simulation

This project proposes a next-generation safe, efficient and reliable traffic signal control framework powered by advanced roadside sensing and foundation models, specifically Visual Language Models and Multi-Modal Large Language Models.

This project will model how psychological, street, and infrastructure characteristics influence bicyclist behavior in urban settings with inadequate bicycling infrastructure.

This project will develop, assess, and optimize the concept of External Human-Machine Interfaces, which enable intuitive communication between autonomous vehicles and other road users, to foster positive perceptions, build trust, and ensure safe interactions in mixed traffic scenarios.

This project will develop a sensor-informed generative digital twin that integrates real-world data from the Riverside Innovation Corridor’s sensor network. By continuously integrating real-time sensory inputs, the platform can be used to create high-fidelity scenarios and simulate rare and challenging transportation dynamics.