  This paper presents a safe learning-based eco-driving framework tailored for
mixed traffic flows, which aims to optimize energy efficiency while
guaranteeing safety during real-system operations. Even though reinforcement
learning (RL) is capable of optimizing energy efficiency in intricate
environments, it is challenged by safety requirements during the training
process. The lack of safety guarantees is the other concern when deploying a
trained policy in real-world application. Compared with RL, model predicted
control (MPC) can handle constrained dynamics systems, ensuring safe driving.
However, the major challenges lie in complicated eco-driving tasks and the
presence of disturbances, which respectively challenge the MPC design and the
satisfaction of constraints. To address these limitations, the proposed
framework incorporates the tube-based enhanced MPC (RMPC) to ensure the safe
execution of the RL policy under disturbances, thereby improving the control
robustness. RL not only optimizes the energy efficiency of the connected and
automated vehicle in mixed traffic but also handles more uncertain scenarios,
in which the energy consumption of the human-driven vehicle and its diverse and
stochastic driving behaviors are considered in the optimization framework.
Simulation results demonstrate that the proposed algorithm, compared with RMPC
technique, shows an average improvement of 10.88% in holistic energy
efficiency, while compared with RL algorithm, it effectively prevents
inter-vehicle collisions.
