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Detecting deviation from normal driving using SHRP2 NDS data

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conference contribution
posted on 2017-03-02, 12:04 authored by E. Papazikou, Mohammed Quddus, Pete Thomas
Normal driving is naturally the first stage of the crash development sequence. Investigating normal driving can be proved useful for comparisons with safety critical scenarios and also crash prevention. The better we understand it, the more effectively we can detect deviations and stop them before they culminate in crashes. This study utilises Naturalistic driving data from the Strategic Highway Research Program 2 (SHRP2) to look into normal driving scenarios. Indicators’ thresholds were assumed with influence by the literature and then the values were validated based on real world data. The paper focuses on the methodology for deriving indicators representative of baseline, uneventful driving. With the approach that is presented here, reliable thresholds for variables can be introduced, capable of detecting the deviation on its very early onset.

Funding

This paper was sponsored by TRB committee AND30 Standing Committee on Simulation and Measurement of Vehicle and Operator Performance. We wish to express special thanks to Insurance Institute for Highway Safety (IIHS-HLDI) for their financial support with which the SHRP2 NDS data was obtained.

History

School

  • Design

Published in

Transportation Research Board 2017

Citation

PAPAZIKOU, E., QUDDUS, M.A. and THOMAS, P., 2017. Detecting deviation from normal driving using SHRP2 NDS data. Presented at the Transportation Research Board (TRB) 96th Annual Meeting, Washington D.C., US, 8th-12th January 2017.

Publisher

© The Authors

Version

  • AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

2016-08-01

Publication date

2017

Notes

This is a conference paper.

Publisher version

Book series

TRB 96th Annual Meeting Compendium of Papers;17-03309

Language

  • en

Location

Washington, US