Hidden Markov Model of Lane-Changing-Based Car-Following Behavior on Freeways using Naturalistic Driving Data
This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3?s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86?s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
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Supplemental Notes:
- © National Academy of Sciences: Transportation Research Board 2021.
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Authors:
- Zhao, Li
- Rilett, Laurence
- Haque, Mm Shakiul
- Publication Date: 2021-8
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 550-561
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Serial:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume: 2675
- Issue Number: 8
- Publisher: Sage Publications, Incorporated
- ISSN: 0361-1981
- EISSN: 2169-4052
- Serial URL: http://journals.sagepub.com/home/trr
Subject/Index Terms
- TRT Terms: Automatic data collection systems; Automobile drivers; Car following; Data processing operations; Freeways; Intelligent vehicles; Lane changing; Machine learning; Markov processes; Predictive models
- Identifier Terms: Safety Pilot Model Deployment
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01771560
- Record Type: Publication
- Files: TRIS, TRB, ATRI
- Created Date: May 14 2021 10:45AM