access icon free Effectiveness of advisory warnings based on cooperative perception

Cooperative perception makes it possible to provide drivers with early advisory warnings about potentially dangerous driving situations. On the basis of the research results pertaining to imminent crash warnings, it was expected that the effectiveness of such advisory warnings depends on situation-specific anticipations by the driver. During a simulator study, N = 20 drivers went through a wide range of longitudinal traffic and intersection scenarios. The scenarios varied in the possibility to anticipate traffic conflicts (anticipation: high against low) and were completed under different visibility conditions (visibility: obstructed against visible), with and without driver assistance based on cooperative perception (i.e. visual–auditory advisory warnings 2 s prior to the last-possible warning moment; assistance: no assistance against with assistance). The warning concept was based on empirical pre-studies and previously validated on a public test intersection. During non-assisted driving, critical situations were mainly experienced when the possibility to anticipate traffic conflicts was low. Visual obstructions lead to a further increase in the frequency of critical situations. Furthermore, the results indicate a clear mitigation of critical encounters when providing early advisory warnings. This applies particularly to surprising and unexpected scenarios and thus illustrates the potential of cooperative perception to enhance active traffic safety.

Inspec keywords: traffic engineering computing; road safety

Other keywords: public test intersection; active traffic safety; imminent crash warnings; situation-specific anticipations; advisory warning effectiveness; emergency warnings; driver assistance; cooperative perception; intersection scenarios; visibility conditions; visual obstruction; traffic conflict; dangerous driving situations; longitudinal traffic

Subjects: Traffic engineering computing

References

    1. 1)
    2. 2)
      • 3. Naujoks, F., Neukum, A.: ‘Specificity and timing of advisory warnings based on cooperative perception’. Mensch & Computer 2014 – Workshop Band, De Gruyter, Oldenbourg, 2014, pp. 229238.
    3. 3)
    4. 4)
      • 49. Näätänen, R., Summala, H.: ‘Road-user behavior and traffic accidents’ (Amsterdam, North-Holland, 1976).
    5. 5)
      • 41. Green, P., Levison, W., Paelke, G., Serafin, C.: ‘Preliminary human factors guidelines for driver information systems’. Technical Report UMTRI-93-21, The University of Michigan Transportation Research Institute, 1993.
    6. 6)
    7. 7)
      • 20. Dingus, T.A., Jahns, S.K., Horowitz, A.D., Knipling, R.: ‘Human factors design issues for crash avoidance systems’, in Barfield, N.V., Dingus, A. (Eds.): ‘Human factors in intelligent transportation systems’ (Erlbaum, New York, 1998), pp. 5593.
    8. 8)
      • 42. Neukum, A., Krüger, H.-P.: ‘Fahrerreaktionen bei Lenksystemstörungen - Untersuchungsmethodik und Bewertungskriterien’, VDI-Berichte, 2003, 1791, pp. 297318.
    9. 9)
    10. 10)
      • 25. Koornstra, M.J.: ‘Safety relevance of vision research and theory’, in Gale, A.G. (Ed.): ‘Vision in Vehicles IV’ (Amersteram, North Holland, 1993), pp. 313.
    11. 11)
      • 15. Seeliger, F., Weidl, G., Petrich, D., et al: ‘Advisory warnings based on cooperative perception’. IEEE Intelligent Vehicles Symp., Dearborn, Michigan, June 2014, pp. 246252.
    12. 12)
    13. 13)
      • 13. Lenné, M.G., Triggs, T.J.: ‘Warning drivers of approaching hazards: the importance of location cues and multi-sensory cues’, in de Waard, D., Godthelp, J., Kooi, F.L., Brookhuis, K. (Eds.): ‘Human factors, security and safety’ (Shaker Publishing, Maastricht, 2009), pp. 203211.
    14. 14)
    15. 15)
      • 37. Statistisches Bundesamt: ‘Unfallentwicklung Auf Deutschen Straßen’ (Statistisches Bundesamt, Wiesbaden, 2012).
    16. 16)
    17. 17)
      • 44. Davis, F.D.: ‘Perceived usefulness, perceived ease of use, and user acceptance of information technology’. MIS Quarterly, 1989, 13, pp. 319340.
    18. 18)
      • 16. Weidl, G., Singhal, V., Petrich, D., Kasper, D., Wedel, A., Breuel, G.: ‘Collision risk prediction and warning at road intersections using an object oriented Bayesian network’. Fifth Int. Conf. on Automotive User Interfaces, Eindhoven, The Netherlands, October 2014, pp. 270277.
    19. 19)
      • 38. Statistisches Bundesamt: ‘Verkehrsunfälle. Unfälle Im Seniorenalter 2011’ (Statistisches Bundesamt, Wiesbaden, 2012).
    20. 20)
      • 43. Neukum, A., Lübbeke, T., Krüger, H.-P., Mayser, C., Steinle, J.: ‘ACC-Stop&Go: Fahrerverhalten an Funktionalen Systemgrenzen’. 5. Workshop Fahrerassistenzsysteme, FMRT, Karlsruhe, 2008, pp. 141150.
    21. 21)
    22. 22)
      • 11. Winner, H.: ‘Frontalkollisionsschutzsysteme’, in Winner, H., Hakuli, S., Wolf, G.: (Eds.): ‘Handbuch Fahrerassistenzsysteme’ (Vieweg & Teubner, Wiesbaden, 2012), pp. 522542.
    23. 23)
      • 51. OECD: ‘Behavioural adaptations to changes in the road transport system’ (Organization for Economic Co-operation and Development, 1990).
    24. 24)
      • 17. Goldhammer, M., Brunsmann, U., Doll, K., Dietmayer, K., Strigel, E., Meissner, D.: ‘Cooperative multi sensor network for traffic safety applications at intersections’. IEEE Conf. on Intelligent Transportation Systems (ITSC), Anchorage, Alaska, September 2012, pp. 11781183.
    25. 25)
      • 28. Theeuwes, J., Hagenzieker, P.: ‘Visual search of traffic scenes: on the effect of location expectations’, in Gale, A.G. (Ed.): ‘Vision in Vehicles IV’ (Amsterdam, North Holland, 1993), pp. 149158.
    26. 26)
    27. 27)
    28. 28)
    29. 29)
    30. 30)
    31. 31)
    32. 32)
    33. 33)
    34. 34)
    35. 35)
      • 18. Petrich, D., Dang, T., Kasper, D., Breuel, G., Stiller, C.: ‘Map-based long term motion prediction for vehicles in traffic environments’. IEEE Conf. on Intelligent Transportation Systems (ITSC), The Hague, The Netherlands, October 2013, pp. 21662172.
    36. 36)
      • 19. Campbell, J.L., Carney, C., Kantowitz, B.H.: ‘Human factors design guidelines for advanced traveler information systems (ATIS) and commercial vehicle operations (CVO)’. Technical Report FHWA-RD-98–057, U.S. Department of Transportation, 1997.
    37. 37)
    38. 38)
      • 9. Totzke, I., Naujoks, F., Mühlbacher, D., Krüger, H.-P.: ‘Precision of congestion warnings: do drivers really need warnings with precise information about the congestion tail's position?’, in de Waard, D., Merat, N., Jamson, A.H., Barnard, Y., Carsten, O.M.J. (Eds.): ‘Human factors of systems and technology’ (Shaker Publishing, Maastricht, 2011), pp. 235247.
    39. 39)
      • 14. Neukum, A.: ‘Wenn Das Fahrzeug mehr sieht als der Fahrer – Konsequenzen für die Gestaltung der Fahrer-Fahrzeug Schnittstelle’. Ko-FAS Interim Presentation, Aschaffenburg, Germany, September 2011.
    40. 40)
      • 36. Gesamtverband der deutschen Versicherungswirtschaft e.V. (GDV): ‘Unfalltypenkatalog: Leitfaden Zur Bestimmung Des Unfalltyps’ (Institut für Straßenverkehr, Cologne, 1998).
    41. 41)
      • 5. Thoma, S., Lindberg, T., Klinker, G.: ‘Evaluation of a generic warning for multiple intersection assistance systems’, in de Waard, D., Godthelp, H., Kooi, F., Brookhuis, K. (Eds.): ‘Human factors, security and safety’ (Shaker Publishing, Maastricht, 2009), pp. 173188.
    42. 42)
      • 2. Weidl, G., Breuel, G.: ‘Overall probabilistic framework for modeling and analysis of intersection situations’. 16th Int. Forum on Advanced Microsystems for Automotive Applications (AMAA), Berlin, Germany, May 2012, pp. 257268.
    43. 43)
    44. 44)
    45. 45)
      • 1. Rauch, A., Klanner, F., Rasshofer, R., Dietmayer, K.: ‘Car2x-based perception in a high-level fusion architecture for cooperative perception systems’. IEEE Intelligent Vehicles Symp., Madrid, Spain, June 2012, pp. 270275.
    46. 46)
      • 30. Naujoks, F., Neukum, A.: ‘Timing of in-vehicle advisory warnings based on cooperative perception’. Proc. Human Factors and Ergonomics Society Europe Chapter Annual Meeting, HFES Europe, 2014, pp. 114.
    47. 47)
      • 46. Tabachnik, B.G., Fidell, L.S.: ‘Using multivariate statistics’ (Pearson Education, Harlow, 2012).
    48. 48)
      • 33. Mahr, A., Cao, Y., Theune, M., Dimitrova-Krause, V., Schwartz, T., Müller, C.A.: ‘What if it suddenly fails? Behavioral aspects of advanced driver assistant systems on the example of local danger alerts’. Proc. 19th European Conf. on Artificial Intelligence, Amsterdam, 2010, pp. 10511052.
    49. 49)
      • 40. Hoffmann, S., Buld, S.: ‘Darstellung und Evaluation eines Trainings zum Fahren in der Fahrsimulation’, inVDI Wissensforum (Ed.): ‘Integrierte Sicherheit und Fahrerassistenzsysteme’ (VDI Verlag, Dusseldorf, 2006), pp. 113132.
    50. 50)
      • 45. Hayward, J.C.: ‘Near-miss determination through use of a scale of danger’, Highway Res. Rec., 1972, 384, pp. 2434.
    51. 51)
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