Reliable, lane-level, absolute position determination for connected and automated vehicles (CAV’s) is near at hand due to advances in sensor and computing technology. This project investigated, analyzed, and demonstrated these related technologies.
Attendees at the Consumer Electronics Show, or CES, got to try out cooperative merging technology created at the Center for Environmental Research and Technology, or CE-CERT, using a high-fidelity driving simulator.
This dissertation developed predictive models and trajectory planning algorithms using machine learning and optimization techniques to address four key challenges of energy efficient driving of connected and automated vehicles.
The purpose of this project is to address the issue of robustness in the design of variable speed limit and bring them closer to successful implementation with consistent and well-understood benefits.
This project will conduct a literature review and expert survey to investigate the most promising emerging technologies for advanced speed management, such as Connected and Autonomous Vehicle technologies, Advanced Driver-Assistance System messaging, Advanced Traveler Information Systems, Advanced Vehicle Location, remote sensing and detection technologies for pedestrians, bicycles, and other vulnerable users; advanced signal timing technologies, etc.
Researcher Matt Barth describes a holistic assessment and examines the co-benefits and trade-offs between safety, mobility and the environment for a variety of CAV applications.
