@article{ref1,
title="Fleet analysis of headway distance for autonomous driving",
journal="Journal of safety research",
year="2017",
author="Ivanco, Andrej",
volume="63",
number="",
pages="145-148",
abstract="INTRODUCTION: Modern automobiles are going through a paradigm shift, where the driver may no longer be needed to drive the vehicle. As the self-driving vehicles are making their way to public roads the automakers have to ensure the naturalistic driving feel to gain drivers' confidence and accelerate adoption rates. <br><br>METHOD: This paper filters and analyzes a subset of radar data collected from SHRP2 with focus on characterizing the naturalistic headway distance with respect to the vehicle speed. <br><br>RESULTS: The paper identifies naturalistic headway distance and compares it with the previous findings from the literature. <br><br>CONCLUSION: A clear relation between time headway and speed was confirmed and quantified. A significant difference exists among individual drivers which supports a need to further refine the analysis. PRACTICAL APPLICATIONS: By understanding the relationship between human driving and their surroundings, the naturalistic driving behavior can be quantified and used to increase the adoption rates of autonomous driving. Dangerous and safety-compromising driving can be identified as well in order to avoid its replication in the control algorithms.<br><br>Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="0022-4375",
doi="10.1016/j.jsr.2017.10.009",
url="http://dx.doi.org/10.1016/j.jsr.2017.10.009"
}