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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE robotics and automation letters Ročník 6; číslo 4; s. 7301 - 7308
Hlavní autoři: Wu, Tianze, Wu, Baofu, Wang, Sa, Liu, Liangkai, Liu, Shaoshan, Bao, Yungang, Shi, Weisong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie: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