Edge Intelligence for Autonomous Driving in 6G Wireless System: Design Challenges and Solutions

In a level-5 autonomous driving system, the autonomous driving vehicles (AVs) are expected to sense the surroundings via analyzing a large amount of data captured by a variety of onboard sensors in near-real-time. As a result, enormous computing costs will be introduced to the AVs for processing the...

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Vydané v:IEEE wireless communications Ročník 28; číslo 2; s. 40 - 47
Hlavní autori: Yang, Bo, Cao, Xuelin, Xiong, Kai, Yuen, Chau, Guan, Yong Liang, Leng, Supeng, Qian, Lijun, Han, Zhu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
6G mobile communication
Artificial neural networks
Autonomous vehicles
Computational modeling
Computing costs
Edge computing
Inference
Intelligence
Machine learning
Mixed integer
Mobile computing
Model accuracy
Neural networks
Nonlinear programming
Real time
Resource allocation
Segmentation
Sensor systems
Servers
Vehicles
Wireless communication
Wireless sensor networks
ISSN:1536-1284, 1558-0687
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Shrnutí:In a level-5 autonomous driving system, the autonomous driving vehicles (AVs) are expected to sense the surroundings via analyzing a large amount of data captured by a variety of onboard sensors in near-real-time. As a result, enormous computing costs will be introduced to the AVs for processing the tasks with the deployed machine learning (ML) model, while the inference accuracy may not be guaranteed. In this context, the advent of edge intelligence (EI) and sixth-generation (6G) wireless networking are expected to pave the way to more reliable and safer autonomous driving by providing multi-access edge computing (MEC) together with ML to AVs in close proximity. To realize this goal, we propose a two-tier EI-empowered autonomous driving framework. In the autonomous-vehicles tier, the autonomous vehicles are deployed with the shallow layers by splitting the trained deep neural network model. In the edge-intelligence tier, an edge server is implemented with the remaining layers (also deep layers) and an appropriately trained multi-task learning (MTL) model. In particular, obtaining the optimal offloading strategy (including the binary offloading decision and the computational resources allocation) can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved via MTL in near-real-time with high accuracy. On another note, an edge-vehicle joint inference is proposed through neural network segmentation to achieve efficient online inference with data privacy-preserving and less communication delay. Experiments demonstrate the effectiveness of the proposed framework, and open research topics are finally listed.
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ISSN:1536-1284
1558-0687
DOI:10.1109/MWC.001.2000292