ii Automotive Path Following using Model Predictive Control

The field of autonomous driving is rapidly expanding and extensive research is conducted in this field. Many of the autonomous functions may be simplified to the desire to follow a path, among them steer assist, self-parking and collision avoidance. The general idea is that an autonomous vehicle would be able to do the maneuver faster, safer and more accurate than a human driver. Model Predictive Control (MPC) utilizes a model of the system and therefore has detailed knowledge of the system’s future behavior. Based on this knowledge, an opti-mization problem can be solved to obtain an optimal input corresponding to a desired behavior. MPC can handle multiple variables and natively offers possibilities for per-formance trade-offs and actuator limitations. It can operate closer to constraints than conventional controllers. Since path following is a complex problem with multiple vari-ables MPC is considered an attractive approach. Throughout this thesis an MPC framework for automotive path following is developed, which is suitable for both simulation and vehicle. The project considered two application