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Smart Car Space: An Application

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Part of the book series: Advanced Topics in Science and Technology in China ((ATSTC))

Abstract

Smart cars are a promising application domain for ubiquitous computing. In the highly mobile car, it is a challenging task to provide a comfortable, convenient, nonintrusive, and safe space that can ubiquitously access information and service. This chapter presents a general framework of smart car space from the point of view of context-awareness. A specific context model is proposed for describing both simple and complex context in smart car space. A driver behavior model for a smart car is proposed for comfortable car-following and multi-task processing. A smart car space prototype is built for demonstration and verification.

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References

  1. Jochemi T, Pomerleau D, Kkmar B, Armstrong J (1995) PANS: a portable navigation platform. In: IEEE symposium on intelligent vehicle, Detroit

    Google Scholar 

  2. Conti JP (2006) Smart cars. Commun Eng 3(6):25–29

    Article  Google Scholar 

  3. Oliver N, Pentland AP (2000) Driver behavior recognition and prediction in a Smart Car. In: Verly JG (ed) Enhanced and synthetic vision 2000, Orlando, USA. SPIE proceedings series, vol. 4023. SPIE, Bellingham, pp 280–290

    Google Scholar 

  4. Siewiorek D, Smailagic A, Hornyak M (2002) Multimodal contextual car-driver interface. In: Martin DC (ed) 4th IEEE international conference on multimodal interfaces (ICMI’02), Washington, DC, IEEE Computer Society Press, pp 367–373

    Google Scholar 

  5. Wang FY, Zeng D, Yang LQ (2006) Smart cars on smart roads: an IEEE intelligent transportation systems society update. IEEE Pervasive Comput 5(4):68–69

    Article  Google Scholar 

  6. Pan G, Wu ZH, Sun J (2007) Towards a smart space inside car. In: The 9th international conference on ubiquitous computing (Ubicomp’07), LBR, Innsbruck, Austria, 16–19 Sept 2007

    Google Scholar 

  7. Sun J, Wu ZH, Pan G (2009) Context-aware smart car: from model to prototype. J Zhejiang Univ (Scie A) 10(7):1049–1059

    Article  Google Scholar 

  8. Tang SM, Wang FY, Miao QH (2006) ITSC 05: current issues and research trends. IEEE Intell Syst 21(2):96–102

    Article  Google Scholar 

  9. Rau PS (1998) A heavy vehicle drowsy driver detection and warning system: scientific issues and technical challenges. In: Proceeding of 16th international technical conference on the enhanced safety of vehicles (ESV98), Ontario, Canada

    Google Scholar 

  10. Moite S (1992) How smart can a car be?. In: Proceedings of the intelligent vehicles’92 symposium. IEEE Press, Los Alamitos, pp 277–279

    Google Scholar 

  11. Waard DD (1996) The measurement of drivers’ mental workload. PhD thesis, Traffic Research Centre, University of Groningen, Haren, The Netherlands

    Google Scholar 

  12. Haworth NL, Triggs TJ, GreyDriver EM (1988) Driver fatigue: concepts, measurement and crash countermeasures. Technical report, Federal Office of Road Safety Contract Report 72 by Human Factors Group, Department of Psychology, Monash University, Melbourne

    Google Scholar 

  13. Veeraraghavan H, Papanikolopoulos NP (2001) Detecting driver fatigue through the use of advanced face monitoring techniques, artificial intelligence, robotics, and vision laboratory. Department of Computer Science and Engineering, University of Minnesota, Twin Cities. http://www.its.umn.edu/Publications/ResearchReports/reportdetail.html?id=517. Retrieved 25 Apr 2013

  14. Bevan J (1998) Drugs and driving: a discussion paper. AA Group Public Policy, Basingstoke

    Google Scholar 

  15. Alm H, Nilsson L (1995) The effects of a mobile telephone task on driver behavior in a car following situation. Accid Anal Prev 27(5):707–715

    Article  Google Scholar 

  16. McKnight AJ, McKnight AS (1993) The effect of cellular phone use upon driver attention. Accid Anal Prev 25(3):259–265

    Article  Google Scholar 

  17. Burns P (2003) Strategies for reducing driver distraction from in-vehicle telematics devices: a discussion document. Transport Canada, Road Safety and Motor Vehicle Regulation Directorate

    Google Scholar 

  18. Fang XP (2001) Driver behavior models for traffic simulation. Master’s thesis, Iowa State University. Transport Canada, Road Safety and Motor Vehicle Regulation Directorate

    Google Scholar 

  19. Yoshiyuki U (2004) Driver behavior and active safety (Overview). RD Rev Toyota CRDL 39(2):1–8

    Google Scholar 

  20. Wu ZH, Liu YF, Pan G (2009) A smart car control model for brake comfort based on car following. IEEE Trans Intell Transp Syst (TITS) 10(1):42–46

    Article  Google Scholar 

  21. Bengtsson J (2001) Adaptive cruise control and driver modeling. Lund University, Sweden

    Google Scholar 

  22. Fang XP, Phaml HA, Kobayashi M (2001) PD controller for car-following models based on real data. In: 1st human-centered transportation simulation conference, Iowa City, Iowa

    Google Scholar 

  23. Pipes LA, Wojcik CK (1968) A contribution to theory of traffic flow. In: Analysis and control of traffic flow symposium, Detroit, USA, Society of Automotive Engineers, pp 53–60

    Book  Google Scholar 

  24. Reijmers IJJ (2003) Traffic guidance systems. Society of Automotive Engineers, Et4-024

    Google Scholar 

  25. Taatgen N (2005) Modeling parallelization and flexibility improvements in skill acquisition: from dual tasks to complex dynamic skills. Cogn Sci 29(3):421–455

    Article  Google Scholar 

  26. Gray WD, John BE, Atwood ME (1993) Project Ernestine: Validating a GOMS analysis for predicting and explaining real-world performance. Hum Comput Interact 8(3):237–309

    Article  Google Scholar 

  27. Meyer DE, Kieras DE (1997) A computational theory of executive cognitive processes and multiple-task performance. Part 1. Basic mechanisms. Psychol Rev 104:2–65

    Google Scholar 

  28. Matessa M, Remington R, Vera A (2003) How apex automates CPMGOMS. In: 5th international conference on cognitive modeling, Bamberg, Germany, pp 93–98

    Google Scholar 

  29. Byrne MD, Anderson JR (2001) Serial modules in parallel: the psychological refractory period and perfect time-sharing. Psychol Rev 108(4):847–869

    Article  Google Scholar 

  30. Anderson JR, Bothell D, Byrne MD (2004) An integrated theory of the mind. Psychol Rev 111(4):1036–1060

    Article  Google Scholar 

  31. Kandel ER (2001) The molecular biology of memory storage: a dialogue between genes and synapses. Science 294(5544):1030–1038

    Article  Google Scholar 

  32. Michon JA (1985) A critical view of driver behavior models: what do we know, what should we do?. In: Evans LA, Schwing RC (eds) Human behaviour and traffic safety. Plenum Press, New York, pp 485–520

    Chapter  Google Scholar 

  33. Salvucci DD (2006) Modeling driver behavior in a cognitive architecture. Hum Factors 48:362–380

    Article  Google Scholar 

  34. Westheimer G (1954) Eye movement responses to horizontally moving visual stimuli. Arch Ophthalmol 52:932

    Article  Google Scholar 

  35. Grzywacz NM, Watamaniuk SN, McKee SP (1995) Temporal coherence theory for the detection and measurement of visual motion. Vision Res 35(22):3183–3203

    Article  Google Scholar 

  36. Carpenter RHS (1988) Movements of the eyes, 2nd Rev. Pion Ltd., London

    Google Scholar 

  37. CAN in Automation. http://www.can-cia.org. Retrieved 25 Apr 2013

  38. Salber D, Dey AK, Abowd GD (1999) The context toolkit: aiding the development of context-enabled applications. In: Williams MG, Altom MW (eds) Proceedings of the SIGCHI conference on human factors in computing systems (CHI’99), Pittsburgh, PA, ACM, pp 434–441

    Google Scholar 

  39. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Med Imaging 23(6):681–685

    Google Scholar 

  40. Viaola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154

    Article  Google Scholar 

  41. Pan G, Sun L, Wu ZH, Lao SH (2007) Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: 11th IEEE international conference on computer vision (ICCV’07), Rio de Janeiro, Brazil, pp 1–8

    Google Scholar 

  42. Pan G, Wu ZH, Sun L (2008) Liveness detection for face recognition. In: Delac K, Grgic M, Bartlett MS (eds) Recent Adv Face Recognit. InTech, pp 109–124

    Google Scholar 

  43. Sun L, Pan G, Wu ZH, Lao SH (2007) Blinking-based live face detection using conditional random fields. In: The 2nd international conference on biometrics (ICB’07), Seoul, Korea, 27–29 Aug 2007

    Google Scholar 

  44. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  45. Liu YY (2007) Sonar 2.0: the speaker recognition software platform. Master’s thesis, Zhejiang University, China (in Chinese)

    Google Scholar 

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© 2013 Zhejiang University Press, Hangzhou and Springer-Verlag Berlin Heidelberg

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Wu, Z., Pan, G. (2013). Smart Car Space: An Application. In: SmartShadow: Models and Methods for Pervasive Computing. Advanced Topics in Science and Technology in China. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36382-5_6

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  • DOI: https://doi.org/10.1007/978-3-642-36382-5_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36381-8

  • Online ISBN: 978-3-642-36382-5

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