驶的边缘计算技术研究综述

doi:10.11959/j.issn.1000-436x.2021045

Abstract

Abstract:

Edge computing plays an extremely important role in the environment perception and data processing of autonomous driving.Autonomous driving vehicles can expand their perception scope by obtaining environmental information from edge nodes, and can also deal with the problem of insufficient computing resources by offloading tasks to edge nodes.Compared with cloud computing, edge computing avoids high latency caused by long-distance data transmission, and provides autonomous driving vehicles with faster responses, and relieves the traffic load of the backbone network.Firstly, the edge computing-based cooperative perception and task offloading technologies for autonomous vehicles were introduced firstly, and related challenging issues were also proposed.Then the state-of-the-art of cooperative perception and task offloading technologies were analyzed and summarized.Finally, the problems need to be further studied in this field were discussed.

Key words: autonomous driving, edge computing, cooperative perception, task offloading

CLC Number:

TP393

Pin LYU, Jia XU, Taoshen LI, Wenbiao XU. Survey on edge computing technology for autonomous driving[J]. Journal on Communications, 2021, 42(3): 190-208.

Figures/Tables 10

算法类型	优化指标	文献	主要研究点
	时延	文献[74-77]	有执行顺序约束的部分卸载
		文献[78]	考虑多种因素找到任务卸载时延最短的节点
		文献[79]	将任务卸载系统分为前端和后端，采用不同的方式减少能耗
		文献[80]	根据边缘服务器的负载和预测成本决定卸载位置
		文献[81]	根据任务的特性，将整体卸载和部分卸载相结合
	能耗	文献[82]	保护车辆隐私的任务卸载
任务调度		文献[83]	基于不完全信道状态信息的卸载
		文献[84]	基于博弈论的任务卸载，每个车辆会考虑其他车辆的决策来制定自身的卸载决策
		文献[85]	在RSU之间调度任务，考虑RSU下行链路能耗
	负载平衡	文献[86]	采用V2X通信在车辆之间传递数据，找到目标RSU进行卸载
		文献[87]	联合优化节点选择方案和卸载比例
		文献[88]	在RSU之间替换等待队列中的任务，提高任务完成率
	任务完成率	文献[89]	根据任务优先级调度任务
		文献[90]	通过预测每个节点在时延约束下完成任务的概率来选择卸载位置
	时延	文献[91]	基于匹配理论分配V2I带宽
		文献[92]	采用随机公平分配算法分配通信资源
资源分配	能耗	文献[93]	考虑异构车辆的计算资源分配模型
		文献[94]	联合分配计算和通信资源
	系统吞吐量	文献[95]	通过信道复用来增加系统吞吐量
任务调度和资源分配联合优化	任务完成率	文献[96]	联合优化资源分配和任务调度，给出了基于拉格朗日松弛的有效解
任务调度和资源分配联合优化	用户体验质量	文献[97]	将任务调度和资源分配策略转化为联合优化问题，使用户体验质量的最大化
数据传输优化	能耗	文献[98]	自动驾驶汽车选择部分传感器数据以特定的频率发送
	网络负载	文献[99]	根据感知范围的相似性，减少上传到边缘服务器的图像数量
	数据传输成功率	文献[100]	根据信道状态在每个时隙中传输不同大小的数据

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	文献[19]	分析不同交通密度和不同渗透率下实现协同感知对V2V和V2I网络容量的需求
	文献[20]	对比ETSI制定的动态CPM生成策略和周期性生成策略的性能
	文献[21]	通过减少数据冗余优化ETSI制定的动态CPM生成策略
	文献[22]	预测信息对接收车辆的潜在价值，衡量网络情况决定是否发送该信息
CPM共享策略	文献[23]	优先广播重要地理位置的感知信息使车辆获得更大的感知范围
	文献[24]	提高大多数车辆感兴趣区域的感知信息的广播频率，提高感知范围，降低信息年龄
	文献[25]	利用RSU的感知信息为自动驾驶汽车提供危险警告和驾驶建议
	文献[26]	在先验环境下，利用RSU的感知信息扩大感知范围，减少车载传感器成本
	文献[27]	RSU将感知到的非自动驾驶汽车或行人信息发送给自动驾驶汽车，扩大车辆感知范围

数据融合方式	相关文献	关键研究点
	文献[29]	通过匹配图像中的特征点进行数据融合，用于有共同视野车辆之间的数据融合
图像融合	文献[30]	通过特征点匹配或坐标转换融合其他车辆生成的鸟瞰图
	文献[31]	融合多个RSU摄像头的数据来扩大协同感知系统的感知范围并提高感知精度
	文献[32]	将其他车辆的点云数据映射到主车辆的位置，然后合并、对齐从附近车辆收集的共享数据
点云融合	文献[33]	利用点匹配算法消除接收到的目标与本身所探测到的目标之间的偏差
	文献[34]	通过节点间激光雷达数据的融合来提升目标跟踪精度
	文献[35]	融合多个方位RSU的点云数据，提高目标检测精度
占用栅格地图融合	文献[36-37]	引入基于占用概率的目标函数来度量地图对齐的一致性程度，采用遗传算法对目标函数进行动态优化
	文献[38]	利用信任函数理论对车辆的相对位置和交换的传感器数据进行不确定性推理
	文献[28]	数据的时间对齐和空间对齐
对象匹配	文献[39]	主车辆接收其他车辆发送的对象列表，丢弃感兴趣范围外的对象，持续跟踪感兴趣范围内的对象
	文献[40]	用高斯混合概率假设密度协同融合方法进行不同车辆间的对象数据融合
	文献[41]	关联对象的轨迹，仅利用车辆的位置信息进行目标匹配

架构类型	解决的问题	文献	主要研究点
	车辆之间的合作方式	文献[49]	分布式任务卸载架构
车群架构		文献[50]	中心式的任务卸载架构，以更好地分配通信和计算资源
车群架构	车辆的移动性	文献[51]	考虑到车辆转弯行为的车辆间任务卸载
		文献[52]	预测任务完成时车辆的位置，提出2种不同的任务卸载方式
		文献[53]	将任务划分为多个部分，分配给多个车辆将要到达的RSU执行
	车辆的移动性	文献[54]	RSU根据车辆最大服务时间管理车群的任务卸载
		文献[55]	根据车辆移动过程中与RSU通信距离的变化选择最佳卸载时间点
		文献[56]	根据车辆的移动，在边缘服务器之间进行服务迁移
车-边架构		文献[57]	采用多路径传输建立车辆与基站之间的连接减少车辆与RSU之间建立连接的时延
	隔离任务执行环境	文献[58]	基于容器的任务卸载架构
		文献[59]	基于契约理论和匹配学习的任务卸载
	充分利用车群计算能力	文献[60]	将任务分为3个部分，分别由主车辆、其他车辆和RSU执行
		文献[61]	RSU根据自身计算能力、车群计算能力以及网络状况做出任务卸载决策
	利用移动设备的计算能力	文献[62]	使用乘客的移动设备进行任务卸载
车-边-云架构	使用SDN或Fi-Wi网络增强系统性能	文献[63,64,65,66]	使用SDN管理任务卸载，使用Fi-Wi网络增加数据传输速度
	实现车、边、云深度合作	文献[67]	云端进行全局协调，多个RSU形成边缘云，车辆之间共享计算任务。
	通用卸载架构	文献[68]	支持多种应用的任务卸载框架
	使用外部服务器控制车辆	文献[69,70,71]	使用云和边缘服务器控制车辆，优化云边控制比例

所面临的挑战	解决的主要问题	相关研究
	合理分配通信或计算资源	文献[83, 91-97]
资源有限	通过合理的任务调度提高系统吞吐量	文献[86-90]
	数据传输优化	文献[3, 98-100]
	车辆之间通信距离的快速变化	文献[49, 51, 54, 74-75]
车辆移动性	车辆频繁切换为其服务的RSU	文献[52-53, 55-57]
	寻找动态车辆网络数据传输路由	文献[82, 86]
任务执行环境的隔离性	基于容器的任务卸载	文献[58]

Survey on edge computing technology for autonomous driving

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