Vehicle Technology

This research aims to develop an equitable and sustainable freight‐oriented land use methodology to support future planning activities, facilitate the integration of freight activity across urban, suburban, and rural areas, and facilitate the transition of heavy‐ and medium‐duty vehicles toward zero‐emission. The project will analyze freight distribution patterns considering supply and demand and estimate social, environmental, and labor impacts in different communities.

This project will package research findings on machine vision algorithms for vehicle identification into a deployable, open-source Automated Fleet Classification Toolkit for practitioners and researchers.

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.

To meet sales requirements of zero-emission transit buses, medium-duty (MD) trucks, and heavy-duty (HD) trucks in California, improvements in battery performance and costs are necessary. This project will support this transition through analysis of battery capabilities, charging infrastructure availability, and vehicle routing strategies.

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.

The goal of this research is to understand vehicle interactions in mixed traffic and leverage the understanding to shape AV behaviors, simultaneously benefiting all road users (i.e., ensuring equity) and achieving emission reduction, even with a limited proportion of AVs.

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.

In the research, smart charging will be explored in the larger context of electric vehicle fleets carrying freight and/or people to assess its potential to again decrease charging costs, increase carbon-free energy usage, decrease spatially-concentrated peak demands on the grid, and lower infrastructure investment.

Building on their previous research on environmental life cycle assessment (LCA) for heavy-duty trucks, the researchers will conduct a social LCA analysis to assess the social impacts of battery-electric and fuel cell trucks.

This research will help policymakers understand the obstacles faced by socially vulnerable populations in accessing healthy food, whether those obstacles involve transportation access or awareness.