Utilization of Visual Information Perception Characteristics to Improve Classification Accuracy of Driver’s Visual Search Intention for Intelligent Vehicle

International Journal of Human-Computer Interaction

Volumn 31, Issue 10, 2015, Pages 717-729

  Kim, Ji Hoon ^a   Lim, Ji Hyoun ^b   Jo, Chun Ik ^c   Kim, Kyungdoh ^a  

^a Hong Ik University   (South Korea)

^b Ulsan National Institute of Science and Technology   (South Korea)

^c ICT and Future Planning   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; COGNITIVE SYSTEMS; K-MEANS CLUSTERING; PREDICTIVE ANALYTICS;

CLASSIFICATION ACCURACY; CLASSIFICATION MODELS; INFLUENTIAL FACTORS; K-MEANS CLUSTERING METHOD; OBSERVABLE BEHAVIOR; PSYCHOLOGICAL EXPERIMENT; SELECTIVE ATTENTION; VISUAL INFORMATION PROCESSING;

EYE MOVEMENTS;

EID: 84943377546     PISSN: 10447318     EISSN: 15327590     Source Type: Journal    
DOI: 10.1080/10447318.2015.1070561     Document Type: Article

References (57)

1. J.Aasman (1995). Modelling driver behaviour in soar (Unpublished doctoral dissertation). Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, the Netherlands.
2. M.Augustinova,, & L.Ferrand, (2014). Social priming of dyslexia and reduction of the Stroop effect: What component of the Stroop effect is actually reduced? Cognition, 130, 442–454.
3. K.M.Bach,, M.G.Jæger,, M.B.Skov,, & N.G.Thomassen, (2009). 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, 453–462.
4. J.A.Bastiaansen,, A.M.van Roon,, J.K.Buitelaar,, & A.J.Oldehinkel, (2015). Mental health problems are associated with low-frequency fluctuations in reaction time in a large general population sample. The TRAILS study. European Psychiatry, 30, 347–353.
5. L.Bi,, M.Wang,, C.Wang,, & Y.Liu, (2015). Development of a driver lateral control model by integrating neuromuscular dynamics into the queuing network-based driver model. IEEE Transactions on Intelligent Transportation Systems, 99. doi:10.1109/TITS.2015.2409115
6. R.Bishop, (2000). Intelligent vehicle applications worldwide. IEEE Intelligent Systems and their Applications, 15, 78–81.
7. L.N.Boyle,, J.D.Lee,, Y.Peng,, M.Ghazizadeh,, Y.Wu,, E.Miller,, & J.Jenness, (2013). Text reading and text input assessment in support of the NHTSA visual-manual driver distraction guidelines (No. DOT HS 811 820).
8. K.A.Brookhuis,, D.De Waard,, & W.H.Janssen, (2001). Behavioural impacts of advanced driver assistance systems–an overview. European Journal of Transport and Infrastructure Research, 1, 245–253.
9. S.K.Card,, T.P.Moran,, & A.Newell, (1983). The psychology of human–computer interaction. Hillsdale, NJ: Erlbaum.
10. C.Castro, (2009) Visual demands and driving. In C.Castro (Ed.), Human factors of visual and cognitive performance in driving (pp. 1–29). New York, NY: Taylor & Francis.
11. F.C.Donders, (1969). On the speed of mental processes. Acta Psychologica, 30, 412–431.
12. M.Doyle,, & R.Walker, (2001). Curved saccade trajectories: Voluntary and reflexive saccades curve away from irrelevant distractors. Experimental Brain Research, 139(3), 333–344.
13. M.R.Endsley, (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37, 32–64.
14. R.Feyen, (2002). Modeling human performance using the Queueing Network Model Human Processor (QN-MHP) (Unpublished doctoral dissertation). Ann Arbor, MI: Department of Industrial and Operations Engineering, University of Michigan.
15. J.H.Goldberg, (2014). Measuring software screen complexity: relating eye tracking, emotional valence, and subjective ratings. International Journal of Human–Computer Interaction, 30, 518–532.
16. J.H.Goldberg,, & X.P.Kotval, (1999). Computer interface evaluation using eye movements: Methods and constructs. International Journal of Industrial Ergonomics, 24, 631–645.
17. J.H.Hair,, Jr., & W.C.Black, (2000). Cluster analysis. In L.G.Grimm & P.R.Yarnold (Eds.), Reading and understanding more multivariate statistics (pp. 147–205). Washington, DC: American Psychological Association.
18. P.A.Hancock,, & R.Parasuraman, (1992). Human factors and safety in the design of intelligent vehicle-highway systems (IVHS). Journal of Safety Research, 23, 181–198.
19. S.Harper,, E.Michailidou,, & R.Stevens, (2009). Toward a definition of visual complexity as an implicit measure of cognitive load. ACM Transactions on Applied Percepttion, 6, 1–18.
20. B.L.Hills, (1980). Vision, visibility, and perception in driving. Perception, 9, 183–216.
21. T.Hummel,, M.Kühn,, J.Bende,, & A.Lang, (2011). Advanced driver assistance systems: an investigation of their potential safety benefits based on an analysis of insurance claims in Germany. German Insurance Association Insurers Accident Research. Research Report FS, 3.
22. R.J.Jacob,, & K.S.Karn, (2003). Eye tracking in human–computer interaction and usability research: Ready to deliver the promises. In R.Radach, J.Hyona, & H.Deubel (Eds.), The mind’s eye: Cognitive and applied aspects of eye movement research (pp. 573–605). Amsterdam, the Netherlands: North-Holland.
23. J.H.Kim, M.S.Ha,, & B.H.Kim, (2011). The clustering and Big Five personality in discriminating the groups by the level of school-related adjustment. Studies on Korean Youth, 22, 149–174.
24. J.Kim,, C.Jo,, & J.Lim, (2013, May). An experimental study on eye movements under different task goal during driving. Paper presented at the spring conference of the Korean Society for Cognitive Science, Seoul, Korea.
25. S.Laapotti,, & E.Keskinen, (2004). Has the difference in accident patterns between male and female drivers changed between 1984 and 2000? Accident Analysis & Prevention, 36, 577–584.
26. S.J.Landry,, T.B.Sheridan,, & Y.M.Yufik, (2001). A methodology for studying cognitive groupings in a target-tracking task. IEEE Transactions on Intelligent Transportation Systems, 2, 92–100.
27. J.D.Lee, (2007). Technology and teen drivers. Journal of Safety Research, 38, 203–213.
28. M.F.Lesch,, & P.A.Hancock, (2004). Driving performance during concurrent cell-phone use: Are drivers aware of their performance decrements?. Accident Analysis & Prevention, 36, 471–480.
29. J.Lim,, & J.Kim, (2014). A classification model of driver’s objectives for information search using eye movement behaviors. Paper presented at the conference of the Korean Society of CAD/CAM Engineers, Pyeong Chang, Korea, February 2014.
30. J.H.Lim,, & Y.Liu, (2009). Reinforcement learning in eye movement: Modeling the influences of top-down and bottom-up processes. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 39, 706–714.
31. J.H.Lim,, O.Tsimhoni,, & Y.Liu, (2010). Investigation of driver performance with night vision and pedestrian detection systems—Part I: Empirical study on visual clutter and glance behavior. IEEE Transactions on Intelligent Transportation Systems, 11, 670–677.
32. C.Little, (1997). The intelligent vehicle initiative: advancing “human-centered” smart vehicles. Public Roads, 61(2), 18–25.
33. Y.Liu,, R.Feyen,, & O.Tsimhoni, (2006). Queueing Network-Model Human Processor (QN-MHP): A computational architecture for multitask performance in human-machine systems. ACM Transactions on Computer-Human Interaction, 13, 37–70.
34. H.S.Lonczak,, C.Neighbors,, & D.M.Donovan, (2007). Predicting risky and angry driving as a function of gender. Accident Analysis & Prevention, 39, 536–545.
35. M.Lu,, K.Wevers,, & R.Van Der Heijden, (2005). Technical feasibility of advanced driver assistance systems (ADAS) for road traffic safety. Transportation Planning and Technology, 28, 167–187.
36. L.Ma,, & X.Yan, (2014). Examining the nonparametric effect of drivers’ age in rear-end accidents through an additive logistic regression model. Accident Analysis & Prevention, 67, 129–136.
37. H.Makishita,, & K.Matsunaga, (2008). Differences of drivers’ reaction times according to age and mental workload. Accident Analysis & Prevention, 40, 567–575.
38. R.L.Moore, (1969). Some human factors affecting the design of vehicles and roads. Journal of the Institute of Highway Engineers, 16, 13–22.
39. U.Neisser, (1976). Cognition and reality. San Francisco, CA: W. H. Freeman.
40. S.Oltedal,, & T.Rundmo, (2006). The effects of personality and gender on risky driving behaviour and accident involvement. Safety Science, 44, 621–628.
41. M.Perez,, M.Hulse,, & L.Angell, (2013). Support for NHTSA visual-manual guidelines: Expert review of the visual occlusion method and how it compares to driver eye-glance behavior (No. DOT HS 811 763). Washington DC: S. Department of Transportation, National Highway Traffic Safety Administration.
42. T.A.Ranney,, G.H.Baldwin,, L.A.Smith,, J.Martin,, & E.N.Mazzae, (2013). Driver behavior during visual-manual secondary task performance: Occlusion method versus simulated driving (No. DOT HS 811 726).
43. K.Rumar, (1982, August). The human factor in road safety. Paper presented at the Eleventh Australian Road Research Board Conference, University of Melbourne, Melbourne, Australia.
44. D.D.Salvucci, (2006). Modeling driver behavior in a cognitive architecture. Human Factors: The Journal of the Human Factors and Ergonomics Society, 48, 362–380.
45. S.A.Shaheen,, & D.A.Niemeier, (2001) Integrating vehicle design and human factors: Minimizing elderly driving constraints. Transportation Research Part C, 9, 155–174.
46. C.E.Shannon & W.Weaver (1949). The mathematical theory of information. Urbana, IL: University of Illinois Press.
47. R.N.Shepard,, & J.Metzler, (1971). Mental rotation of three-dimensional objects. Science, 171, 701–703.
48. J.R.Stroop, (1953). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662.
49. W.P.Tanner,, Jr., & J.A.Swets, (1954). A decision-making theory of visual detection. Psychological Review, 61, 401–409.
50. A.J.Thissen,, M.Luman,, C.Hartman,, P.Hoekstra,, M.van Lieshout,, B.Franke,, … J.K.Buitelaar, (2014). Attention-deficit/hyperactivity disorder (ADHD) and motor timing in adolescents and their parents: familial characteristics of reaction time variability vary with age. Journal of the American Academy of Child & Adolescent Psychiatry, 53, 1010–1019.
51. L.Warshawsky-Livne,, & D.Shinar, (2002). Effects of uncertainty, transmission type, driver age and gender on brake reaction and movement time. Journal of Safety Research, 33, 117–128.
52. C.D.Wickens,, S.E.Gordon,, & Y.Liu, (2004). An introduction to human factors engineering (2nd ed.). Upper Saddle River, NJ: Pearson
53. C.D.Wickens,, J.G.Hollands,, S.Banbury,, & R.Parasuraman, (2012). Engineering psychology and human performance (4th ed.). New York, NY: Pearson
54. C.Wu,, & Y.Liu, (2007). Queuing network modeling of driver workload and performance. IEEE Transactions on Intelligent Transportation Systems, 8, 528–537.
55. Y.Yang,, A.R.Bender,, & N.Raz, (2015). Age related differences in reaction time components and diffusion properties of normal-appearing white matter in healthy adults. Neuropsychologia, 66, 246–258.
56. A.L.Yarbus, (1967). Eye movements during perception of complex objects. In L.A.Riggs (Ed.), Eye movements and vision (pp. 171–196). New York, NY: Plenum.
57. K.Young,, M.Regan,, & M.Hammer, (2007). Driver distraction: A review of the literature. In I.J.Faulks, M.Regan, M.Stevenson, J.Brown, A.Porter, & J.D.Irwin (Eds.), Distracted driving (pp. 379–405). Sydney, Australia: Australasian College of Road Safety.