The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
권호 표지
DOI QR코드

DOI QR Code

Comparing the Effects of Visual and Visual-auditory Feedback on Eco-driving and Driving Workload

시각적 피드백과 시각-청각적 피드백이 에코 드라이빙과 운전부하에 미치는 상대적 효과

  • 이계훈 (중앙대학교 심리서비스 대학원) ;
  • 임성준 (중앙대학교 심리학과) ;
  • 오세진 (중앙대학교 심리학과)
  • Received : 2017.05.17
  • Accepted : 2017.06.19
  • Published : 2017.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Recent studies have suggested that providing in-vehicle feedback on various driving behaviors promote eco-friendly driving behaviors. However, there was relatively little interest in cognitive overload that can be caused by the provision of information. Thus, the goal of this study was to investigate the relative effects of two types of feedback(visual feedback vs. visual-auditory feedback) to increase eco-driving performance while minimizing driving workload. Also, in this study, the complexity of the driving task was distinguished (secondary vs. tertiary task) in order to reflect the actual driving situation. The study adopted a counterbalancing design in which the two feedback types were delivered in a different order under the two different task conditions. Results showed that providing the visual-auditory feedback was more effective than the visual only feedback in both promoting eco-friendly driving behaviors and minimizing driving workload under both task conditions.

최근 차량 내 정보 제공 장비를 통한 에코 드라이빙의 향상이 연료 효율과 안전 운전을 증가시킬 수 있다는 연구들이 보고되고 있다. 그러나 정보의 제공으로 인하여 야기될 수 있는 인지적 부하에 대한 관심은 상대적으로 적은 편이다. 본 연구는 에코 드라이빙을 향상시킴과 동시에 운전자의 운전부하를 최소화 할 수 있는 차량 내 정보 제공 장비의 특성을 확인하기 위해 두(시각vs.시청각) 피드백의 상대적인 효과 차이와 운전 중 상황의 복잡성 수준에 따른 정보 제공방식의 차이가 운전 행동과 운전부하에 미치는 효과를 알아보았다. 본 실험에는 총 38명의 운전자가 참가하였다. 연구 결과, 시각-청각 피드백의 제공이 시각적 피드백을 제공하는 조건에 비하여 에코 드라이빙을 더 향상시키며, 운전부하를 최소화하였다.

Keywords

References

  1. Alavosius M. P. and Sulzer‐Azaroff B.(1990), "Acquisition and maintenance of health‐care routines as a function of feedback density," Journal of Applied Behavior Analysis, vol. 23, no. 1, pp.151-162. https://doi.org/10.1901/jaba.1990.23-151
  2. Azzi S., Reymond G., Merienne F. and Kemeny A.(2011), "Eco-driving performance assessment with in-car visual and haptic feedback assistance," Journal of Computing and Information Science in Engineering, vol. 11, no. 4, pp.181-190.
  3. Beusen B., Broekx S., Denys T., Beckx C., Degraeuwe B., Gijsbers M., Scheepers K., Govaerts L., Torfs R. and Panis L. I.(2009), "Using on-board logging devices to study the longer-term impact of an eco-driving course," Transportation Research Part D: Transport and Environment, vol. 14, no. 7, pp.514-520. https://doi.org/10.1016/j.trd.2009.05.009
  4. Birrell S. A. and Young M. S.(2011), "The impact of smart driving aids on driving performance and driver distraction," Transportation Research Part F: Traffic Psychology and Behaviour, vol. 14, no. 6, pp.484-493. https://doi.org/10.1016/j.trf.2011.08.004
  5. Boriboonsomsin K., Vu A. and Barth M.(2010), Eco-driving: Pilot evaluation of driving behavior changes among US drivers, University of California Transportation Center. pp.1-18.
  6. Daniels A. C. and Daniels J. E.(2004), Performance management: Changing behavior that drives organizational effectiveness (4th ed.). GA: Aubrey Daniels International(Atlanta), pp.157-170.
  7. Jamson A. H., Hibberd D. L. and Merat N.(2015), "Interface design considerations for an in-vehicle eco-driving assistance system," Transportation Research Part C: Emerging Technologies, vol. 58, pp.642-656. https://doi.org/10.1016/j.trc.2014.12.008
  8. Jeong J. H. and Lee S. K.(2013), "Estimating the Effect of VMS on Drivers' Legibility and Perception," The Journal of the Korea Contents Association, vol. 13, no. 11, pp.944-956. https://doi.org/10.5392/JKCA.2013.13.11.944
  9. Joo Y. K. and Lee J. E. R.(2014), "Can "The Voices in the Car" Persuade Drivers to Go Green?: Effects of Benefit Appeals from In-Vehicle Voice Agents and the Role of Drivers' Affective States on Eco-Driving," Cyberpsychology, Behavior, and Social Networking, vol. 17, no. 4, pp.255-261. https://doi.org/10.1089/cyber.2013.0157
  10. Klauer S. G., Dingus T. A., Neale V. L., Sudweeks J. D. and Ramsey D. J.(2006), The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data, National Highway Traffic Safety Administration(Washington, DC), pp.25-127.
  11. Lee K. H., Choi S. J., Choi I. S. and Oah S. Z.(2011), "The Effects of Self-management Technique on Eco-driving Behaviors," Korean Journal of Psychological and Social Issues, vol. 17, no. 4, pp.381-393.
  12. Liu Y. C.(2001), "Comparative study of the effects of auditory, visual and multimodality displays on drivers' performance in advanced traveller information systems," Ergonomics, vol. 44, no. 4, pp.425-442. https://doi.org/10.1080/00140130010011369
  13. Ministry of Environment(2017), Business plan reporting, pp.1-30.
  14. Pauzie A.(2008), "A method to assess the driver mental workload: The driving activity load index (DALI)," IET Intelligent Transport Systems, vol. 2, no. 4, pp.315-322. https://doi.org/10.1049/iet-its:20080023
  15. Rouzikhah H., King M. and Rakotonirainy A.(2013), "Examining the effects of an eco-driving message on driver distraction," Accident Analysis & Prevention, vol. 50, pp.975-983. https://doi.org/10.1016/j.aap.2012.07.024
  16. Taniguchi M.(2008), "Eco-driving and fuel economy of passenger cars," In Proc. of Annual Meeting of IEE Japan S, vol. 21, pp.5-8.
  17. The Korea Transport Institute(2009), Review of Eco-driving Policy in Advanced Countries and Its Implication. pp.1-85.
  18. US Department of Energy Energy Information Administration: U. EIA(2012), Annual energy review 2011. pp.303-319.
  19. Wickens C. D.(2002), "Multiple resources and performance prediction," Theoretical Issues In Ergonomics Science, vol. 3, no. 2, pp.159-177. https://doi.org/10.1080/14639220210123806
  20. Young M. S., Birrell S. A. and Stanton N. A.(2011), "Safe driving in a green world: A review of driver performance benchmarks and technologies to support 'smart'driving," Applied Ergonomics, vol. 42, no. 4, pp.533-539. https://doi.org/10.1016/j.apergo.2010.08.012
  21. Zeitlin L. R.(1995), "Estimates of driver mental workload: A long-term field trial of two subsidiary tasks," Human Factors, vol. 37, no. 3, pp.611-621. https://doi.org/10.1518/001872095779049327

Cited by

  1. 딥러닝 기법을 이용한 차량 연료차단 주행의 감지법 vol.10, pp.11, 2017, https://doi.org/10.15207/jkcs.2019.10.11.327