Cooperative control of a platoon of connected autonomous vehicles and unconnected human-driven vehicles
During the transition to full traffic automation, CAVs and human-driven vehicles (HDVs) will coexist in mixed-flow traffic. Connected autonomous vehicles (CAVs) can cooperate in mitigating shockwaves from traffic incidents, using kinematic state information obtained through vehicle-to-vehicle communications (V2I), but human driving behavior can cause disturbances in traffic flow and negatively affect platooning. To counter the negative effects of HDVs in mixed traffic, the authors propose a cooperative control strategy, with three components, for mixed-traffic platoons: 1) a number estimator for estimating the number of HDVs between two CAVs; 2) a kinematic state predictor for predicting the kinematic states of HDVs; and 3) a control strategy using information on kinematic state of multiple preceding vehicles to enable CAVs to maintain string stability and headway. The number estimator is developed using a deep neural network (DNN). A DNN-based kinematic state predictor then estimates HDV kinematics to enable multi-anticipative car-following control for CAVs. The car-following controller is implemented using an intelligent driver algorithm, which ensures safety, string stability, and efficient traffic flow. The control strategy’s effectiveness is validated using Next Generation Simulation (NGSIM) data. Results indicate that the proposed strategy can accurately estimate the number and the kinematic states of HDVs between CAVs. It can also achieve string stability while maintaining smaller time headways.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/10939687
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Authors:
- Zhou, Anye
- Peeta, Srinivas
- Wang, Jian
- Publication Date: 2023-12
Language
- English
Media Info
- Media Type: Web
- Features: Appendices; Figures; References; Tables;
- Pagination: pp 2513-2536
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Serial:
- Computer-Aided Civil and Infrastructure Engineering
- Volume: 38
- Issue Number: 18
- Publisher: Blackwell Publishing
- ISSN: 1093-9687
- Serial URL: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-8667
Subject/Index Terms
- TRT Terms: Algorithms; Autonomous vehicles; Connected vehicles; Neural networks; Traffic platooning; Vehicle mix; Vehicle to vehicle communications
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;
Filing Info
- Accession Number: 01909065
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 21 2024 10:04AM