Abstract
Developing feedback systems that can detect the attention level of the driver can play a key role in preventing accidents by alerting the driver about possible hazardous situations. Monitoring drivers’ distraction is an important research problem, especially with new forms of technology that are made available to drivers. An important question is how to define reference labels that can be used as ground truth to train machine-learning algorithms to detect distracted drivers. The answer to this question is not simple since drivers are affected by visual, cognitive, auditory, psychological, and physical distractions. This chapter proposes to define reference labels with perceptual evaluations from external evaluators. We describe the consistency and effectiveness of using a visual-cognitive space for subjective evaluations. The analysis shows that this approach captures the multidimensional nature of distractions. The representation also defines natural modes to characterize driving behaviors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
M. C. F. Aguilo, Development of guidelines for in-vehicle information presentation: text vs. speech, Master’s thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, May 2004
P. Angkititrakul, D. Kwak, S. Choi, J. Kim, A. Phucphan, A. Sathyanarayana, and J.H.L. Hansen, Getting start with UTDrive: driver-behavior modeling and assessment of distraction for in-vehicle speech systems, in Interspeech 2007, Antwerp, Belgium, August 2007, pp. 1334–1337
A. Azman, Q. Meng, E. Edirisinghe, Non intrusive physiological measurement for driver cognitive distraction detection: Eye and mouth movements. In International Conference on Advanced Computer Theory and Engineering (ICACTE 2010), vol. 3, Chengdu, China, August 2010
K. M. Bach, M.G. Jaeger, M.B. Skov, and N.G. Thomassen, Interacting with in-vehicle systems: understanding, measuring, and evaluating attention. In Proceedings of the 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology, Cambridge, United Kingdom, September 2009
M.S. Bartlett, G.C. Littlewort, M.G. Frank, C. Lainscsek, I. Fasel, J.R. Movellan, Automatic recognition of facial actions in spontaneous expressions. J. Multimedia 1, 22–35 (2006)
C. Busso, J. Jain, Advances in multimodal tracking of driver distraction, in Digital Signal Processing for In-Vehicle Systems and Safety, ed. by J. Hansen, P. Boyraz, K. Takeda, H. Abut (Springer, New York, NY, 2011), pp. 253–270
Y. Dong, Z. Hu, K. Uchimura, N. Murayama, Driver inattention monitoring system for intelligent vehicles: a review. IEEE Trans. Intel. Trans. Syst. 12(2), 596–614 (2011)
F.A. Drews, M. Pasupathi, D.L. Strayer, Passenger and cell phone conversations in simulated driving. J. Exp. Psychol. Appl. 14(4), 392–400 (2008)
J. Engström, E. Johansson, J. Östlund, Effects of visual and cognitive load in real and simulated motorway driving. Trans. Res. Part F Traffic Psychol. Behav. 8(2), 97–120 (2005)
T. Ersal, H.J.A. Fuller, O. Tsimhoni, J.L. Stein, H.K. Fathy, Model-based analysis and classification of driver distraction under secondary tasks. IEEE Trans. Intel. Trans. Syst. 11(3), 692–701 (2010)
J.P. Foley, Now you see it, now you dont: visual occlusion as a surrogate distraction measurement technique, in Driver Distraction: Theory Effects, and Mitigation, ed. by M.A. Regan, J.D. Lee, K.L. Young (CRC Press, Boca Raton, FL, 2008), pp. 123–134
A. L. Glaze, J.M. Ellis, Pilot study of distracted drivers. Technical report, Transportation and Safety Training Center, Virginia Commonwealth University, Richmond, VA, USA, January 2003
P. Green, The 15-second rule for driver information systems. In Intelligent Transportation Society (ITS) America Ninth Annual Meeting, Washington, DC, USA, April 1999
J.L. Harbluk, Y.I. Noy, P.L. Trbovich, M. Eizenman, An on-road assessment of cognitive distraction: impacts on drivers’ visual behavior and braking performance. Accid. Anal. Prev. 39(2), 372–379 (2007)
J. Jain, C. Busso. Analysis of driver behaviors during common tasks using frontal video camera and CAN-Bus information. In IEEE International Conference on Multi media and Expo (ICME 2011), Barcelona, Spain, July 2011
J.J. Jain, C. Busso. Assessment of driver’s distraction using perceptual evaluations, self assessments and multimodal feature analysis. In 5th Biennial Workshop on DSP for In-Vehicle Systems, Kiel, Germany, September 2011
S.G. Klauer, T.A. Dingus, V.L. Neale, J.D. Sudweeks, D.J. Ramsey, The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data. Technical Report DOT HS 810 594, National Highway Traffic Safety Administration, Blacksburg, VA, USA, April 2006
J.D. Lee, B. Caven, S. Haake, T.L. Brown, Speech-based interaction with in-vehicle computers: the effect of speech-based e-mail on drivers’ attention to the road-way. Hum. Factors 43(4), 631–640 (Winter 2001)
J.D. Lee, D.V. McGehee, T.L. Brown, M.L. Reyes, Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator. Hum. Factors 44, 314–334 (Summer 2002)
Y. Liang, M.L. Reyes, J.D. Lee, Real-time detection of driver cognitive distraction using support vector machines. IEEE Trans. Intel. Trans. Syst. 8(2), 340–350 (2007)
S. Mattes, A. Hallén, Surrogate distraction measurement techniques: the lane change test, in Driver Distraction: Theory, Effects, and Mitigation, ed. by M.A. Regan, J.D. Lee, K.L. Young (CRC Press, Boca Raton, FL, 2008), pp. 107–122
J.C. McCall, M.M. Trivedi, Driver behavior and situation aware brake assistance for intelligent vehicles. Proc. IEEE 95(2), 374–387 (2007)
B. Mehler, B. Reimer, J.F. Coughlin, J.A. Dusek, Impact of incremental increases in cognitive workload on physiological arousal and performance in young adult drivers. Trans. Res. Record 2138, 6–12 (2009)
V. Neale, T. Dingus, S. Klauer, J. Sudweeks, M. Goodman, An overview of the 100-car naturalistic study and findings. Technical Report Paper No. 05-0400, National Highway Traffic Safety Administration, June 2005
W. Piechulla, C. Mayser, H. Gehrke, W. Koenig, Reducing drivers’ mental work-load by means of an adaptive man–machine interface. Trans. Res. Part F Traffic Psychol. Behav. 6(4), 233–248 (2003)
F. Putze, J.-P. Jarvis, T. Schultz, Multimodal recognition of cognitive workload for multitasking in the car. In International Conference on Pattern Recognition (ICPR 2010), Istanbul, Turkey, August 2010
T.A. Ranney, Driver distraction: a review of the current state-of-knowledge. Technical Report DOT HS 810 787, National Highway Traffic Safety Administration, April 2008
T.A. Ranney, W.R. Garrott, M.J. Goodman, NHTSA driver distraction research: past, present, and future. Technical Report Paper No. 2001-06-0177, National High- way Traffic Safety Administration, June 2001
E.M. Rantanen, J.H. Goldberg, The effect of mental workload on the visual field size and shape. Ergonomics 42(6), 816–834 (1999)
M.A. Recarte, L.M. Nunes, Mental workload while driving: effects on visual search, discrimination, and decision making. J. Exp. Psychol. Appl. 9(2), 119–137 (2003)
A. Sathyanarayana, S. Nageswaren, H. Ghasemzadeh, R. Jafari, J.H.L. Hansen, Body sensor networks for driver distraction identification. In IEEE International Conference on Vehicular Electronics and Safety (ICVES 2008), Columbus, OH, USA, September 2008
D.L. Strayer, J.M. Cooper, F.A. Drews. What do drivers fail to see when conversing on a cell phone? In Proceedings of Human Factors and Ergonomics Society Annual Meeting, volume 48, New Orleans, LA, USA, September 2004
D.L. Strayer, J.M. Watson, F.A. Drews, Cognitive distraction while multitasking in the automobile, in The Psychology of Learning and Motivation, ed. by B.H. Ross, vol. 54 (Academic, Burlington, MA, 2011), pp. 29–58
J.C. Stutts, D.W. Reinfurt, L. Staplin, E.A. Rodgman, The role of driver distraction in traffic crashes. Technical report, AAA Foundation for Traffic Safety, Washington, DC, USA, May 2001
F. Tango, M. Botta, Evaluation of distraction in a driver-vehicle-environment framework: an application of different data-mining techniques, in Advances in Data Mining. Applications and Theoretical Aspects, ed. by P. Perner. Lecture Notes in Computer Science, vol. 5633 (Springer, Berlin, 2009), pp. 176–190
T.W. Victor, J. Engstroem, J.L. Harbluk, Distraction assessment methods based on visual behavior and event detection, in Driver Distraction: Theory, Effects, and Mitigation, ed. by M.A. Regan, J.D. Lee, K.L. Young (CRC Press, Boca Raton, FL, 2008), pp. 135–165
W. Wierwille, L. Tijerina, S. Kiger, T. Rockwell, E. Lauber, A. Bittner Jr, Final report supplement—task 4: review of workload and related research. Technical Report DOT HS 808 467, U.S. Department of Transportation, National Highway Traffic Safety Administration, Washington, DC, USA, October 1996
Q. Wu, An overview of driving distraction measure methods. In IEEE 10th International Conference on Computer-Aided Industrial Design & Conceptual Design (CAID CD 2009), Wenzhou, China, November 2009
K.L. Young, M.A. Regan, J.D. Lee, Measuring the effects of driver distraction: direct driving performance methods and measures, in Driver Distraction: Theory Effects, and Mitigation, ed. by M.A. Regan, J.D. Lee, K.L. Young (CRC Press, Boca Raton, FL, 2008), pp. 85–105
Y. Zhang, Y. Owechko, J. Zhang. Driver cognitive workload estimation: a data-driven perspective. In IEEE Intelligent Transportation Systems, Washington, DC, USA, October 2004, pp. 642–647
Acknowledgment
The authors would like to thank Dr. John Hansen for his support with the UTDrive Platform. We want to thank the Machine Perception Lab (MPLab) at The University of California, San Diego, for providing the CERT software. The authors are also thankful to Ms. Rosarita Khadij M Lubag for her support and efforts with the data collection.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this chapter
Cite this chapter
Li, N., Busso, C. (2014). Using Perceptual Evaluation to Quantify Cognitive and Visual Driver Distractions. In: Schmidt, G., Abut, H., Takeda, K., Hansen, J. (eds) Smart Mobile In-Vehicle Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-9120-0_11
Download citation
DOI: https://doi.org/10.1007/978-1-4614-9120-0_11
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-9119-4
Online ISBN: 978-1-4614-9120-0
eBook Packages: EngineeringEngineering (R0)