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RaviLanka_ThesisFinal.pdf (4.82 MB)

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VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS

Lanka, Venkata Raghava Ravi Teja, Lanka
http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084

Abstract Details

2017, Master of Science, Ohio State University, Mechanical Engineering.
With sporadic advancement in computer technology, transportation is moving towards autonomy. With rapid increase in production of highly automated vehicles (AVs), validation and safety of AVs is gaining high importance. The estimation of safety for AVs is a challenging problem as the AVs mimic human drivers and it requires an estimate of AVs response at all critical scenarios. AV response in each scenario, if known, can be used for estimating its safety. In this work, methods for estimating vehicle response are proposed by using various models based on both physics-based modeling as well as Machine Learning algorithms. Various Machine Learning algorithms were explored for classifying and predicting driver’s intention, such as Extremely Randomized Trees and Gaussian Mixture Model based Hidden Markov Model. Also, physics-based modeling is done for longitudinal car-following conditions using three models namely: Spring-Damper model, Time-to-Collision model and Gazis-Herman-Rothery model. The Machine Learning models were fitted using Naturalistic Driving Study dataset (NDS) collected as a part of Strategic Highway Research Program-2 (SHRP2). The vehicular data comprising of various vehicular parameters is processed and analyzed for preparing driver’s behavior model, which gives an estimate of vehicle’s longitudinal and lateral acceleration at that given instance. Physics-based models were limited to longitudinal acceleration prediction as lateral acceleration prediction in dynamic traffic conditions is a highly complex problem for modeling. The physics-based models were fitted using both SHRP2 as well as the test track data of AVs collected from Transportation Research Center Inc. Then, the fitted Machine Learning and physics-based models were validated against validation data. The parameters obtained from physics-based models were used for obtaining driving characteristics, which were used to compare tested AVs among themselves as well as human drivers.
Gary Heydinger (Advisor)
Dennis Guenther (Committee Chair)
116 p.
Engineering; Mechanical Engineering; Transportation
Machine Learning, Longitudinal Car-Following Models, Extremely Randomized Trees, Random Forest, Hidden Markov Models, Validation

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Citations

  • Lanka, Lanka, V. R. R. T. (2017). VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084

    APA Style (7th edition)

  • Lanka, Lanka, Venkata Raghava Ravi Teja. VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS. 2017. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084.

    MLA Style (8th edition)

  • Lanka, Lanka, Venkata Raghava Ravi Teja. "VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS." Master's thesis, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084

    Chicago Manual of Style (17th edition)

osu1511891682062084
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© 2017, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.