A Comparative Study on Model Predictive Control Design for Highway Car-Following Scenarios: Space-Domain and Time-Domain Model

Abstract

Model predictive control (MPC) has been widely adopted for cooperative adaptive cruise control (CACC) due to its superior performance in achieving fuel-efficient driving while satisfying constraints such as inter-vehicle distance. The core of an MPC-based algorithm is to predict the vehicle's behavior using a dynamic model, and the space-domain vehicle dynamic model is frequently implemented in recent research along with the time-domain vehicle dynamic model. This paper presents a comparative performance analysis between the space-domain and the time-domain models in the MPC framework for the car-following problem. An MPC design process and analysis method for the high-speed car-following scenario is suggested and presented for equivalent performance comparison between the two approaches. In order to analyze trends between speed tracking and fuel-saving performance, which are conflicting objectives as car-following performance, a bi-objective cost function is proposed and manipulated by various weightings. It is observed that the space-domain model presents stable tracking performance, and the time-domain model shows better fuel efficiency. However, the space-domain model with road information is superior in tracking and fuel efficiency compared to the time-domain model with limited road information. Pareto analysis was implemented to visualize and describe performance differences in various situations regarding tracking error, fuel efficiency, and road grade information levels.