A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services

IF 34.4 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Communications Surveys and Tutorials Pub Date : 2023-08-09 DOI:10.1109/COMST.2023.3302474

Dewant Katare;Diego Perino;Jari Nurmi;Martijn Warnier;Marijn Janssen;Aaron Yi Ding

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引用次数: 0

Abstract

Autonomous driving services depends on active sensing from modules such as camera, LiDAR, radar, and communication units. Traditionally, these modules process the sensed data on high-performance computing units inside the vehicle, which can deploy intelligent algorithms and AI models. The sensors mentioned above can produce large volumes of data, potentially reaching up to 20 Terabytes. This data size is influenced by factors such as the duration of driving, the data rate, and the sensor specifications. Consequently, this substantial amount of data can lead to significant power consumption on the vehicle. Similarly, a substantial amount of data will be exchanged between infrastructure sensors and vehicles for collaborative vehicle applications or fully connected autonomous vehicles. This communication process generates an additional surge of energy consumption. Although the autonomous vehicle domain has seen advancements in sensory technologies, wireless communication, computing and AI/ML algorithms, the challenge still exists in how to apply and integrate these technology innovations to achieve energy efficiency. This survey reviews and compares the connected vehicular applications, vehicular communications, approximation and Edge AI techniques. The focus is on energy efficiency by covering newly proposed approximation and enabling frameworks. To the best of our knowledge, this survey is the first to review the latest approximate Edge AI frameworks and publicly available datasets in energy-efficient autonomous driving. The insights from this survey can benefit the collaborative driving service development on low-power and memory-constrained systems and the energy optimization of autonomous vehicles.

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面向节能自动驾驶服务的近似边缘人工智能研究

自动驾驶服务依赖于摄像头、激光雷达、雷达和通信单元等模块的主动传感。传统上，这些模块在车内的高性能计算单元上处理感知数据，这些计算单元可以部署智能算法和人工智能模型。上面提到的传感器可以产生大量数据，可能达到20tb。该数据大小受驾驶持续时间、数据速率和传感器规格等因素的影响。因此，大量的数据可能会导致车辆的大量功耗。同样，基础设施传感器和车辆之间将交换大量数据，用于协作车辆应用或完全连接的自动驾驶车辆。这种通信过程产生了额外的能源消耗激增。尽管自动驾驶汽车领域在传感技术、无线通信、计算和人工智能/机器学习算法方面取得了进步，但如何应用和整合这些技术创新以实现能源效率仍然存在挑战。本调查回顾并比较了联网车辆应用、车辆通信、近似和边缘人工智能技术。重点是通过涵盖新提出的近似和使能框架来提高能源效率。据我们所知，这项调查首次回顾了节能自动驾驶领域最新的近似边缘人工智能框架和公开可用的数据集。该调查的见解有助于低功耗和内存受限系统的协同驾驶服务开发，以及自动驾驶汽车的能源优化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Surveys and Tutorials COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS

CiteScore

80.20

自引率

2.50%

发文量

审稿时长

6 months

期刊介绍： IEEE Communications Surveys & Tutorials is an online journal published by the IEEE Communications Society for tutorials and surveys covering all aspects of the communications field. Telecommunications technology is progressing at a rapid pace, and the IEEE Communications Society is committed to providing researchers and other professionals the information and tools to stay abreast. IEEE Communications Surveys and Tutorials focuses on integrating and adding understanding to the existing literature on communications, putting results in context. Whether searching for in-depth information about a familiar area or an introduction into a new area, IEEE Communications Surveys & Tutorials aims to be the premier source of peer-reviewed, comprehensive tutorials and surveys, and pointers to further sources. IEEE Communications Surveys & Tutorials publishes only articles exclusively written for IEEE Communications Surveys & Tutorials and go through a rigorous review process before their publication in the quarterly issues. A tutorial article in the IEEE Communications Surveys & Tutorials should be designed to help the reader to become familiar with and learn something specific about a chosen topic. In contrast, the term survey, as applied here, is defined to mean a survey of the literature. A survey article in IEEE Communications Surveys & Tutorials should provide a comprehensive review of developments in a selected area, covering its development from its inception to its current state and beyond, and illustrating its development through liberal citations from the literature. Both tutorials and surveys should be tutorial in nature and should be written in a style comprehensible to readers outside the specialty of the article.