Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles
Abstract
The introduction of Automated Vehicles (AVs) into the transportation network is expected to improve system performance, but the impacts of AVs in mixed traffic streams have not been clearly studied. As AV's market penetration increases, the interactions between conventional vehicles and AVs are inevitable but by no means clear. This study aims to create new knowledge by quantifying the behavioral changes caused when conventional human-driven vehicles follow AVs and investigating the impact of these changes (if any) on safety and the environment. This study analyzes data obtained from a field experiment by Texas A&M University to evaluate the effects of AVs on the behavior of a following human-driver. The dataset is comprised of nine drivers that attempted to follow 5 speed-profiles, with two scenarios per profile. In scenario one, a human-driven vehicle follows an AV that implements a human driver speed profile (base). In scenario two, the human-driven vehicle follows an AV that executes an AV speed profile. In order to evaluate safety, these scenarios are compared using time-to-collision (TTC) and several other driving volatility measures. Likewise, fuel consumption and emissions are used to investigate environmental impacts. Overall, the results show that AVs in mixed traffic streams can induce behavioral changes in conventional vehicle drivers, with some beneficial effects on safety and the environment. On average, a driver that follows an AV exhibits lower driving volatil…
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VF is capable of quantitatively analyzing irregular changes in indicators over time and has been reported to be effectively used for driving behavior analysis.
“VF is capable of quantitatively analyzing irregular changes in the indicators over time and has been reported to be effectively used for driving behavior analysis [46,47].”
From Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic (2024) · cites this paper as [47]
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- Authors
- Mahdinia, I., Mohammadnazar, A., Arvin, R., & Khattak, A. J.
- Affiliations
- University of Tennessee at Knoxville (US)
- Corresponding author
- Khattak, Asad J.
- Venue
- Accident Analysis & Prevention
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 152
- Pages
- 106006
- ISSN
- 0001-4575 (ISSN-L)
- OpenAlex
- W3127056839
- Cited by
- 127
- References
- 72
- Citations by year
- 2026: 19 · 2025: 29 · 2024: 32 · 2023: 20 · 2022: 16 · 2021: 11
- FWCI
- 8.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Accident Analysis & Prevention (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Federal Highway Administration
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- autonomous vehicles, mixed traffic, human-driven vehicles, following behavior, driver behavior change, time-to-collision, speed profiles, road safety, fuel consumption, vehicle emissions, environmental impact
- MeSH terms
- Acceleration, Accidents, Traffic, Automobile Driving, Humans, Robotics, Safety, Texas, Time Factors
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