Autonomous & Connected Vehicles

Connected vehicles (CV) use various communication technologies to exchange information with other cars on the road (vehicle-to-vehicle [V2V]), roadside infrastructure (vehicle-to-infrastructure [V2I]), and the “Cloud” [V2C]. Autonomous, or “self-driving” vehicles are defined by the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA) as “those in which operation of the vehicle occurs without direct driver input to control the steering, acceleration, and braking and are designed so that the driver is not expected to constantly monitor the roadway while operating in self-driving mode.”  Connected and/or Autonomous Vehicle (CV/AV/CAV) technologies are among the most attractive automotive technologies, and they  continue to evolve and rapidly develop new capabilities.  It is widely believed that CV/AV/CAV have great potential to not only improve vehicle safety, but also to improve traffic mobility, efficiency and sustainability. The UFTI is  a leader in this area, both with respect to technology development as well as traffic management. It has competed nationally in events such as the DARPA Urban Challenge and is still actively conducting related research.

Related on-going research projects include but are not limited to the following list.

Example Projects

Title Principal Investigator Agency/Source Description
LiDAR Sensor (Future Data Collection Progress) Dr. Carl Crane, III FDOT This research will compare collection times, costs, safety, and data consistency between traditional collection and LiDAR collection.
Traffic Signal Control with Connected and Autonomous Vehicles in the Traffic Stream

For updated information visit the AVIAN website here: [link]

Dr. Lily Elefteriadou NSF This project develops signalized intersection control strategies and other enabling sensor mechanisms for jointly optimizing vehicle trajectories and signal control by taking advantage of existing advanced technologies (connected vehicles and vehicle to infrastructure communications, sensors, autonomous vehicle technologies, etc.).
Development and Testing of Optimized Autonomous and Connected Vehicle Trajectories at Signalized Intersections Dr. Lily Elefteriadou FDOT The objectives of this research were to develop, test, and deploy an intelligent real-time intersection traffic control system in order to optimize simultaneously signal control and automated vehicle trajectories when the traffic stream consists of autonomous, connected, and conventional vehicles.
Policy Implications of Automated Vehicle Technology Dr. Siva Srinivasan FDOT The main objective of this research project is to provide the Florida Department of Transportation (FDOT) with the necessary information and guidance on how to most appropriately draft and implement policies associated with automatic vehicle (AV) technology.
Collaborative Research: Coordinated Real-Time Traffic Management Based on Dynamic Information Propagation and Aggregation under Connected Vehicle Systems Dr. Lili Du NSF This study seeks to develop real-time coordinated traffic management strategies built upon dynamic information propagation and aggregation in connected vehicle systems. To achieve this goal, it investigates three interrelated research challenges: (i) information propagation dynamics, (ii) distributed information aggregation, and (iii) coordinated real-time traffic management.
CAREER: Integrated Online Coordinated Routing and Decentralized Control for Connected Vehicle Systems Dr. Lili Du NSF The goal of this Faculty Early Career Development (CAREER) program award is to develop innovative approaches to the coordination of connected vehicle drivers- online route choices.

More projects can be found on the TRID database at


Lily Elefteriadou, Ph.D.

Professor of Civil and Coastal Engineering

Siva Srinivasan, Ph.D.

Associate Professor Civil & Coastal Engineering

Carl Crane, Ph.D.

Professor of Mechanical Engineering

Lili Du, Ph.D.

Professor of Civil and Coastal Engineering