Automatic Tuning of an MPCC-based Motion Planner

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Research and development of motion control in the field of autonomous driving is significantly increasing nowadays. Model predictive control (MPC) is one of the most powerful and practical tools currently available. It is important to select the parameters of the MPC, such as weights, in a way so that different control objectives can be met within the desired performance constraints. The tuning procedure of the MPC variables can be achieved automatically by employing appropriate approaches. To make the tuning process more robust to different scenarios, one approach is to choose a decision-making architecture that provides guidance. This thesis therefore aims at developing a system integrating an automatic tuning method with a decision-making module. In order to achieve the objectives, Genetic Algorithm and Behavior Tree are employed on top of an existing motion planner. The motion planner is based on model predictive contouring control (MPCC) and the proposed method is tested in CARLA simulation environment. This report also highlights the limitations of the proposed automatic tuning method and gives concrete recommendations on how to deal with the shortcomings.