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Full text release has been delayed at the author's request until May 10, 2025

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Forecasting Human Response in The loop with Eco-Driving Advanced Driver Assistance Systems (ADAS): A Modeling and Experimental Study

Jacome, Olivia M
http://orcid.org/0000-0003-4614-6438
http://rave.ohiolink.edu/etdc/view?acc_num=osu1650474477983082

Abstract Details

2022, Master of Science, Ohio State University, Mechanical Engineering.
In recent years, vehicle electrification has risen due to the increasingly stringent polices put in place to reduce greenhouse gas emissions in the transportation industry. At the same time, research and development efforts in Connected and Autonomous Vehicles (CAVs) has grown substantially due to the advancement of new technologies that has encouraged the deployment of semi-autonomous vehicles. Vehicles with partial or conditional automation require a collaboration between the vehicle control system and the human driver for safe execution of maneuvers. As a result, humans play a critical role in the development and deployment of Advanced Driver Assistance Systems (ADAS), warranting the need to understand the human-machine interaction issues related to these systems, and to analyze their effects on vehicle performance and energy consumption. This work investigates the effects of the interactions between a human driver and a vehicle equipped with ADAS, focusing on the case of a human in the loop with a vehicle speed advisory system. To this end, a simulation study is conducted to evaluate the importance of modeling the driver behavior when optimizing the vehicle velocity for Eco-Driving. An optimization study is conducted via dynamic programming, incorporating driver behavior and its response to a velocity advisory. Next, an investigation is conducted to evaluate the accuracy of different mathematical models predicting driver behavior in the context of ADAS. To this end, an experimental study was conducted on a driving simulator where human drivers were compared with respect to their ability to follow a velocity advisor. Data collected from the driver simulator were used to calibrate a deterministic and a stochastic driver model, and compare their ability to replicate realistic velocity profiles and driver error.
Marcello Canova (Advisor)
Giorgio Rizzioni (Committee Member)
Stephanie Stockar (Committee Member)
110 p.
Mechanical Engineering
Eco-driving, speed advisory, HEV, vehicle optimization, EDM, LSTM, LSTMED, Dynamic Programing

Recommended Citations

Citations

  • Jacome, O. M. (2022). Forecasting Human Response in The loop with Eco-Driving Advanced Driver Assistance Systems (ADAS): A Modeling and Experimental Study [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1650474477983082

    APA Style (7th edition)

  • Jacome, Olivia. Forecasting Human Response in The loop with Eco-Driving Advanced Driver Assistance Systems (ADAS): A Modeling and Experimental Study. 2022. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1650474477983082.

    MLA Style (8th edition)

  • Jacome, Olivia. "Forecasting Human Response in The loop with Eco-Driving Advanced Driver Assistance Systems (ADAS): A Modeling and Experimental Study." Master's thesis, Ohio State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=osu1650474477983082

    Chicago Manual of Style (17th edition)

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© 2022, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.