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Fleet analysis of headway distance for autonomous driving

Andrej Ivanco¹

¹Department of Automotive Engineering, Clemson University, Greenville 29607, SC, USA.

Journal of Safety Research

|December 6, 2017

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.

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.

Results:

The paper identifies naturalistic headway distance and compares it with the previous findings from the literature.

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.

Keywords:

Autonomous Headway distance Naturalistic driving Radar data SHRP2

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