Oops! It's Too Late. Your Autonomous Driving System Needs a Faster Middleware

Autonomous Driving (AD) has entered a period of rapid development in recent years. With the amount of sensors and control logics installed increasing tremendously to guarantee robustness, a big challenge is posed for AD middleware. Both the academia and the industry are eager for an investigation of...

Full description

Saved in:
Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 6; no. 4; pp. 7301 - 7308
Main Authors: Wu, Tianze, Wu, Baofu, Wang, Sa, Liu, Liangkai, Liu, Shaoshan, Bao, Yungang, Shi, Weisong
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Communication
Data processing
distributed robot systems
Embedded systems for robotic and automation
Laser radar
Middleware
Open source software
Optimization
Real-time systems
software architecture for robotic and automation
Task analysis
ISSN:2377-3766, 2377-3766
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Autonomous Driving (AD) has entered a period of rapid development in recent years. With the amount of sensors and control logics installed increasing tremendously to guarantee robustness, a big challenge is posed for AD middleware. Both the academia and the industry are eager for an investigation of the performance of middlewares in Autonomous Driving Vehicles (AVs). To fill this gap, we summarize typical communication scenarios of AVs and evaluate different communication mechanisms of three popular open-source middlewares comprehensively. Besides, we construct a benchmark pack named ComP which consists of a perception communication scenario and a group of real AD applications for researchers to assess middleware performance. Our findings provide useful guidelines for researchers and insightful optimization advice for designing middlewares.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3097439