Nico Steinhardt, Raphael Wenzel, Malte Probst , Markus Amann, "Lateral Model Predictive Control for Autonomous Vehicle Prototypes", IFAC World Congress 2023, 2023.

This paper shows a (lateral) Model Predictive Control (MPC) implementation on an Autonomous Driving (AD) prototype. Rapid prototyping and testing of AD functions in a realistic environment is a crucial step to understanding the advantages and shortcomings of algorithms in research and development of AD. Prototype vehicles show a specific set of requirements which differ from the control deployed in the final products. Vehicles are equipped with steering and pedal actuation, as well as high-precision localization systems. The control system is used for lateral trajectory control – it expects desired trajectory commands from a high-level motion planner. Prototype vehicle control has high requirements on precise control due to actuation lag and velocity-dependent vehicle dynamics, making MPC especially suited for such applications. The dynamic models used in the shown control allow for simple identification and parametrization by conducting basic driving maneuvers and applying a series of commands to the actuators while recording the vehicle’s reactions with a reference measurement system. As these dynamic effects are heavily velocity-dependent, the model linearizes its internal equations at the expected velocity, which is part of the trajectory command. This enables a wide velocity range of control, reaching from standstill to about 70 km/h. In this paper, we will present both the model and architecture of the lateral control as well as the identification steps necessary to deploy it.