IEEE Communications Surveys & Tutorials (IEEE COMST)
2023

Impact Factor: 35.6   

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

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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IEEE Library Access

@article{Katare:COMST2023,
author = "Katare, Dewant and Perino, Diego and Nurmi, Jari and Warnier, Martijn and Janssen, Marijn and Ding, Aaron Yi",
title = "A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services",
journal = "IEEE Communications Surveys & Tutorials",
year = "2023",
volume={25},
number={4}
}

Dewant Katare, Diego Perino, Jari Nurmi, Martijn Warnier, Marijn Janssen, Aaron Yi Ding, "A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services", in IEEE Communications Surveys & Tutorials, Vol. 25, No. 4, 2023.