Autonomous Driving Based on Nonlinear Model Predictive Control and Multi-Sensor Fusion

Abstract

Abstract Algorithms for controlling fully autonomous systems must meet especially high requirements with respect to safety and robustness. A particularly challenging example are autonomous deep space missions, which we investigated in several projects. In this context, we showed that a safe and robust autonomous system can be realized through nonlinear model predictive control approaches using optimization techniques in combination with multi-sensor fusion based on an extended representation of uncertainty. The focus of this paper is on demonstrating the versatility of that concept by transferring the corresponding algorithms to the also very challenging application of autonomous driving. In particular, we propose a system concept for a self-driving car based on our methodology. Furthermore, we present results of a real world research vehicle that autonomously explores a parking lot, dynamically takes obstacles into account, and finally performs a parking maneuver.