Co-Simulation Platform for Modeling and Evaluating Connected and Automated Vehicles and Human Behavior in Mixed Traffic
Modeling, prediction, and evaluation of personalized driving behaviors are crucial to emerging advanced driver-assistance systems (ADAS) that require a large amount of customized driving data. However, collecting such type of data from the real world could be very costly and sometimes unrealistic. To address this need, several high-definition game engine-based simulators have been developed. Furthermore, the computational load for cooperative automated driving systems (CADS) with a decent size may be much beyond the capability of a standalone (edge) computer. To address all these concerns, in this study we develop a co-simulation platform integrating Unity, Simulation of Urban MObility (SUMO), and Amazon Web Services (AWS), where Unity provides realistic driving experience and simulates on-board sensors; SUMO models realistic traffic dynamics; and AWS provides serverless cloud computing power and personalized data storage. To evaluate this platform, we select cooperative on-ramp merging in mixed traffic as a study case, and establish human-in-the-loop (HuiL) simulations. The results show that our proposed platform can facilitate data collection and performance assessment for modeling personalized behaviors and interactions in CADS under various traffic scenarios.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/25740741
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Supplemental Notes:
- Abstract reprinted with permission of SAE International.
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Authors:
- Zhao, Xuanpeng
- Liao, Xishun
- Wang, Ziran
- Wu, Guoyuan
- Barth, Matthew
- Han, Kyungtae
- Tiwari, Prashant
- Publication Date: 2022-4-21
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 313-326
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Serial:
- SAE International Journal of Connected and Automated Vehicles
- Volume: 5
- Issue Number: 4
- Publisher: SAE International
- ISSN: 2574-0741
- Serial URL: https://www.sae.org/publications/collections/content/E-JOURNAL-12/
Subject/Index Terms
- TRT Terms: Actuators; Behavior; Cloud computing; Data collection; Driver support systems; Intelligent vehicles; Sensors; Simulation; Training simulators; Vehicle design
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01845557
- Record Type: Publication
- Source Agency: SAE International
- Report/Paper Numbers: 12-05-04-0025
- Files: TRIS, SAE
- Created Date: May 17 2022 1:27PM