NDLI: Modelling merging behaviour joining a cooperative adaptive cruise control platoon

Content Provider	IET Digital Library
Author	Hu, Jia Wang, Haoran Li, Xin Li, Xinghua
Abstract	Cooperative adaptive cruise control (CACC) has shown great potential in improving freeway capacity. Although the benefit of CACC is obvious, its potential side effects are not yet well studied. One of the major factors that have been overlooked is merging behaviour. A driving simulator study has been recently conducted at the Federal Highway Administration of the United States and reveals that there is unique driving behaviour when joining and leaving a CACC platoon. Unlike the conventional merging model which is a passive decision action, merging into a CACC platoon is a proactive action. Without simulating this unique behaviour, any simulation evaluation on CACC is biased. To improve the validity of future CACC simulation evaluation, this research constructs a merging model. The model consists of two parts: the longitudinal trajectory model and the merging duration prediction model. The model was constructed for both human manual driver and CACC automated controller. The evaluation of the proposed model shows that the model is 96.5% accurate in terms of merging duration prediction and 95.2% accurate in terms of speed prediction.
Starting Page	693
Ending Page	701
Page Count	9
ISSN	1751956X
Volume Number	14
e-ISSN	17519578
Issue Number	Issue 7, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0378
Journal	IET Intelligent Transport Systems
Publisher Date	2020-02-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Control Adaptive Cruise Control Platoon CACC Automated Controller CACC Platoon Collision Avoidance Conventional Merging Model Driving Simulator Study Federal Highway Administration Freeway Capacity Future CACC Simulation Evaluation Longitudinal Trajectory Model Merging Duration Prediction Model Passive Decision Action Potential Side Effect Proactive Action Road Accidents Road Safety Road Traffic Road Traffic Control Road Traffic System Control Road Vehicle Self Adjusting Control System Traffic Control Traffic Engineering Computing Unique Behaviour Unique Driving Behaviour United State
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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