Car-Following Characteristics of Adaptive Cruise Control from Empirical Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 9
Abstract
Computer-driven vehicles will behave differently from human-driven vehicles due to changes in perception abilities, precision control, and reaction times. These changes are expected to have profound impacts on capacity, yet few models of automated driving are based on empirical measurements of computer-driven vehicles in real traffic. To this end, this paper investigates characteristics of an early form of longitudinal control automation, a commercially available adaptive cruise control (ACC) system driven in real traffic. Two car-following models were calibrated to a vehicle with ACC. First, the Intelligent Driver Model was reformulated to comply with ACC design standards then calibrated to match speed and range data from the test vehicle. The vehicle with ACC was found to decelerate less severely than predicted by the model when tested in severe braking and unimpeded acceleration scenarios. Second, the Wiedemann 99 model was calibrated because it is the default car-following model in the traffic microsimulation software program Vissim and can therefore be implemented cheaply and quickly in sophisticated models of roadways worldwide. Four parameters of the Wiedemann 99 model were measured directly from field observations of the test vehicle: standstill distance, start-up time, unimpeded acceleration profile, and maximum desired deceleration. Simulation results in Vissim were found to match the adaptive cruise control in unimpeded acceleration tests. These findings will benefit researchers and modelers seeking more accurate models of car-following behavior with adaptive cruise control and automated longitudinal control.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Available data include speed/acceleration/gap measurements, Vissim trajectories and models, and IDM calculations.
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. In-car video may be shared in short, anonymized sections to protect identities of other road users.
Acknowledgments
This work was sponsored by the Virginia Department of Transportation. The authors thank Eun (Tina) Lee for her assistance with data collection.
References
Audi AG. 2016. Owner’s manual, 2017 Q7. Ingolstadt, Germany: Audi AG.
Bierstedt, J., A. Gooze, C. Gray, J. Peterman, L. Raykin, and J. Walters. 2014. Effects of next-generation vehicles on travel demand and highway capacity. Princeton, NJ: FP Think Working Group, Univ. of Princeton.
Brackstone, M., and M. McDonald. 1999. “Car-following: A historical review.” Transp. Res. Part F Traffic Psychol. Behav. 2 (4): 181–196. https://doi.org/10.1016/S1369-8478(00)00005-X.
Brackstone, M., M. Montanino, W. Daamen, C. Buisson, and V. Punzo. 2012. “Use, calibration, and validation of traffic simulation models in practice: Results of web-based survey.” In Proc., 91st Annual Meeting of the Transportation Research Board, Washington, DC: Transportation Research Board.
Gipps, P. G. 1981. “A behavioural car-following model for computer simulation.” Transp. Res. Part B Methodol. 15 (2): 105–111. https://doi.org/10.1016/0191-2615(81)90037-0.
ISO. 2010. Intelligent transport systems—Adaptive cruise control systems—Performance requirements and test procedures. ISO 15622:2010(EN). Geneva: ISO.
Jerath, K., and S. N. Brennan. 2012. “Analytical prediction of self-organized traffic jams as a function of increasing ACC penetration.” IEEE Trans. Intell. Transp. Syst. 13 (4): 1782–1791. https://doi.org/10.1109/TITS.2012.2217742.
Kesting, A., M. Treiber, and D. Helbing. 2010. “Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity.” Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 368 (1928): 4585–4605. https://doi.org/10.1098/rsta.2010.0084.
Kesting, A., M. Treiber, M. Schönhof, and D. Helbing. 2007a. “Extending adaptive cruise control to adaptive driving strategies.” Transp. Res. Rec. 2000 (1): 16–24. https://doi.org/10.3141/2000-03.
Kesting, A., M. Treiber, M. Schönhof, F. Kranke, and D. Helbing. 2007b. “Jam-avoiding adaptive cruise control (ACC) and its impact on traffic dynamics.” In Traffic and granular flow’05, edited by A. Schadschneider, T. Pöschel, R. Kühne, M. Schreckenberg, and D. E. Wolf, 633–643. Berlin: Springer.
Lewis, P., and A. Grossman. 2019. Beyond speculation 2.0: An update to Eno’s action plan for federal, state, and local policymakers. Washington, DC: Eno Center for Transportation.
Li, Z., W. Li, S. Xu, and Y. Qian. 2015. “Stability analysis of an extended intelligent driver model and its simulations under open boundary condition.” Physica A: Stat. Mech. Appl. 419 (Feb): 526–536. https://doi.org/10.1016/j.physa.2014.10.063.
Liebner, M., M. Baumann, F. Klanner, and C. Stiller. 2012. “Driver intent inference at urban intersections using the intelligent driver model.” In Proc., 2012 IEEE Int. Vehicles Symp., 1162–1167. New York: IEEE.
Mahmassani, H. S., A. Elfar, S. Shladover, and Z. Huang. 2018. Development of an analysis/modeling/simulation (AMS) framework for V2I and connected/automated vehicle environment, 180. Washington, DC: Federal Highway Administration.
Makridis, M., K. Mattas, and B. Ciuffo. 2020. “Response time and time headway of an adaptive cruise control: An empirical characterization and potential impacts on road capacity.” IEEE Trans. Intell. Transp. Syst. 21 (4): 1677–1686. https://doi.org/10.1109/TITS.2019.2948646.
Milanés, V., and S. E. Shladover. 2014. “Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data.” Transp. Res. Part C Emerging Technol. 48 (Nov): 285–300. https://doi.org/10.1016/j.trc.2014.09.001.
Miller, J. S., and D. Kang. 2019. Ways to consider driverless vehicles in Virginia long-range travel demand models. Charlottesville, VA: Virginia Transportation Research Council.
National Conference of State Legislatures. 2019. “Autonomous vehicles | Self-driving vehicles enacted legislation.” Accessed July 10, 2019. http://www.ncsl.org/research/transportation/autonomous-vehicles-self-driving-vehicles-enacted-legislation.aspx.
Ntousakis, I. A., I. K. Nikolos, and M. Papageorgiou. 2015. “On microscopic modelling of adaptive cruise control systems.” Transp. Res. Procedia 6: 111–127. https://doi.org/10.1016/j.trpro.2015.03.010.
Olstam, J. J., and A. Tapani. 2004. Comparison of car-following models. Linkping, Sweden: Swedish National Road and Transport Research Institute.
PTV AG. 2018. PTV Vissim 10 user manual, 1155. Karlsruhe, Germany: PTV Group.
Shladover, S. E., D. Su, and X.-Y. Lu. 2012. “Impacts of cooperative adaptive cruise control on freeway traffic flow.” Transp. Res. Rec. 2324 (1): 63–70. https://doi.org/10.3141/2324-08.
Su, P. P., J. Ma, T. W. P. Lochrane, D. J. Dailey, and D. Hale. 2016. “Integrated adaptive cruise control car-following model based on trajectory data.” In Proc., 95th Annual Meeting of the Transportation Research Board. Washington, DC: Transportation Research Board.
Sukennik, P. 2018. Micro-simulation guide for automated vehicles. PTV Group, Karlsruhe, Germany.
Transportation Research Board. 2010. Highway capacity manual 2010. Washington, DC: Transportation Research Board.
Treiber, M., A. Hennecke, and D. Helbing. 2000. “Congested traffic states in empirical observations and microscopic simulations.” Phys. Rev. E 62 (2): 1805–1824. https://doi.org/10.1103/PhysRevE.62.1805.
VanderWerf, J., S. Shladover, N. Kourjanskaia, M. Miller, and H. Krishnan. 2001. “Modeling effects of driver control assistance systems on traffic.” Transp. Res. Rec. 1748 (1): 167–174. https://doi.org/10.3141/1748-21.
VanderWerf, J., S. E. Shladover, M. A. Miller, and N. Kourjanskaia. 2002. “Effects of adaptive cruise control systems on highway traffic flow capacity.” Transp. Res. Rec. 1800 (1): 78–84. https://doi.org/10.3141/1800-10.
Wayland, M. 2015. “Adaptive cruise control goes mainstream.” Accessed July 30, 2019. https://www.detroitnews.com/story/business/autos/2015/03/03/adaptive-cruise-control-growing/24352141/.
Weinberger, M., H. Winner, and H. Bubb. 2001. “Adaptive cruise control field operational test—The learning phase.” JSAE Rev. 22 (4): 487–494. https://doi.org/10.1016/S0389-4304(01)00142-4.
Wiedemann, R. 1974. Simulation des strassenverkehrsflusses. Karlsruhe, Germany: Schriftenreihe des Instituts für Verkehrswesen der Universität Karlsruhe.
Wiedemann, R., and U. Reiter. 1992. The simulation system MISSION, background and actual state. Brussels, Belgium: Commission of the European Communities.
Xiao, L., and F. Gao. 2010. “A comprehensive review of the development of adaptive cruise control systems.” Veh. Syst. Dyn. 48 (10): 1167–1192. https://doi.org/10.1080/00423110903365910.
Yuan, Y.-M., R. Jiang, M.-B. Hu, Q.-S. Wu, and R. Wang. 2009. “Traffic flow characteristics in a mixed traffic system consisting of ACC vehicles and manual vehicles: A hybrid modelling approach.” Physica A 388 (12): 2483–2491. https://doi.org/10.1016/j.physa.2009.02.033.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 9 • September 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Dec 6, 2019
Accepted: May 4, 2020
Published online: Jul 7, 2020
Published in print: Sep 1, 2020
Discussion open until: Dec 7, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.