CAREER: A Combined Experimental and Theoretical Study of Human-Connected Automated Vehicle Interactions

This Faculty Early Career Development (CAREER) grant will contribute to the national economy and welfare by enhancing the transportation system efficiency and safety. This will be achieved through introducing a novel congestion management strategy based on vehicle automation and communications technology. The current automated vehicle development efforts mainly focus on the safety of isolated vehicles with little consideration of the impacts of design on human drivers and on transportation system efficiency.

Such design approaches, in many cases, can potentially result in additional congestion and unforeseen safety issues for human drivers. Through careful characterization of the interactions between human drivers and connected automated vehicles (CAVs), this CAREER project will lead to new paradigms for congestion management and will result in more efficient utilization of our roadway system and a significant reduction in congestion throughout the transportation system.

This grant will also result in the development of an online intelligent tutoring system that offers a systematic approach to teach students the skills and expertise required to function within highly diverse and dynamic work environments of the future. This customizable educational platform can be adopted by other disciplines and other universities across the nation.

The findings of this study will be disseminated to a wide range of audiences through round table discussions, stakeholder meetings, and webinars.

The research objective of this CAREER grant is to test the hypothesis that humans exhibit a different decision-making logic when interacting with CAVs in comparison with when interacting with other humans. This hypothesis will be evaluated using extensive field tests and data collection from human driver behavior in response to various CAV maneuvers.

This project will also explore the potential for the evolution of human behavior in response to CAVs over time and will introduce robust models to capture such changes in human behavior. Utilizing the findings from the field experiments, this project will lead to the introduction of decentralized CAV-based traffic shaping methodologies that can maintain a desired traffic state by utilizing CAVs to influence the behavior of human drivers.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.