Citation

Elhenawy M, Glaser S, Bond A, Rakotonirainy A, Demmel S, Masoud M. PLoS One 2020; 15(2): e0229289.

Affiliation

Centre for Accident Research & Road Safety, Queensland University of Technology, Brisbane, QLD, Australia.

Copyright

(Copyright © 2020, Public Library of Science)

DOI

10.1371/journal.pone.0229289

PMID

32106227

Abstract

Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. We are preparing for the largest Field Operational Test (FOT) in Australia to evaluate C-ITS safety benefits. Two of the safety benefit hypotheses we formulated assume a dependency between lane changes and C-ITS warnings displayed on the Human Machine Interface (HMI) during safety events. Lane change detection is done by processing many predictors from several sensors at the time of the safety event. However, in our planned FOT, the participating vehicles are only equipped with the vehicle C-ITS and the IMU. Therefore, in this paper, we propose a framework to test lane change and C-ITS dependency. In this framework, we train a random forest classifier using data collected from the IMU to detect lane changes. Consequently, the random forest output probabilities of the testing data in case of C-ITS and control are used to construct a 2x2 contingency table. Then we develop a permutation test to calculate the null hypothesis needed to test the independence of the lane change during safety events and the C-ITS.

Language: en