Model Predictive Control for Integrated Motion Planning and Control of Automated Vehicles

• Applicant: Professor Dr. Georg Schildbach
• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics ; Traffic and Transport Systems, Intelligent and Automated Traffic
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 460891204

The main goal of this project is to improve the technology of Model Predictive Control (MPC) for automated vehicles. MPC has been identified as a promising approach for the trajectory tracking problem in several research projects. Its key advantage is the holistic formulation of nonlinear multi-variable control problems with constraints, which is versatile and intuitive. However, MPC has not found access into industrial production yet, for several well-known reasons. Most importantly, MPC is computationally rather expensive and it requires a high degree of implementation skills. Therefore this project aims at mitigating these drawbacks, while further strengthening the advantages of MPC. In particular, the focus will be on the approach of Scenario-based MPC (SCMPC), on which the applicant has worked intensively over the past years. This work shall be continued in order to strengthen and expand the fundamental theory of SCMPC. Moreover, the scope of MPC shall be extended to comprise the two basic tasks of trajectory tracking control and motion planning. This leads to a simplification of the underlying software architecture and a reduction of the required interfaces compared to other approaches. The particular strength of MPC of incorporating constraints can be used to explicitly derive safety guarantees for the closed-loop system. To avoid unnecessary conservatism in the behavior of the automated vehicle, the future behavior of the surrounding traffic has to be interpreted and anticipated. To this end, the framework of SCMPC uses scenarios for the prediction of the behavior of other traffic participants. The main idea is that a bundle of scenarios (or particles) can be used to express the likely future behavior of other agents as well as its inherent uncertainty. This approach is computationally efficient, intuitive, and easy to implement, because it can draw directly from empirical driving data. Explicit safety guarantees can be derived by the theory on scenario-based optimization.In the second part of this project, a user-friendly toolbox will be developed that facilitates the implementation of MPC on a wide range of hardware systems. This toolbox will significantly reduce the computational burden of MPC, as it builds on efficient state-of-the-art algorithms that are specifically tailored for the task of integrated vehicle motion planning and control. The toolbox includes a code generator that can be operated via a graphical user interface (GUI). Thus it abstracts most of the difficulties of the implementation process and standalone C code can be produced for the controller, which can be integrated and tested on most embedded platforms.In summary, this project will expand the current state of technology of MPC with respect to automated driving, and it will significantly simplify its industrial application.

• DFG Programme: Research Grants