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
Our work is accepted by IEEE Transactions on Intelligent Transportation Systems
How would anticipatory Eco-Driving affect the microscopic mixed traffic energy efficiency?
- One paper was presented at 2020 ASME Dynamic Systems and Control Conference
- Our work is accepted by ASME Journal of Dynamic Systems, Measurement and Control
- One paper was presented at 2020 American Control Conference
- Incoming project on vehicle efficiency and alternative energy
- Mehmet is selected as Community of Scholar at Texas Tech University! Congratulations!