Journal of Intelligent and Connected Vehicles 2022, 5(3): 309-315 https://doi.org/10.1108/JICV-07-2022-0031

Research paper |

Open Access | Issue | Published: 01 September 2022

Behavioral adaptation of drivers when driving among automated vehicles

Show Author's Information Hide Author's Information Maytheewat Aramrattana^¹(

), Jiali Fu^¹, Selpi^{²^,³}

Department of Traffic and Road Users, Swedish National Road and Transport Research Institute, Linköping, Sweden

Department of Computer Science and Engineering, Chalmers University of Technology, Göteborg

Sweden and Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Göteborg, Sweden

Keywords:

Automated vehicles, Driver behaviors and assistance, Human–robot interaction, Behavioral adaptation, Driving simulator experiment

Cite this article:

Aramrattana M, Fu J, Selpi. Behavioral adaptation of drivers when driving among automated vehicles. Journal of Intelligent and Connected Vehicles, 2022, 5(3): 309-315. https://doi.org/10.1108/JICV-07-2022-0031

Download citation

EndNote(RIS)

BibTeX

399

Views

Downloads

Citations

Crossref

N/A

WoS

Scopus

N/A

CSCD

Abstract Full text About this article

Abstract

Purpose

This paper aims to explore whether drivers would adapt their behavior when they drive among automated vehicles (AVs) compared to driving among manually driven vehicles (MVs).Understanding behavioral adaptation of drivers when they encounter AVs is crucial for assessing impacts of AVs in mixed-traffic situations. Here, mixed-traffic situations refer to situations where AVs share the roads with existing nonautomated vehicles such as conventional MVs.

Design/methodology/approach

A driving simulator study is designed to explore whether such behavioral adaptations exist. Two different driving scenarios were explored on a three-lane highway: driving on the main highway and merging from an on-ramp. For this study, 18 research participants were recruited.

Findings

Behavioral adaptation can be observed in terms of car-following speed, car-following time gap, number of lane change and overall driving speed. The adaptations are dependent on the driving scenario and whether the surrounding traffic was AVs or MVs. Although significant differences in behavior were found in more than 90% of the research participants, they adapted their behavior differently, and thus, magnitude of the behavioral adaptation remains unclear.

Originality/value

The observed behavioral adaptations in this paper were dependent on the driving scenario rather than the time gap between surrounding vehicles. This finding differs from previous studies, which have shown that drivers tend to adapt their behaviors with respect to the surrounding vehicles. Furthermore, the surrounding vehicles in this study are more “free flow'” compared to previous studies with a fixed formation such as platoons. Nevertheless, long-term observations are required to further support this claim.

Full text

Abstract

Full text

Outline

About this article

Behavioral adaptation of drivers when driving among automated vehicles

Show Author's information Hide Author's Information Maytheewat Aramrattana^¹(

), Jiali Fu^¹, Selpi^{²^,³}

Department of Traffic and Road Users, Swedish National Road and Transport Research Institute, Linköping, Sweden

Department of Computer Science and Engineering, Chalmers University of Technology, Göteborg

Sweden and Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Göteborg, Sweden

Abstract

Purpose

Design/methodology/approach

Findings

Originality/value

Keywords: Automated vehicles, Driver behaviors and assistance, Human–robot interaction, Behavioral adaptation, Driving simulator experiment

References(24)

Ali, Y., Zheng, Z. and Mazharul Haque, M. (2021), “Modelling lanechanging execution behaviour in a connected environment: a grouped random parameters with heterogeneity-in-means approach”, Communications in Transportation Research, Vol. 1, p. 100009, doi: 10.1016/j.commtr.2021.100009. ISSN: 2772-4247, available at: www.sciencedirect.com/science/article/pii/S2772424721000093

Andreotti, E., P., . and Boyraz, Selpi. (2020), “Safety-centred analysis of transition stages to traffic with fully autonomous vehicles”, IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1-6, doi: 10.1109/ITSC45102.2020.9294644.

DOI Google Scholar

Aramrattana, M., Larsson, T., Jansson, J. and Nåbo, A. (2019), “A simulation framework for cooperative intelligent transport systems testing and evaluation”, Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 61, pp. 268-280, doi: 10.1016/j.trf.2017.08.004. ISSN: 1369-8478, available at: www.sciencedirect.com/science/article/pii/S1369847816306635

Gouy, M., Wiedemann, K., Stevens, A., Brunett, G. and Reed, N. (2014), “Driving next to automated vehicle platoons: how do short time headways influence non-platoon drivers’ longitudinal control?”, Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 27, pp. 264-273, doi: 10.1016/j.trf.2014.03.003, ISSN: 1369-8478, available at: www.sciencedirect.com/science/article/pii/S1369847814000345

Hiraoka, T., Masui, J. and Nishikawa, S. (2010), “Behavioral adaptation to advanced driver-assistance systems”, Proceedings of SICE Annual Conference, pp. 930-935.

Google Scholar

Jansson, J., Sandin, J., Augusto, B., Fischer, M., Blissing, B. and Källgren, L. (2014), “Design and performance of the VTI sim IV”, Driving simulation conference, pp. 128-138.

Google Scholar

Latin Square Designs (2008), The Concise Encyclopedia of Statistics, Springer, New York, NY, p. 297. ISBN: 978-0-387-32833-1, doi: 10.1007/978-0-387-32833-1_223

Lopez, P., Alvarez, M., Behrisch, L., Bieker-Walz, J., Erdmann, Y. -P., Flötteröd, R., Hilbrich, L., Lücken, J., Rummel, P. and Wießner, E. (2018), “Microscopic traffic simulation using SUMO”, The 21st IEEE International Conference on Intelligent Transportation Systems, IEEE, available at: https://elib.dlr.de/124092/

Louw, T., Goncalves, R., Torrao, G., Radhakrishnan, V., Lyu, W., Guillen, P.P. and Merat, N. (2020), “Do drivers change their manual car-following behaviour after automated car-following?”, Cognition, Technology & Work, pp. 1-15.

Google Scholar

Metz, B., Wörle, J., Hanig, M., Schmitt, M. and Lutz, A. (2020), “Repeated usage of an L3 motorway chauffeur: change of evaluation and usage”, Information, Vol. 11 No. 2, doi: 10.3390/info11020114, ISSN: 2078-2489, available at: www.mdpi.com/2078-2489/11/2/114

Mintsis, E., Koutras, D., Porfyri, K., Mitsakis, E., Lücken, L., Erdmann, J., Flötteröd, Y. -P., Alms, R., Rondinone, M. and Maerivoet, S. (2019), “TransAID deliverable 3.1-Modelling, simulation and assessment of vehicle automations and automated vehicles’ driver behaviour in mixed traffic”, Tech. rep, available at: www.transaid.eu/wp-content/uploads/2017/Deliverables/WP3/TransAID_D3.1_Modelling-simulation-and-assessment-of-vehicle-automations.pdf (accessed 2 August 2022).

Morando, M.M., Tian, Q., Truong, L.T. and Vu, H.L. (2018), “Studying the safety impact of autonomous vehicles using Simulation-Based surrogate safety measures”, Journal of Advanced Transportation, Vol. 2018, doi: 10.1155/2018/6135183.

DOI Google Scholar

Ngoduy, D. (2015), “Effect of the car-following combinations on the instability of heterogeneous traffic flow”, Transportmetrica B: Transport Dynamics, Vol. 3 No. 1, pp. 44-58, doi: 10.1080/21680566.2014.960503.

DOI Google Scholar

Rämä, P. and Kulmala, R. (2013), “Definition of behavioural adaptation”, in Rudin-Brown C. and Jamson, S. (Eds), Behavioural Adaptation and Road Safety, CRC Press, pp. 11-21, ISBN: 978-1-4398-5667-3.

Robertson, R.D., Meister, S.R., Vanlaar, W.G. and Mainegra Hing, M. (2017), “Automated vehicles and behavioural adaptation in Canada”, Transportation Research Part A: Policy and Practice, Vol. 104, pp. 50-57, doi: 10.1016/j.tra.2017.08.005. ISSN: 0965-8564, available at: www.sciencedirect.com/science/article/pii/S0965856417305062

Schoenmakers, M., Yang, D. and Farah, H. (2021), “Car-following behavioural adaptation when driving next to automated vehicles on a dedicated lane on motorways: a driving simulator study in The Netherlands”, Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 78, pp. 119-129, doi: 10.1016/j.trf.2021.01.010, ISSN: 1369-8478, available at: www.sciencedirect.com/science/article/pii/S1369847821000176

Sibi, S., Balters, S., Fu, E., Strack, E.G., Steinert, M. and Ju, W. (2020), “Back to school: impact of training on driver behavior and state in autonomous vehicles”, IEEE Intelligent Vehicles Symposium (IV), pp. 1189-1196, doi: 10.1109/IV47402.2020.9304537.

DOI Google Scholar

Soni, S. (2020), “Understanding behavioural adaptations of human drivers interacting with automated vehicles”, Master’s Thesis, Delft University of Technology.

Sullivan, J.M., Flannagan, M.J., Pradhan, A.K. and Bao, S. (2016), “Literature review of behavioral adaptations to advanced driver assistance systems”, Technical report, AAA Foundation for Traffic Safety.

Treiber, M., Hennecke, A. and Helbing, D. (2000), “Congested traffic states in empirical observations and microscopic simulations”, Physical Review E, Vol. 62 No. 2, pp. 1805-1824, doi: 10.1103/PhysRevE.62.1805.

DOI Google Scholar

Van Der Laan, J.D., Heino, A. and De Waard, D. (1997), “A simple procedure for the assessment of acceptance of advanced transport telematics”, Transportation Research Part C: Emerging Technologies, Vol. 5 No. 1, pp. 1-10, ISSN: 0968-090X, doi: 10.1016/S0968-090X(96)00025-3 available at: www.sciencedirect.com/science/article/pii/S0968090X96000253

Varotto, S.F., Farah, H., Bogenberger, K., van Arem, B. and Hoogendoorn, S.P. (2020), “Adaptations in driver behaviour characteristics during control transitions from full-range adaptive cruise control to manual driving: an on-road study”, Transportmetrica A: Transport Science, Vol. 16 No. 3, pp. 776-806, doi: 10.1080/23249935.2020.1720856.

DOI Google Scholar

Vogel, K. (2002), “What characterizes a “free vehicle” in an urban area?”, Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 5 No. 1, pp. 15-29, ISSN: 1369-8478, doi: 10.1016/S1369-8478(02)00003-7, available at: www.sciencedirect.com/science/article/pii/S1369847802000037

Yang, D., Jin, P., Pu, Y. and Ran, B. (2014), “Stability analysis of the mixed traffic flow of cars and trucks using heterogeneous optimal velocity carfollowing model”, Physica A: Statistical Mechanics and Its Applications, Vol. 395, pp. 371-383, ISSN: 03784371, doi: 10.1016/j.physa.2013.10.017.

About this article

Publication history

Rights and permissions

Publication history

Received: 16 July 2022

Revised: 01 August 2022

Accepted: 01 August 2022

Published: 01 September 2022

Issue date: October 2022

Copyright

Rights and permissions

This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode