You are here

FIELD EXPERIMENT OF MIXED TRAFFIC – HUMAN DRIVER INTERACTION BETWEEN ADAPTIVE CRUISE CONTROL (ACC) AND HUMAN DRIVERS

Download pdf | Full Screen View

Date Issued:
2023
Abstract/Description:
Mainstream vehicles sold today are equipped with the Advanced Driver Assistance System (ADAS) known as Adaptive Cruise Control (ACC). ACC automatically adjusts speeds and maintains a safe following distance with the preceding vehicle. This enables partial automation by automating longitudinal car-following. Despite the ever-increasing market penetration, ACC-equipped vehicles will likely operate in a mixed environment with other human-driven vehicles first. However, the traffic flow impact of human driver behavior when following ACC-equipped vehicles is largely unknown, and it is uncertain whether this deserves special consideration when modeling human driver behavior near ACC enabled vehicles. This study conducted a preliminary real-world experiment on a freeway (a portion of Interstate 95) and an urban arterial (a portion of state route A1A) to investigate the human driver behavior with and without the presence of vehicles in ACC mode as the leaders. This unbiased experiment was conducted in naturalistic traffic conditions. Results from the field experiments demonstrate that in a mixed environment with ACC-equipped vehicles as leaders, the human driven vehicles as the follower adopt similar headway, spacing, and acceleration on both freeway and arterial, with no statistically significant difference. The only exception is when traveling at speeds below 15 mph on urban arterials, where human drivers adopt significantly larger spacing while following ACC-enabled vehicles. We expect that findings from these field experiments will provide important initial insights to future research on human driver car following models in a mixed traffic environment and dedicated lanes for automated vehicles.
Title: FIELD EXPERIMENT OF MIXED TRAFFIC – HUMAN DRIVER INTERACTION BETWEEN ADAPTIVE CRUISE CONTROL (ACC) AND HUMAN DRIVERS.
0 views
0 downloads
Name(s): Natrajan, Swarna Lexmi , author
Kan, David, Thesis advisor
Florida Atlantic University, Degree grantor
Department of Civil, Environmental and Geomatics Engineering
College of Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2023
Date Issued: 2023
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 91 p.
Language(s): English
Abstract/Description: Mainstream vehicles sold today are equipped with the Advanced Driver Assistance System (ADAS) known as Adaptive Cruise Control (ACC). ACC automatically adjusts speeds and maintains a safe following distance with the preceding vehicle. This enables partial automation by automating longitudinal car-following. Despite the ever-increasing market penetration, ACC-equipped vehicles will likely operate in a mixed environment with other human-driven vehicles first. However, the traffic flow impact of human driver behavior when following ACC-equipped vehicles is largely unknown, and it is uncertain whether this deserves special consideration when modeling human driver behavior near ACC enabled vehicles. This study conducted a preliminary real-world experiment on a freeway (a portion of Interstate 95) and an urban arterial (a portion of state route A1A) to investigate the human driver behavior with and without the presence of vehicles in ACC mode as the leaders. This unbiased experiment was conducted in naturalistic traffic conditions. Results from the field experiments demonstrate that in a mixed environment with ACC-equipped vehicles as leaders, the human driven vehicles as the follower adopt similar headway, spacing, and acceleration on both freeway and arterial, with no statistically significant difference. The only exception is when traveling at speeds below 15 mph on urban arterials, where human drivers adopt significantly larger spacing while following ACC-enabled vehicles. We expect that findings from these field experiments will provide important initial insights to future research on human driver car following models in a mixed traffic environment and dedicated lanes for automated vehicles.
Identifier: FA00014190 (IID)
Degree granted: Thesis (MS)--Florida Atlantic University, 2023.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Driver assistance systems
Automated vehicles
Automobile drivers--Behavior--Evaluation
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00014190
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.