Pedestrian Behavior and Interaction with Autonomous Vehicles

Connected and autonomous vehicles (CAV) are advancing in many ways in the current automobile market, such as adaptive cruise control, forward collision warning, and lane detection. By 2023, worldwide net additions of vehicles equipped with hardware that could enable autonomous driving without human supervision may exceed 700,000 units, which is up from 137,129 units in 2018. McKinsey & Co estimated that self-driving vehicles would eliminate 90% of the vehicle accidents in the United States and save up to US$190 billion of the expenses related to damages and health costs while also saving thousands of lives. It is expected that self-driving technology will enable the efficient use of traffic patterns, reduce traffic congestion, and increase roadway capacity. CAV will have the ability to understand the environment around them without any human involvement. Studying the interactions between pedestrians and autonomous vehicles is challenging due to the complexity of this interaction process. Pedestrians often rely on eye contact, hand motions, or audible dialogue with human drivers to accomplish roadway crossings. However with CAVs there is no driver with whom to interact. The lack of human interaction and communication inherent with CAV technology could influence unpredictable pedestrian behavior. Autonomous vehicles are expected to be designed to attempt to overcome this challenge. Communication systems between CAV and pedestrian are being developed and tested. However, how CAVs interact with pedestrians is a relatively unexplored topic due to the difficulty in replicating pedestrian-CAV interactions in a safe manner. This study proposes the use of virtual reality (VR) as a means to overcome the safety challenges inherent in studying pedestrian-vehicle interactions and will focus on identifying any differences in pedestrian behavior when CAV are introduced to the traffic stream. The central research question of this proposal is: Are there significant behavioral changes in the way pedestrians interact with vehicles at a crossing when a portion of the vehicles is autonomous? The proposed research will focus on the following topics: (1) To determine the impact of autonomous vehicles on pedestrian measures such as gap acceptance, waiting time while crossing the road and pedestrian acceleration. (2) Measuring any pedestrian behavior changes with the automation level of the vehicle. (3) Measuring the psychophysiological (e.g., Electrodermal Activity-EDA, blood pressure, and heart rate change) changes of the pedestrians’ while interacting with AV.

Language

• English

Project

• Status: Completed
• Funding: $90007
• Contract Numbers:

  69A3551747133

• Sponsor Organizations:

  Center for Advanced Multimodal Mobility Solutions and Education

  University of North Carolina, Charlotte
  Charlotte, NC  United States  28223

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  University of North Carolina - Charlotte

  9201 University City Blvd
  Charlotte, North Carolina  United States  28223-0001
• Project Managers:

  Fan, Wei

• Performing Organizations:

  University of Connecticut

  Sponsored Programs Services
  438 Whitney Road Ext 1133
  Storrs, CT  United States  06269-1133
• Principal Investigators:

  Lownes, Nicholas

• Start Date: 20201001
• Expected Completion Date: 20220930
• Actual Completion Date: 20220930

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Behavior; Connected vehicles; Crosswalks; Pedestrian movement; Pedestrian vehicle interface; Pedestrians; Virtual reality
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Pedestrians and Bicyclists; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01754808
• Record Type: Research project
• Source Agency: Center for Advanced Multimodal Mobility Solutions and Education
• Contract Numbers: 69A3551747133
• Files: UTC, RIP
• Created Date: Oct 17 2020 2:04PM