Skip to main content

Advertisement

SpringerLink
Log in

Effect of risk-predictive haptic guidance in one-pedal driving mode

  • Original Article
  • Published:
Cognition, Technology & Work Aims and scope Submit manuscript

Abstract

The research presented in this article focuses on the design of a driver support system for risk-predictive driving under a potentially hazardous situation for a pedestrian who crosses a road from the driver’s blind spots. Our aim is to develop a system that would cooperate with the driver in leading the normative speed calculated by the co-driver function. The design philosophy of haptic guidance is to communicate to the drivers the potentially hazardous situation through tactile cues from the active gas pedal and to assist drivers to in preparing for possible road surprises. We intended to combine the algorithm of the haptic feedback loop with the functionality of the one-pedal driving mode interface. Three design issues for the haptic guidance system can be distinguished: the design of a one-pedal driving mode based on a one-pedal operation; the modeling of risk-predictive driving behavior; and the haptic feedback algorithm with active gas pedal. We tested our system in human-in-the-loop experiments in a driving simulator to investigate (1) the effect of the one-pedal driving mode interface on the driver behavior and (2) the effect of haptic guidance support on the driver behavior. From the results of our experiments, we confirmed that haptic guidance can improve the risk-predictive driving performance for a slowdown task via the one-pedal driving mode.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • AAA-Foundation (2006) How risky is it? An assessment of the relative risk of engaging in potentially unsafe driving behaviors. Technical report

  • Abbink DA, Mulder M, Boer ER (2012) Haptic shared control: smoothly shifting control authority? Cogn Technol Work 14:19–28

    Google Scholar 

  • Adell E, Várhelyi A, Hjälmdahl M (2008) Auditory and haptic systems for in-car speed management—a comparative real life study. Transp Res Part F Traffic Psychol Behav 11:445–458

    Article  Google Scholar 

  • Akagi Y, Raksincharoensak P (2015) Stochastic driver speed control behavior modeling in urban intersections using risk potential-based motion planning framework. In: 2015 IEEE intelligent vehicles symposium (IV), pp 368–373

  • Cacciabue PC, Saad F (2008) Behavioural adaptations to driver support systems: a modelling and road safety perspective. Cogn Technol Work 10:31–39

    Article  Google Scholar 

  • Centre, OECD/ECMT Transport Research (2006) Speed management report. Technical report, Paris

  • Endsley MR, Kiris EO (1995) The out-of-the-loop performance problem and level of control in automation. Human Factors 37:381–394

    Article  Google Scholar 

  • Flemisch FO, Abbink DA, Itoh M, Pacaux-Lemoine M-P, Weßel G (2016) Shared control is the sharp end of cooperation: towards a common framework of joint action, shared control and human machine cooperation. IFAC Pap OnLine 49:72–77

    Article  Google Scholar 

  • Flemisch FO, Bengler K, Bubb H, Winner H, Bruder R (2014) Towards cooperative guidance and control of highly automated vehicles: H-mode and conduct-by-wire. Ergonomics 57:343–360

    Article  Google Scholar 

  • GmbH DEKRA Automobil (2015) Strategies for preventing accidents on European roads. Technical report, ROAD SAFETY REPORT 2015 A future based on experience

  • Hart SG, Staveland LE (1988) Development of NASA-TLX (task load index): results of empirical and theoretical research. Adv Psychol 52:139–183

    Article  Google Scholar 

  • Hollnagel E, Woods DD (2005) Joint cognitive systems: foundations of cognitive systems engineering. Taylor & Francis, Boca Raton

    Book  Google Scholar 

  • Toshiyuki I, Makoto I (2013) Human’s overtrust in and overreliance on advanced driver assistance systems: a theoretical framework. Int J Vehic Technol 951762:8

    Google Scholar 

  • Inoue H, Raksincharoensak P, Inoue S (2017) Intelligent driving system for safer automobiles. J Inf Process 25:32–43

    Google Scholar 

  • Ito T, Mio M, Tohriyama K, Kamata M (2015) Novel map platform based on primitive elements of traffic environments for automated driving technologies. In: Proceedings of the FAST-zero’15: 3rd international symposium on future active safety technology toward zero traffic accidents 2015, vol 361, pp 361–368

  • Ito T, Shino T, Kamata M (2017) Information sharing to improve understanding of proactive braking intervention for elderly drivers. Int J Intell Transp Syst Res 16:173

    Google Scholar 

  • Jamson H, Hibberd DL, Merat N (2013) The design of haptic gas pedal feedback to support eco-driving. In: Seventh international driving symposium on human factors in driver assessment, training, and vehicle design, pp 264–270

  • Laan JD, Der V, Heino A, De Waard D (1997) A simple procedure for the assessment of acceptance of advanced transport telematics. Transp Res Part C Emerg Technol 5:1–10

    Article  Google Scholar 

  • McDermott R (2010) Decision making under uncertainty. In: Proceedings of a workshop on deterring cyberattacks: informing strategies and developing options for US Policy. The National Academies Press

  • Merat N, Jamson AH (2009) How do drivers behave in a highly automated car? In: International driving symposium on human factors in driver assessment, training, and vehicle design, vol 5, pp 514–521

  • Mulder M, Abbink DA, van Paassen MM, Mulder M (2011) Design of a haptic gas pedal for active car-following support. IEEE Trans Intell Transp Syst 12:268–279

    Article  Google Scholar 

  • Mulder M, Pauwelussen JJA, van Paassen MM, Mulder M, Abbink DA (2010) Active deceleration support in car following. IEEE Trans Syst Man Cyber Part A Syst Hum 40:1271–1284

    Article  Google Scholar 

  • NPA (2017) Traffic accidents situation in 2016. Technical report, National Police Agency of JAPAN

  • OECD (1990) Behavioural adaptations to changes in the road transport system. Organization for Economic Co-operation and Development, Paris

  • Petermeijer SM, Abbink DA, Mulder M, de Winter JCF (2015) The effect of haptic support systems on driver performance: a literature survey. IEEE Trans Haptics 8:467–479

    Article  Google Scholar 

  • Raksincharoensak P, Inoue H (2017) Safety cushion: context-sensitive hazard anticipation-objectified driving behavior of experienced and careful drivers for developing context-sensing driving assistance systems. In: FAST-zero’17: 4th international symposium on future active safety technology toward zero traffic accidents, 20174609 (2017)

  • Rankavat S, Tiwari G (2016) Pedestrians perceptions for utilization of pedestrian facilities—Delhi, India. Transp Res Part F Traffic Psychol Behav 42:495–499

    Article  Google Scholar 

  • Saito Y, Raksincharoensak P (2016a) Shared control in risk predictive braking maneuver for preventing collisions with pedestrians. IEEE Trans Intell Veh 1:314–324

    Article  Google Scholar 

  • Saito Y, Raksincharoensak P (2016b) A shared control in risk predictive braking maneuver for preventing collision with pedestrian. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC), pp 685–690

  • Sarter NB, Woods DD (1995) How in the world did we ever get into that mode? Mode error and awareness in supervisory control. Hum Factors 37:5–19

    Article  Google Scholar 

  • Takae Y, Seto Y, Yamamura T, Sugano T, Kobayashi M, Sato K (2009) Development and evaluation of a distance control assist system with an active accelerator pedal. SAE Int J Passeng Cars Electron Electr Syst 2:46–55

    Article  Google Scholar 

  • Verwey WB, Alm H, Groeger JA, Janssen WH, Kuiken MJ, Schraagen JM, Schumann J, vanWinsum W, Wontorra H (1993) Gids functions. In: Michon JA (eds) Generic intelligent driver support. Taylor&Francis, New York

  • Vlassenroot S, Broekx S, De Mol J, Panis LI, Brijs T, Wets G (2007) Driving with intelligent speed adaptation: final results of the Belgian ISA-trial. Transp Res Part A Policy Pract 41:267–279

    Article  Google Scholar 

  • Young MS, Birrell SA, Stanton NA (2011) Safe driving in a green world: a review of driver performance benchmarks and technologies to support ‘smart’ driving. Appl Ergon 42:533–539

    Article  Google Scholar 

Download references

Acknowledgements

This study has been conducted as a part of the research project “Autonomous Driving Intelligence System to Enhance Safe and Secured Traffic Society for Elderly Drivers” granted by Japan Science and Technology Agency. The authors would like to thank the agency for providing financial support.

Author information

Authors and Affiliations

  1. Faculty of Engineering, Information and Systems, University of Tsukuba, Ibaraki, 305-8573, Japan

    Y. Saito

  2. Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-8588, Japan

    P. Raksincharoensak

Authors
  1. Y. Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Raksincharoensak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Y. Saito.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saito, Y., Raksincharoensak, P. Effect of risk-predictive haptic guidance in one-pedal driving mode. Cogn Tech Work 21, 671–684 (2019). https://doi.org/10.1007/s10111-019-00558-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10111-019-00558-3

Keywords

Search

Navigation