Abstract
This article uses experimentally collected car following data from seven different commercially available adaptive cruise control (ACC) vehicles to calibrate microscopic models for each system's car following behavior using three different common car following models. Calibration is conducted by selecting the model parameters that minimize the error between the simulated vehicle trajectories and the experimental data. The goal of this study is two-fold: (i) assess which car- following models typically used to describe human driving behavior are best for describing ACC car-following dynamics, and (ii) provide best-fit calibrated car following models for seven different commercially available ACC vehicles, which can be used to understand the traffic flow impact of ACC systems via simulation analysis. We find that the intelligent driver model and the optimal velocity model with a relative velocity term perform best, and with similar performance to one another, while the Gazis-Herman-Rothery model as calibrated does not capture all the ACC car following dynamics.
| Original language | English (US) |
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| Title of host publication | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3049-3054 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538670248 |
| DOIs | |
| State | Published - Oct 2019 |
| Event | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand Duration: Oct 27 2019 → Oct 30 2019 |
Publication series
| Name | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 |
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Conference
| Conference | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 |
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| Country/Territory | New Zealand |
| City | Auckland |
| Period | 10/27/19 → 10/30/19 |
Bibliographical note
Funding Information:VII. ACKNOWLEDGEMENTS This material is based upon work supported by the National Science Foundation under Grants No. CNS-1837652 (D.W. & R.S.) and OISE-1743772 (G.G.) as well as through the Federal Highway Administration’s Dwight David Eisenhower Transportation Fellowship Program under Grant No. 693JJ31845050 (R.S). The authors would also like to acknowledge C. Janssen, Y. Wang, D. Gloudemans, and W. Barbour for assisting in data collection as well as the Vanderbilt VUSE Summer Research Program.
Funding Information:
This material is based upon work supported by the National Science Foundation under Grants No. CNS-1837652 (D.W. & R.S.) and OISE-1743772 (G.G.) as well as through the Federal Highway Administration's Dwight David Eisenhower Transportation Fellowship Program under Grant No. 693JJ31845050 (R.S). The authors would also like to acknowledge C. Janssen, Y. Wang, D. Gloudemans, and W. Barbour for assisting in data collection as well as the Vanderbilt VUSE Summer Research Program.
Publisher Copyright:
© 2019 IEEE.