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Design of Safety Map with Collectives of Smartphone Sensors

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Human Behavior Understanding in Networked Sensing
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Abstract

Recently, there have been strong demand and interest for developing methods to analyze driving data for extracting traffic safety information. In this chapter, we study a method to extract incident factors that interfere with smooth driving for making safety map by using smartphone as a terminal data logger. In automobile research field, several methods for detecting sudden braking have been proposed; however, the detection of the factors those disturb the driving process, which drivers should pay attention, has not been fully discussed. Our method is based on smartphone with GPS information, therefore sophisticated equipments such as speed cameras are not required. We highly expect to utilize data from community in which each member shares smartphone data for generating incident map collectively. In our method, we apply the IMAC method (a dynamic map generating method) [1] for generating safety map. We carry out computer simulations and take real-world experiments in order to validate a part of safety map which generated by the proposed method. The result shows that based on the proposed method, safety map are correctly archived.

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References

  1. Saarinen J, Andreasson H, Achim JL (2012) Independent Markov chain occupancy grid maps for representation of dynamic environment, 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 3489–3495

    Google Scholar 

  2. Kanagawa police safety map http://www.police.pref.kanagawa.jp/mes/mesf0152.htm

  3. Aichi Kirashitara county safety map http://www.pref.aichi.jp/cmsfiles/contents/0000041/41119/sitara-jikomap.pdf

  4. Nakajima Y, Makimura K, Mashiko T (2005) Near-miss data for urban transport administration. IBS annual report 2005, pp 81–86

    Google Scholar 

  5. Honda safety map project http://safetymap.jp/

  6. http://g-book.com/pc/smart_G-BOOK/

  7. Herrera JC, Work DB, Herring R, Ban XJ, Jacobson Q, Bayen AM (2010) Evaluation of traffic data obtained via GPS-enabled mobile phones: the mobile century field experiment. Transp Res C: Emerg Technol 18(4):568–583

    Article  Google Scholar 

  8. Herrera JC, Bayen AM (2010) Incorporation of Lagrangian measurements in freeway traffic state estimation. Transp Res B: Methodol 44(4):460–481

    Article  Google Scholar 

  9. Demonstration promotion of probe technology export, H23 Trade and Investment business facilitation support program, Hitachi Co., Ltd, Nippon Koei Co. Ltd and Center for Information Cooperation CICC

    Google Scholar 

  10. http://www.meti.go.jp/committee/kenkyukai/seisan/juntenchoueisei/003_02_2.pdf

  11. Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. MIT press, Cambridge

    MATH  Google Scholar 

  12. Fagan D, Meier R (2010) Highly-dynamic, pervasive monitoring of traffic congestion levels. Proceedings of ITRN2010, pp 1–8

    Google Scholar 

  13. K Li, Misener JA, Hedrick K (2007) On-board road condition monitoring system using slip-based tyre road friction estimation and wheel speed signal analysis. Proc Inst Mech Eng K: J Multi-body Dyn 221(1):129–146

    Article  Google Scholar 

  14. Mohan P, Venkata N, Ramjee R (2008) Nericell rich monitoring of road and traffic conditions using mobile smartphones. In: Proceedings of the 6th ACM conference on embedded network sensor systems, pp 323–336

    Google Scholar 

  15. Fazeen M, Gozick B, Dantu R, Bhukhiya M, Gonzalez MC (2012) Safe driving using mobile phones. IEEE Trans Intell Transp Syst 13(3):1462–1468

    Article  Google Scholar 

  16. Zhang L, Thiemann F, Sester M (2010) Integration of GPS traces with road map. In: Proceeding of IWCTS’10 Proceedings of the second international workshop on computational transportation science, pp 17–22

    Google Scholar 

  17. Fathi A, Krumm J (2010) Detecting road intersections from GPS traces. Geogr Inf Sci 6292:56–69

    Article  Google Scholar 

  18. Study of outcome measures with potential accident data. IBS annual report 205, pp 85–86

    Google Scholar 

  19. Yamazaki S, Funakubo A, Tanizawa Y (2011) Estimation of dangerous routes using the near-miss incident data of probe-car

    Google Scholar 

  20. Dang CV, Sato H, Shirakawa T, Namatame A (2014) Occupancy grid map of semi-static objects by mobile observer. In: Proceedings of the international symposium on artificial life and robotics 19th 2014

    Google Scholar 

  21. Cao L, Krumm J (2009) From GPS traces to a routable road map, Proceeding of GIS’09 proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pp 3–12

    Google Scholar 

  22. Hilton BN, Horan TA, Burkhard R, Schooley B, SafeRoadMaps: communication of location and density of traffic fatalities through spatial visualization and heat map analysis information visualization http://saferoadmaps.org

  23. White J, Thompson C, Turner H, Dougherty B, Schmidt DC (2011) Wreckwatch: automatic traffic accident detection and notification with smartphones. J Mob Netw Appl 16(3):285–303

    Article  Google Scholar 

  24. Thompson C (2010) Using smartphones to detect car accidents and provide situational awareness to first responders. Third international ICST conference on mobile wireless middleware, operating systems, and applications, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering vol 48, pp 29–42

    Google Scholar 

  25. Nagai M, Yohei M, Kamata M, Motokiso K, Study on near-miss analysis using the drive recorder, Research report part 1

    Google Scholar 

  26. Fujita M, Yohei M, Kotake M, Kamata M, Nagai M, Study on near-miss analysis using the drive recorder, Originally research report part 2

    Google Scholar 

  27. Fujita M, Kotake M, Kamata M, Nagai M, Yohei M (2007) Study on near-miss analysis using the drive recorder, rear-end collision near-miss analysis using the database. In: The Society of Automotive Engineers Society of Automotive Engineers Proceedings, vol 38(4), pp 151–156

    Google Scholar 

  28. Macadam CC (2003) Understanding and modeling the human driver. Veh Syst Dyn 40(1–3):101–134

    Article  Google Scholar 

  29. Levesque A, Johrendt J (2011) The state of the art of driver model development. SAE Technical Paper 2011-01-0432. doi:10.4271/2011-01-0432

  30. Jurgensohn T (2007) Control theory models of the driver. Modelling driver behaviour in automotive environments. Springer, London

    Google Scholar 

  31. Weir HD, Chao KC (2007) Review of control theory models for directional and speed control. Modelling driver behaviour in automotive environments. Springer, London, pp 293–311

    Google Scholar 

  32. Fox D, Burgard W, Thrun S (1999) Markov localization for mobile robots in dynamic environments. J Artif Intell Res (JAIR) 11:391–427

    MATH  Google Scholar 

  33. Luber M, Tipaldi GD, Arras KO (2011) Place-dependent people tracking. Int J Robot Res 30(3):280

    Article  Google Scholar 

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Correspondence to Akira Namatame .

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Chau, D.V., Kubo, M., Sato, H., Namatame, A. (2014). Design of Safety Map with Collectives of Smartphone Sensors. In: Spagnolo, P., Mazzeo, P., Distante, C. (eds) Human Behavior Understanding in Networked Sensing. Springer, Cham. https://doi.org/10.1007/978-3-319-10807-0_20

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  • DOI: https://doi.org/10.1007/978-3-319-10807-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10806-3

  • Online ISBN: 978-3-319-10807-0

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