Our work is accepted by IEEE Transactions on Intelligent Transportation Systems

How would anticipatory Eco-Driving affect the microscopic mixed traffic energy efficiency?

Utilizing the predictive traffic information, automated vehicle can achieve efficient motion planning and control to significantly reduce its fuel consumption, often noted as “eco-driving” strategy in literatures. The impacts of such a strategy on mixed traffic where human driven vehicles and automated vehicles share the road, however, is unclear due to human drivers’ diverse driving behaviors when interacting with automated vehicles in real world. In our latest work Energetic Impacts Evaluation of Eco-Driving on Mixed Traffic with Driver Behavioral Diversity (accepted by the IEEE Transactions on Intelligent Transportation Systems) we try to answer this question through a data-driven modeling approach. DOI: 10.1109/TITS.2020.3036326

Yao Ma
Assistant Professor

My research interests focus on control and modeling of intelligent vehicle systems for improvement of efficiency, mobility, and safety.