A Computationally Efficient MPC for Green Light Optimal Speed Advisory of Highly Automated Vehicles

Abstract

The current study introduces an approach for energy efficient longitudinal vehicle guidance. The key idea is to utilize a model predictive control (MPC) for the longitudinal vehicle dynamics which explicitly considers the current and the predicted states of multiple traffic lights ahead. Consequently, the vehicle can drive in urban situations much more energy efficient, which can be used to enlarge the range of electric vehicles or save fuel while additionally improving travel time. Modern traffic lights are equipped with transmitters that send information about their actual and upcoming system states. Additionally, traffic lights connected to a traffic control center can broadcast their future signal phases to vehicles many kilometers ahead. This information may be used to adapt the vehicle speed so that engine operation points are optimal and stops can be avoided. These kind of algorithms are referred to as green light optimal speed advisory. This work presents a novel online capable MPC approach that uses a sequential quadratic program to solve the respective optimal control problem. This approach is implemented in a framework introduced as well which allows driving tests in a real vehicle.