Real-Time Connected Autonomous Vehicle (CAV) Control for Ecodriving

Leadership

• Coordinated cross-functional team, including industry partners and researchers, to ensure smooth integration of vehicle on-board software, cloud-based communication system, and traffic infrastructure.

Research and Development

• Developed subscription-based Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication between connected and autonomous vehicles (Linux, channels), traffic infrastructure (REST APIs), and a web application (OpenStack, django).
• Derived insights 6 IoT traffic monitoring devices and over 70 virtual sensors through data analysis and visualization. Predicted traffic signal timings and traffic volumes for 6 traffic signals.
• Developed a strategy to handle big sensor data streams.
• Designed and developed data-driven coupling between historic and real-time sensor data. Coordinated integration with predictions, VisSIM simulations, and visual analytics components.

Selected Publications:

• Cloud-Based Computing System for Ecodriving
• Virtual Reality Digital Twins for Autonomous Driving
• Mobile App for Ecodriving

Pending Patents 💡

• Eco-Pilot-Energy-Efficient Vehicle Speed Advisory Through Vehicle-to-Vehicle Communications
• Energy-Efficient Vehicle and/or Traffic Light Control

Awards 🏆

• Best Poster and Demo Paper for our IEEE MASS 2022 paper Mobile App for Ecodriving