Model predictive control for adaptive cruise control with multi-objectives: comfort, fuel-economy, safety and car-following

For automated vehicles, comfortable driving will improve passengers' satisfaction. Reducing fuel consumption brings economic profits for car owners, decreases the impact on the environment and increases energy sustainability. In addition to comfort and fuel-economy, automated vehicles also have the basic requirements of safety and car-following. For this purpose, an adaptive cruise control (ACC) algorithm with multi-objectives is proposed based on a model predictive control (MPC) framework. In the proposed ACC algorithm, safety is guaranteed by constraining the inter-distance within a safe range; the requirements of comfort and car-following are considered to be the performance criteria and some optimal reference trajectories are introduced to increase fuel-economy. The performances of the proposed ACC algorithm are simulated and analyzed in five representative traffic scenarios and multiple experiments. The results show that not only are safety and car-following objectives satisfied, but also driving comfort and fuel-economy are improved significantly.