Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification
<|reference_start|>Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification: This paper addresses the eco-driving problem for connected vehicles on urban roads, considering localization uncertainty. Eco-driving is defined as longitudinal speed planning and control on roads with the presence of a sequence of traffic lights. We solve the problem by using a data-driven model predictive control (MPC) strategy. This approach involves learning a cost-to-go function and constraints from state-input data. The cost-to-go function represents the remaining energy-to-spend from the given state, and the constraints ensure that the controlled vehicle passes the upcoming traffic light timely while obeying traffic laws. The resulting convex optimization problem has a short horizon and is amenable for real-time implementations. We demonstrate the effectiveness of our approach through real-world vehicle experiments. Our method demonstrates $12\%$ improvement in energy efficiency compared to the traditional approaches, which plan longitudinal speed by solving a long-horizon optimal control problem and track the planned speed using another controller, as evidenced by vehicle experiments.<|reference_end|>
@article{joa2024eco-driving,
    title={Eco-driving under localization uncertainty for connected vehicles on
  Urban roads: Data-driven approach and Experiment verification},
    author={Eunhyek Joa, Eric Yongkeun Choi, Francesco Borrelli},
    journal={arXiv preprint arXiv:2402.01059},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01059},
    primaryClass={eess.SY cs.SY}
}