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IPOD Abstract for presentation (Poster or Podium) with a Paper in the Conference Proceedings

CAV Impacts

1666392 - Human-Autonomous Interplay: Evaluating Driver Trust and Behavior in Mixed Traffic Conditions

Monday, June 17, 2024

11:15 AM - 11:30 AM ET

Room: 218

Primary Author(s)

Saki Rezwana, PhD candidate

Student
University of Connecticut
Willimantic, CT, United States

Presenter(s)

Saki Rezwana, PhD candidate

Student
University of Connecticut
Willimantic, CT, United States

Co-Author(s)

Mohammad Razaur Rahman Shaon, Ph.D.

Associate Research Scientist
Connecticut Transportation Institute, United States
Nicholas Lownes, Ph.D.

Associate Professor
University of Connecticut
Storrs, Connecticut, United States

Submitter(s)

Saki Rezwana, PhD candidate

Student
University of Connecticut
Willimantic, CT, United States

Abstract:

The introduction of Autonomous Vehicles (AVs) into traditional traffic presents novel challenges in safety, trust, and adaptation for existing human-driven environments. The integration of this emerging technology into traditional traffic systems necessitates an understanding of human drivers' adaptation strategies, emphasizing trust and behavioral dynamics in mixed traffic scenarios. This study delves into drivers' trust levels, behavioral adaptations, and interaction strategies with AVs, utilizing a detailed survey and observational methodology to extract insights from drivers familiar with various AV exposure levels. Analysis reveals diverse trust determinants, including perceived AV reliability, firsthand experience, and expected AV performance in traffic. Notably, drivers exhibit behavioral shifts, ranging from heightened caution to dependency, reflecting the profound psychological impacts of AV introduction. To augment coexistence, this research endorses specialized educational initiatives and enhanced vehicle intercommunication systems, promoting proactive driving habits. These strategies are pivotal for harmonizing human-AV interactions, ensuring a collaborative future in mixed traffic landscapes.

Learning Objectives:

Attendees can expect to learn the following from this session:

To identify the key factors influence drivers' trust in Autonomous Vehicles within mixed traffic environments. Includes an understanding of how perceived reliability and expectations of AV performance in traffic scenarios.
Analyze the range of behavioral changes human drivers experience in integration of AVs, including increased caution and a growing dependency on automation. Explore the psychological underpinnings of these adaptive behaviors.
Assess the need for and impact of educational and awareness programs aimed at human drivers. Explore how these initiatives contribute to integration by addressing safety, trust issues, regarding AV technology.
Evaluate the significance of advanced vehicle-to-vehicle communication systems in fostering anticipatory and proactive driving habits. Understand how these systems serve foundational element for enhancing co-adaptation between AV and non- AV