A suggestion-based fuel efficient control framework for connected and automated vehicles in heterogeneous urban traffic

Abstract

This paper proposes a novel robust suggestion-based control framework based on model predictive control (MPC) to optimize the fuel efficiency of connected and automated vehicles (CAVs) in heterogeneous urban traffic (i.e., including both CAVs and human-driven vehicles (HDVs)). In this suggestion-based control framework, the CAVs are considered to provide suggestion commands to the HDVs to follow. These suggestion commands are the velocities the CAVs want the HDVs to follow to improve their own, as well as the group’s fuel economy. The host CAV thus needs to find its own fuel-efficient control solution, as well as the suggestion commands to be provided to its preceding HDV. We assume the CAVs can communicate with the HDVs via Vehicle to Vehicle (V2V) communication, and the Signal Phase and Timing (SPaT) information are available to the CAVs through Vehicle to Infrastructure (V2I) communication. The suggested velocity commands are held constant for a predefined time to allow the driver in the HDV to reach the suggested velocity. We also consider that the suggested velocity commands are non binding, i.e., a driver can choose not to follow the suggested velocity. For this control framework to function, we present a velocity prediction model based on experimental data that captures the response of a HDV to different suggested-commands, and a robust approach to ensure collision avoidance. The velocity prediction’s accuracy is also validated with the experimental data (on a table-top drive simulator), and the results are presented in this paper. Simulation studies show the proposed control strategy’s efficacy compared with existing baseline methods.