A Customisable Longitudinal Controller of Autonomous Vehicle using Data-driven MPC

Model Predictive Control (MPC) is a high-performing solution for Autonomous Vehicle’s (AV) control. This technique can tailor balance between various aspects of vehicle dynamics such as vehicle’s speed, acceleration and jerk. This study proposes a longitudinal controller for AV using a data-driven MPC based on human driving demonstration. A novel parameterised cost function-based MPC is designed in order to provide a general solution for different driving scenarios. This parametric cost function provides a customisable approach towards longitudinal motion generation by learning a proper set of parameter values from the user’s driving style. Instead of using any classification technique for identifying driving styles, we asked human drivers to drive with different styles and use that data directly to learn the values of the parameters. The Bayesian Optimisation (BO) approach is used to learn an optimised set of parameters minimising the gap between some carefully chosen feature values of the controller and human-generated motion. The observations of simulation show that the proposed controller is capable of generating customisable longitudinal vehicle speed, acceleration, jerk, as well as headway distance between vehicles based on a specific human driving style.