Eco-driving strategy for connected vehicles at signalized intersections considering human driver error

In recent years, eco-driving strategies based on connected vehicle (CV) technologies have been studied to assist human drivers to reduce fuel consumption and pollutant emissions. In this paper, a real-time eco-driving strategy for CVs that considers human driver error is proposed to improve both traffic and fuel efficiency at signalized intersections where CVs and human-driven vehicles (HDVs) coexist. Firstly, a human driver error estimation model is established using real-world driving data. Then, based on the signal phase and timing information, vehicle state information, and the estimated human driver errors, a constrained nonlinear optimal control problem (OCP) is proposed to calculate the optimal advisory speed of each CV. The trajectory of HDV is estimated by utilizing the Gipps' car-following model. Fast stochastic model predictive control (SMPC) is employed to solve the proposed OCP effectively. At last, simulation studies and real-vehicle experiments are conducted in various scenarios to verify the performance of the proposed strategy. Simulation and experiment results indicate that compared with the baseline strategies, the proposed eco-driving strategy can significantly reduce travel time and fuel consumption while ensuring the real-time performance.

Automated summary

This paper proposes a real-time eco-driving strategy based on connected vehicle technologies, aiming to improve traffic and fuel efficiency by considering human driver error. By establishing a human driver error estimation model and utilizing signal phase and timing information, vehicle state information, and estimated human driver errors, a constrained nonlinear optimal control problem is solved to calculate the optimal advisory speed of each vehicle. Simulation studies and real-vehicle experiments verify the performance of the proposed strategy.