• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • ART Lab
  • Research
  • Publications
  • ART Studio
  • People
  • News
  • Contact Us

Adaptive Robotics & Technology Lab

ART Lab

Texas A&M University College of Engineering

Small and rural towns’ perception of autonomous vehicles: insights from a survey in Texas

Muhammad Usman, Wei Li, Jiahe Bian, Andong Chen, Xinyue Ye, Xiao Li, Bahar Dadashova, Chanam Lee, Kiju Lee, Sivakumar Rathinam & Marcia Ory

Transportation Planning and Technology, 47:2, 200-225, DOI: 10.1080/03081060.2023.2259373

September 2023

People’s perceptions of Autonomous Vehicles (AVs) are critical to understanding the role of AVs in future transportation systems. Most previous work on AVs perceptions is based on large cities or metropolitan areas. This study provides a unique perspective regarding perceptions of impacts of AVs in small and rural communities through an online survey in Central Texas (n = 1153). Our questionnaires gathered basic socio-demographic characteristics and AV impacts variables identified from the literature. We used summary statistics and ordered logistic regression models to reveal the perceived impacts of AVs. Residents of small and rural communities, particularly older adults (65 + years), were more enthusiastic about the development of AVs than the national average. Our findings reveal that being an employed, married male with a higher income increases the likelihood of accepting the impacts of AVs, suggesting further research to explore a feasible approach to utilizing AVs in small, rural communities.

Recent Posts

  • Comparison of GPS Collars and Solar-Powered GPS Ear Tags for Animal Movement Studies September 29, 2025
  • Low-Cost, Compact Mobile Robot for Autonomous Soil Monitoring in Crop Fields September 29, 2025
  • Hardware Prototype and System Apparatus of an Autonomous Robotic Harvesting Cell September 29, 2025
  • Multi-Robot Shepherding: A CLF-CBF Approach September 29, 2025
  • Unmanned aerial system and machine learning driven Digital-Twin framework for in-season cotton growth forecasting September 29, 2025

© 2016–2026 Adaptive Robotics & Technology Lab Log in

Texas A&M Engineering Experiment Station Logo
  • State of Texas
  • Open Records
  • Risk, Fraud & Misconduct Hotline
  • Statewide Search
  • Site Links & Policies
  • Accommodations
  • Environmental Health, Safety & Security
  • Employment