A hybrid approach for identifying factors affecting driver reaction time using naturalistic driving data

Nasim Arbabzadeh, Mohsen Jafari, Mohammad Jalayer, Shan Jiang, Mohamed Kharbeche

Research output: Contribution to journalArticle

Abstract

The National Transportation Safety Board (NTSB) estimates that 80% of the deaths and injuries resulting from rear-end collisions could be prevented by the use of advanced collision avoidance systems. While autonomous or higher-level vehicles will be equipped with this technology by default, most of the vehicles on our roadways will lack these advances, so rear-end crashes will dominate accident statistics for many years to come. However, a simple and cost-effective in-vehicle device that uses predictive tools and real-time driver-behavior and roadway data can significantly reduce the likelihood of these crashes. In this paper, we propose a hybrid physics/data-driven approach that can be used in a kinematic-based forward-collision warning system. In particular, we use a hierarchical regularized regression model to estimate driver reaction time based on individual driver characteristics, driving behavior, and surrounding driving conditions. This personalized reaction time is input into the Brill's one-dimensional car-following model to calculate the critical distance for collision warning. We use the Second Strategic Highway Research Program (SHRP-2)'s Naturalistic Driving Study (NDS) data, the largest and most comprehensive study of its kind, to model driver brake-to-stop response time. The results show that the inclusion of driver characteristics increases model precision in predicting driver reaction times.

Original languageEnglish (US)
Pages (from-to)107-124
Number of pages18
JournalTransportation Research Part C: Emerging Technologies
Volume100
DOIs
StatePublished - Mar 1 2019

Fingerprint

driver
Alarm systems
Collision avoidance
Brakes
accident statistics
Accidents
Kinematics
Railroad cars
Physics
traffic behavior
Statistics
physics
time
inclusion
death
Costs
regression
lack
costs

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

Cite this

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A hybrid approach for identifying factors affecting driver reaction time using naturalistic driving data. / Arbabzadeh, Nasim; Jafari, Mohsen; Jalayer, Mohammad; Jiang, Shan; Kharbeche, Mohamed.

In: Transportation Research Part C: Emerging Technologies, Vol. 100, 01.03.2019, p. 107-124.

Research output: Contribution to journalArticle

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