research-article

The Exploration of Autonomous Vehicle Driving Styles: Preferred Longitudinal, Lateral, and Vertical Accelerations

Authors:

Nidzamuddin Md. Yusof,

Juffrizal Karjanto,

Jacques Terken,

Frank Delbressine,

Muhammad Zahir Hassan,

Matthias RauterbergAuthors Info & Claims

Automotive'UI 16: Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

Pages 245 - 252

https://doi.org/10.1145/3003715.3005455

Published: 24 October 2016 Publication History

Abstract

This paper describes a new approach in exploring preferred driving styles for autonomous vehicles through simulation of autonomous driving in real road conditions. A Wizard experiment with an equipped car was conducted to investigate the preferences of people with different driving styles, assertive and defensive, for three autonomous vehicle driving styles (defensive, assertive and light rail transit), inducing different acceleration forces, at three different road profiles. Subjective and objective measurements were collected. The results show that the defensive driving style was preferred and there were variations between participants related to their own driving style. The results indicate that the preferences of assertive drivers for the driving style of an autonomous vehicle may not match their own driving style. Yet, users of future autonomous vehicles should be able to indicate and adjust the driving behaviour of an autonomous vehicle to their own preferences in order to maximize comfort in travelling experience.

References

[1]

C. Diels. 2014. Will autonomous vehicles make us sick?. In Contemporary Ergonomics and Human Factors, S. Sharples and S. Shorrock (eds.). 301--307.

[2]

A. E. af Wåhlberg. 2006. Short-term effects of training in economical driving: Passenger comfort and driver acceleration behavior. International Journal of Industrial Ergonomics. 36, 2, 151--163.

[3]

O. Taubman-Ben-Ari, M. Mikulincer, and O. Gillath. 2004. The multidimensional driving style inventory - Scale construct and validation. Accident Analysis and Prevention. 36, 3, 323--332.

[4]

H. Hooft van Huysduynen, J. Terken, J. Martens, and B. Eggen. 2015. Measuring Driving Styles: A Validation of the Multidimensional Driving Style Inventory. In 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 257--264.

Digital Library

[5]

M. Zuckerman, D. Michael Kuhlman, M. Joireman, and H. Kraft. 1993. Five robust questionnaire scale factors of personality without culture. Personality and Individual Differences. 12, 9, 929--941.

[6]

E. R. Dahlen, R. C. Martin, K. Ragan, and M. M. Kuhlman. 2005. Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving. Accident Analysis and Prevention. 37, 2, 341--348.

[7]

T. Vaa. 2007. Modelling Driver Behaviour on Basis of Emotions and Feelings: Intelligent Transport Systems and Behavioural Adaptations. In Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver Interactions with Intelligent Transport Systems, P. C. Cacciabue (ed.). 208--232.

[8]

H. Summala. 2007. Towards understanding motivational and emotional factors in driver behaviour: Comfort through satisficing. In Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver Interactions with Intelligent Transport Systems, P. C. Cacciabue (ed.). 189--207.

[9]

S. Le Vine, A. Zolfaghari, and J. Polak. 2015. Autonomous cars: The tension between occupant experience and intersection capacity. Transportation Research Part C: Emerging Technologies. 52, 1--14.

[10]

G. Reymond and A. Kemeny. 2000. Motion Cueing in the Renault Driving Simulator. Vehicle System Dynamics. 34, 4, 249--259.

[11]

J. A. Molino, K. S. Opiela, B. J. Katz, and M. J. Moyer. 2005. Validate First; Simulate Later: A New Approach Used at the FHWA Highway Driving Simulator. In Proceedings of Driver Simulation Conference. 411--420.

[12]

S. Baltodano, S. Sibi, N. Martelaro, N. Gowda, and W. Ju. 2015. The RRADS Platform: A Real Road Autonomous Driving Simulator. In 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 281--288.

Digital Library

[13]

J. Karjanto, N. M. Yusof, J. M. B. Terken, F. L. M. Delbressine, M. Z. Hassan, and G. W. M. Rauterberg. 2016. Simulating Autonomous Driving Styles: Accelerations For Three Road Profiles. Manuscript submitted for publication.

[14]

V. Bogdanović, N. Ruškić, Z. Papić, and M. Simeunović. 2013. The research of vehicle acceleration at signalized intersections. Traffic and Transportation. 25, 1, 33--42.

[15]

I. El-Shawarby, H. Rakha, V. Inman, and G. Davis. 2009. Evaluation of Driver Deceleration Behavior at Signalized Intersections. Transportation Research Record: Journal of the Transportation Research Board. 2018, 29--35.

[16]

W. Hugemann and M. Nickel. 2003. Longitudinal and Lateral Accelerations in Normal Day Driving. In 12th EVU Annual Congress.

[17]

transit Cooperative Research Program (TCRP). 2012. TCRP Report 155 -- Track Design Handbook for Light Rail Transit, Second Edition.

[18]

P. A. Weber and J. P. Braaksma. 2000. Towards a North American geometric design standard for speed humps. Institute of Transportation Engineers. 70, 1, 30--34.

[19]

T. A. Louis, P. W. Lavori, J. C. Bailar, and P. Marcia. 1984. Crossover and Self-Controlled Designs in Clinical Research, New England Journal of Medicine. 310, 1, 24--31.

[20]

C. Diels and J. E. Bos. 2016. Self-driving carsickness. Applied Ergonomics. 53, 374--382.

[21]

A. Field. 2009. Discovering Statistics Using SPSS, SAGE Publications, Third Edition.

[22]

T. Lajunen and H. Summala. 1997. Effects of driving experience, personality, driver's skill and safety orientation on speed regulation and accidents. Traffic and Transport Psychology: Theory and Application. 283--294.

Cited By

Xia TChen H(2024)A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User ExpectationsSensors10.3390/s2415480824:15(4808)Online publication date: 24-Jul-2024
https://doi.org/10.3390/s24154808
Schrum MSumner EGombolay MBest A(2024)MAVERIC: A Data-Driven Approach to Personalized Autonomous DrivingIEEE Transactions on Robotics10.1109/TRO.2024.335954340(1952-1965)Online publication date: 2024
https://doi.org/10.1109/TRO.2024.3359543
Schwager RGrimm MLiu XEwecker LBrühl TSohn THohmann S(2024)An Analysis of Driver-Initiated Takeovers during Assisted Driving and their Effect on Driver Satisfaction2024 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55156.2024.10588585(1907-1914)Online publication date: 2-Jun-2024
https://doi.org/10.1109/IV55156.2024.10588585
Show More Cited By

Index Terms

The Exploration of Autonomous Vehicle Driving Styles: Preferred Longitudinal, Lateral, and Vertical Accelerations
1. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

Where is the Best Autonomous Vehicle Interactive Display Place When Meeting a Manual Driving Vehicle in Intersection?
Cross-Cultural Design. Applications in Cultural Heritage, Tourism, Autonomous Vehicles, and Intelligent Agents
Abstract
Autonomous vehicle technology is becoming more and more practical currently. To drive in urban environment safely, autonomous vehicles need to become able to convey the information to other road users, such as pedestrians or driving drivers, and ...
Autonomous vehicle lane-change maneuver accounting for emotion-induced driving behavior in other vehicles
Abstract
Lane-change maneuvers are a critical aspect of autonomous vehicles operation, but executing them efficiently and safely in the presence of other vehicles with varying driving behaviors, influenced by drivers’ emotions, poses a significant ...
An analysis of vehicle-to-infrastructure communications for non-signalised intersection control under mixed driving behaviour

Intersection control has an important role in the management of urban traffic to ensure safety, high traffic flow and to prevent congestion. Recently, a growing body of literature has been reported on the theme of non-signalised intersection control in ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

Automotive'UI 16: Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

October 2016

296 pages

ISBN:9781450345330

DOI:10.1145/3003715

General Chair:
Paul Green,
Program Chairs:
Susanne Boll,
Joe Gabbard,
Sebastian Osswald,
Gary Burnett,
Publications Chairs:
Shadan Sadeghian Borojeni,
Andreas Löcken,
Anuj Pradhan

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction
University of Michigan: University of Michigan

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

AutomotiveUI'16

AutomotiveUI'16: 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

October 24 - 26, 2016

MI, Ann Arbor, USA

Acceptance Rates

Automotive'UI 16 Paper Acceptance Rate 39 of 85 submissions, 46%;

Overall Acceptance Rate 248 of 566 submissions, 44%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

54
Total Citations
View Citations
1,024
Total Downloads

Downloads (Last 12 months)120
Downloads (Last 6 weeks)9

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Xia TChen H(2024)A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User ExpectationsSensors10.3390/s2415480824:15(4808)Online publication date: 24-Jul-2024
https://doi.org/10.3390/s24154808
Schrum MSumner EGombolay MBest A(2024)MAVERIC: A Data-Driven Approach to Personalized Autonomous DrivingIEEE Transactions on Robotics10.1109/TRO.2024.335954340(1952-1965)Online publication date: 2024
https://doi.org/10.1109/TRO.2024.3359543
Schwager RGrimm MLiu XEwecker LBrühl TSohn THohmann S(2024)An Analysis of Driver-Initiated Takeovers during Assisted Driving and their Effect on Driver Satisfaction2024 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55156.2024.10588585(1907-1914)Online publication date: 2-Jun-2024
https://doi.org/10.1109/IV55156.2024.10588585
Peintner JHimmels CRock TManger CJung ORiener A(2024)Driving Behavior Analysis: A Human Factors Perspective on Automated Driving Styles2024 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55156.2024.10588527(3312-3317)Online publication date: 2-Jun-2024
https://doi.org/10.1109/IV55156.2024.10588527
Neumeier MDorn SBotsch MUtschick W(2024)Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00350(3461-3470)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00350
Yi BCao HSong XGuo WWang JLi M(2024)Will we as Passengers Use Highly Automated Vehicles? Examining the Importance of Role Adaptation on People’s Acceptance of the AutomationInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2372892(1-13)Online publication date: 5-Jul-2024
https://doi.org/10.1080/10447318.2024.2372892
Horn SRossner PMadigan RBieg HMarberger CAlt POtto HSchulz MSchultz AKenar EBullinger AMerat N(2024)User evaluation of comfortable deceleration profiles for highly automated driving: Findings from a test track studyTransportation Research Part F: Traffic Psychology and Behaviour10.1016/j.trf.2024.05.025105(206-221)Online publication date: Aug-2024
https://doi.org/10.1016/j.trf.2024.05.025
Zang YWen LCai PFu DMao SShi BLi YLu G(2024)How drivers perform under different scenarios: Ability-related driving style extraction for large-scale datasetAccident Analysis & Prevention10.1016/j.aap.2023.107445196(107445)Online publication date: Mar-2024
https://doi.org/10.1016/j.aap.2023.107445
Chen WZheng RJiang JTian ZZhang FLiu Y(2024)EDSD: efficient driving scenes detection based on Swin TransformerMultimedia Tools and Applications10.1007/s11042-024-19622-w83:39(87179-87198)Online publication date: 20-Jul-2024
https://doi.org/10.1007/s11042-024-19622-w
Buzdugan IButnariu SRoșu IPridie AAntonya C(2023)Personalized Driving Styles in Safety-Critical Scenarios for Autonomous Vehicles: An Approach Using Driver-in-the-Loop SimulationsVehicles10.3390/vehicles50300645:3(1149-1166)Online publication date: 12-Sep-2023
https://doi.org/10.3390/vehicles5030064
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents