自動駕駛車聯網中通感算融合研究綜述與展望

馬忠貴; 李卓; 梁彥鵬

doi:10.13374/j.issn2095-9389.2022.04.16.003

自動駕駛車聯網中通感算融合研究綜述與展望

doi: 10.13374/j.issn2095-9389.2022.04.16.003

北京科技大學計算機與通信工程學院，北京 100083

基金項目: 中央高校基本科研業務費專項資金資助項目（FRF-DF-20-12, FRF-GF-18-017B）

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E-mail: m18715964030@163.com

中圖分類號: U463.67
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出版歷程
- 收稿日期: 2022-04-16
- 網絡出版日期: 2022-08-08
- 刊出日期: 2023-01-01

Overview and prospect of communication-sensing-computing integration for autonomous driving in the internet of vehicles

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

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Corresponding author: E-mail: m18715964030@163.com

摘要

摘要: 為了應對自動駕駛車聯網極低的通信時延、極高的可靠性、更高的傳輸速率等極致性能需求，亟需破解現有車聯網中通信、感知、計算相互割裂與獨立分治的問題，實現“云?邊?端”一體化協同感知、協同傳輸和協同決策。為此，急需對自動駕駛車聯網的通感算融合開展研究，實現三者的高效融合。首先論述了目前在通信、感知、計算融合領域的研究進展，然后給出了通感算融合網絡的定義，論述了通感助算、通算助感以及感算助通的研究進展。針對自動駕駛車聯網的應用場景，創造性地提出了“五層四面”通感算融合的網絡架構，橫向五層自下而上分別是：多元接入層、統一網絡層、多域資源層、協同服務層、管理與應用層；縱向四面分別是：通信面、感知面、算力面、智能融合面，通過五層四面的深度融合，進一步提升了自動駕駛車聯網中通感算融合網絡的性能。其次，提出了評價通感算融合網絡的性能指標體系，最后針對目前研究存在的問題以及未來發展方向給出了四點可行性建議。
- 車聯網 /
- 通信?感知?計算融合 /
- 自動駕駛 /
- 邊緣智能 /
- 智能交通
Abstract: To meet extreme performance requirements, such as extremely low communication delay, extremely high reliability, and a higher transmission rate, for autonomous driving in the Internet of vehicles (IoV), the future IoV should be merged into a united framework that integrates communication, sensing, and computing. At the same time, as the 5G-Advanced system moves toward supporting a broader toB vertical industry, it will face a more complex and heterogeneous user environment and multidimensional digital space, which requires 5G-Advanced terminals and 5G-Advanced networks to have stronger environmental sensing, computing, and intelligence capabilities. To realize deep integration for autonomous driving in the IoV, the sensing of IoV depends on not only radar positioning, camera imaging, and various sensor detections but also communication, which can collect a variety of data to the edge node for calculation. At the same time, with the support of cloud edge and end integration efficient computing power to achieve high-precision sensing and efficient communication, the integration network further improves collaborative mobile computing robustness. Therefore, the three functions of communication, sensing, and computing for autonomous driving in the IoV are interrelated and promote each other. To break through the architectural barrier of universal sensing integration in the Internet of autonomous vehicles, it is necessary to explore how to build a universal sensing integration network architecture with decoupled resources, scalable capabilities, and reconfigurable architecture, as well as universal sensing integration resource management technology. However, communication, sensing, and computing are separated from each other in the existing IoV. Thus, we scrutinize the scientific problem of the endogenous integration of communication, sensing, and computing for autonomous driving in the IoV. First, the current research progress in integrating communication, sensing, and computing is discussed. Second, communication-sensing-computing-integrated IoV is defined, and the research progress on communication-sensing-assisted computing, communication-computing-assisted sensing, and sensing-computing-assisted communication is discussed. Aiming at the scenario of an IoV for autonomous driving, the architecture of communication-sensing-computing-integrated IoV with five layers and four planes is proposed. The horizontal five layers from bottom to top are a multiple access layer, unified network layer, multi-domain resource layer, collaborative service layer, and management and application layer. The four vertical planes are communication, sensing, computing power, and intelligent integration planes, respectively. Deeply integrating the five layers and four planes further improves the performance of the integrated IoV. Third, key performance indexes for evaluating the integrated IoV are proposed. Finally, four feasible suggestions are given for the current research problems and the future development direction.
- internet of vehicles /
- communication-sensing-computing integration /
- autonomous driving /
- edge intelligence /
- intelligent transportation

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圖 1 自動駕駛車聯網中通信、感知、計算三大功能關聯關系

Figure 1. Relationships of communication, sensing, and computing in the internet of vehicles for autonomous driving

下載: 全尺寸圖片幻燈片

圖 2 智能內生的“五層四面”通感算融合網絡架構

Figure 2. Intelligent endogenous architecture of the communication-sensing-computing-integrated IoV with five layers and four planes

下載: 全尺寸圖片幻燈片

表 1 通感算融合的資源管理研究領域的代表性論文

Table 1. Representative papers in the field of communication-sensing-computing-integrated resource management

References	Introduction	The target or related work
[6]	Summarize the standard research progress of C-V2X and the resource pool of C-V2X	The centralized and distributed resource scheduling methods under LTE-V2X and NR-V2X are described, respectively
[7]	An edge intelligence multisource data processing scheme for autonomous driving in the IoV is proposed	Improve the system throughout and the accurate inference rate of neural networks
[8]	The network architecture of the Next-generation IoV is proposed	Content distribution, edge caching, computing offloading, and autonomous driving technology are analyzed in detail
[9]	Compare with the delivery to the cloud, MEC reduces the transmission distance and transmission delay	An integrated platform is designed to solve the problem of resource deployment and management
[10]	A system based on MEC is constructed, and an offloading algorithm is proposed	To solve the problem of high traffic density in the Internet of vehicles, manage and control the data offloading of V2X
[11]	The mathematical model for task importance is established, and the task offloading sorting algorithm and the offloading algorithm based on Q learning are designed	The energy consumption and delay of task offloading are optimized
[12]	A distributed offloading strategy where multiple collaborative nodes have a serial offloading mode and parallel computing mode in the V2X scenario is proposed	The optimization problem of system delay minimization is established

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表 2 通信和感知一體化算法部署研究的代表性論文

Table 2. Representative papers on communication–sensing algorithm deployment

References	Introduction	The target or related work
[31]	Vehicle computing resources, cloud computing resources, and MEC resources are used for overall resource scheduling and allocation	The solution is feasible and efficient
[32]	Propose a vehicle-edge-cloud collaborative offloading scheme based on the particle swarm optimization algorithm	Obtain the optimal offloading strategy of each vehicle-edge-cloud computing node
[33]	Propose a distributed end-to-edge collaboration algorithm for the edge network of intelligent connected vehicles	Improve network resource utilization and ensure the fairness of energy consumption of a single vehicle
[34]	The concept of a computing system for an autonomous vehicle is presented	The aim is to better process sensor data and make reliable decisions in real time
[35–36]	The basic concepts of edge intelligence and existing edge intelligence systems are reviewed	The vision and mission of the Internet of vehicles and an application scenario based on 6G edge intelligence are summarized
[37]	The combination of AI processing power and computing power is applied to the problem of computing task offloading in the IoV	By applying edge intelligence technology, computing efficiency is significantly improved

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表 3 通感算融合網絡性能評價體系

Table 3. Key performance indexes (KPIs) of communication-sensing-computing-integrated IoV

First classification	Secondary classification	KPIs
Communication KPIs	Security	Data security
	Reliability	Bit error rate
	Reliability	Network coverage
	Availability	Time delay
		Transmission rate
		Connectivity density
		Spectral efficiency
		Energy efficiency
Sensing KPIs	Target localization	Detection performance
		Localization accuracy
		Target resolution
	Environmental detection	Spatial resolution
		Peak side lobe ratio
		Image entropy
		Perceived range
Computing KPIs	Computing performance index	CPU utilization
		Throughput
		MIPS
	Computing resource index	Total computing resources
		Computing resource usage
		Computing resource utilization
	Computing service index	Computing service reliability
		Computing service efficiency
		Computing service response time

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