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Reliability and safety analysis methodology for identification of drivers’ erroneous actions

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Abstract

Owing to significantly individual differences in everyday driving behavior, it is quite difficult to assess the relative importance of driver errors compared with vehicle faults or road environment anomalies. This paper briefly presents several basic concepts for analysis of driving dependability including driving errors, driving reliability, driver recovery from erroneous actions, and key factors that shape driving behavior. This presentation is followed by construction of a shaping architecture for driving behavior that consists of a perception stage, a decision-making stage, an execution stage and correlativity among stages, in addition to internal feedback from complex traffic states. The causation classification of driving errors is then discussed in the context of three elemental types: perception error, decision-making error and execution error. The emphasis of this paper is on how to quantify driving dependability in order to identify various erroneous driver actions during traffic accidents. Specifically, this paper proposes a methodology to measure the probability of driving errors by considering the driver recovery from erroneous actions. The purpose of model-based driving dependability analysis is to quantitatively and qualitatively analyze the relationship between driving errors and traffic accidents causations.

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References

  • Clare, K. (2003). WHO acts on road safety to reverse accident trends. The Lancet 362,4, 1125.

    Google Scholar 

  • Glendon, A. I., Stanton, N. A. and Harrision, D. (1994). Factor analyzing a behavior shaping concepts questionnaire. Robertson, S. A. (Edn), Contemporary Ergonomics, 340–345.

  • Groeger, J. A. (1990). Drivers’ errors in, and out of context. Ergonomics, 33, 1201–1213.

    Article  Google Scholar 

  • Han, I. and Yang, K. S. (2009). Characteristic analysis for cognition of dangerous driving using automobile black boxes. Int. J. Automotive Technology 10,5, 597–605.

    Article  Google Scholar 

  • Inokuti, M. and Yasuo, Y. (1985). New Traffic System. Asakura Press. Tokyo.

    Google Scholar 

  • Kim, S. Y., Choi, H. C., Won, W. J. and Oh, S. Y. (2009). Driving environment assessment using fusion of in- and out-of-vehicle vision systems. Int. J. Automotive Technology 10,1, 103–113.

    Article  Google Scholar 

  • Kim, J. H., Kim, Y. W. and Sim, K. Y. (2007). Quantitative study on the fearfulness of human driver using vector quantization. Int. J. Automotive Technology 8,4, 505–512.

    Google Scholar 

  • Koppa, R. J. (1996). Human factors. Monograph on traffic flow theory. The Federal Highway Administration (FHWA), Gartner, N. H., Messer, C., Rathi, A. K. (Edn), 3-1–3-32.

  • Liu, X. M. and Ren, F. T. (1991). Road traffic system safety analysis. Traffic Engineering, 1, 7–10.

    Google Scholar 

  • Kuzminski, P., Eisele, J. S., Garber, N., Schwing, R., Haimes, Y. Y., Li, D. and Chowdhury, M. (1995). Improvement of highway safety I: Identification of causal factors through fault-tree modeling. Risk Analysis, 15, 293–312.

    Article  Google Scholar 

  • Malaterre, G. (1990). Error analysis and in-depth accident studies. Ergonomics, 33, 1403–1421.

    Article  Google Scholar 

  • Moieni, P., Surgin, A. J. and Singh, A. (1994). Advances in human reliability analysis methodology. Part I: Frameworks, models and data; Part II: PC-based HRA software, Reliability Engineering & System Safety, 44, 27–66.

    Article  Google Scholar 

  • Mortazavi, A., Eskandarian, A. and Sayed, R. A. (2009). Effect of drowsiness on driving performance variables of commercial vehicle drivers. Int. J. Automotive Technology 10,3, 391–404.

    Article  Google Scholar 

  • Nebi, S. (2003). Personality and behavioral predictors of traffic accidents: Testing a contextual mediated model. Accident Analysis and Prevention, 35, 949–964.

    Article  Google Scholar 

  • Parker, D., Reason, J. T., Manstead, A. S. R. and Stradling, S. G. (1995). Driving errors, driving violations and accident involvement. Ergonomics, 38, 1036–1048.

    Article  Google Scholar 

  • Parry, G. W. (1995). Suggestions for an improved HRA method for use in probabilistic safety assessment. Reliability Engineering and System Safety, 49, 1–12.

    Article  Google Scholar 

  • Ranney, T. A. (1994). Models of driving behavior: A review of their evolution. Accidents Analysis and Prevention, 26, 733–750.

    Article  Google Scholar 

  • Reason, J. (1990). Human Error. Cambridge University Press. Cambridge.

    Google Scholar 

  • Rin, Y., Okawa, M. and Inokuti, M. (1971). Human-Machine System Design. Humans and Technology Company. Tokyo.

    Google Scholar 

  • Sayed, T., Walid, A. and Frank, N. (1995). Identifying accident-prone location using fuzzy pattern recognition. J. Transportation Engineering 121,4, 352–358.

    Article  Google Scholar 

  • Summala, H. (1996). Accident risk and driver behaviour. Safety Science 22,l–3, 103–I 17.

    Article  Google Scholar 

  • Treat, J. R. (1980). A study of precrash factors involved in traffic accidents. HSRI Research Review 10(6)–11(1), 1–35.

    Google Scholar 

  • Wang, W. H., Bubb, H., Ikeuchi, K. and Cao, Q. (2010). Measurement of dangerous traffic conditions through driving dependability analysis. J. Scientific and Industrial Research, 69, 254–258.

    Google Scholar 

  • Wang, W. H. (2002). A digital driving system for smart vehicle. IEEE Intelligent Systems 17,5, 81–83.

    Article  Google Scholar 

  • Wang, W. H. (2001). Driving Behaviour Theory and Method in Road Transport System. Science Press. Beijing.

    Google Scholar 

  • Wang, W. H. (2010). Incident tree model and incident tree analysis method for quantified risk assessment: An indepth accident study in traffic operation. Safety Sci. 48,10, 1248–1262.

    Article  Google Scholar 

  • Wang, W. H. (2010). A framework for function allocation in intelligent driver interface design for comfort and safety. Int. J. Computational Intelligence Systems, 3, 531–541.

    Google Scholar 

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Wang, W.H., Cao, Q., Ikeuchi, K. et al. Reliability and safety analysis methodology for identification of drivers’ erroneous actions. Int.J Automot. Technol. 11, 873–881 (2010). https://doi.org/10.1007/s12239-010-0104-3

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  • DOI: https://doi.org/10.1007/s12239-010-0104-3

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