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A Path Towards Understanding Factors Affecting Crash Severity in Autonomous Vehicles Using Current Naturalistic Driving Data

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1038))

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Abstract

In the U.S., in 2015 alone, there were approximately 35,000 fatalities and 2.4 million injuries caused by an estimated 6.3 million traffic accidents. In the future, it is speculated that automated systems will help to avoid or decrease the number and severity of accidents. However, before such a time, a broad range of vehicles, from non-autonomous to fully-autonomous, will share the road. Hence, measures need to be put in place to improve both safety and efficiency, while not compromising the advantages of autonomous driving technology. In this study, a Bayesian network model is developed to predict the severity of an accident, should it occur, given the road and the immediate environment conditions. The model is calibrated for the case of traditional vehicles using pre-crash information on driver behaviour, road surface conditions, weather and lighting conditions, among other variables, to predict two categories of consequences for accidents, namely property damage and injury/fatality. The results demonstrate that the proposed methodology and the determinant factors used in the models can predict the consequences of an accident, and more importantly, the probability of a crash causing injury/fatality, with high accuracy. Approaches to extend this model are proposed to predict accident severity for autonomous vehicles through leveraging their sensor data. Such a model would assist the development of countermeasures to identify the most important factors impacting severity of accidents for semi- and fully-autonomous vehicles to prevent accidents, decrease accident severity in cases where accidents are bound to occur, and improve transportation safety in general.

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Author information

Authors and Affiliations

  1. University of Tennessee, Knoxville, TN, 37996, USA

    Franco van Wyk & Anahita Khojandi

  2. University of Michigan, Ann Arbor, MI, 48109, USA

    Neda Masoud

Authors
  1. Franco van Wyk
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  2. Anahita Khojandi
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  3. Neda Masoud
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Corresponding author

Correspondence to Anahita Khojandi .

Editor information

Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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van Wyk, F., Khojandi, A., Masoud, N. (2020). A Path Towards Understanding Factors Affecting Crash Severity in Autonomous Vehicles Using Current Naturalistic Driving Data. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_8

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