Autonomous Driving Based on Nonlinear Model Predictive Control and Multi-Sensor Fusion
by , , , ,
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.
Reference:
Autonomous Driving Based on Nonlinear Model Predictive Control and Multi-Sensor Fusion (Matthias Rick, Joachim Clemens, Laura Sommer, Kerstin Schill, Christof Büskens), In 10th IFAC Symposium on Intelligent Autonomous Vehicles (IAV2019), 2019.
Bibtex Entry:
@inproceedings{rick2019autonomous,
	author = {Rick, Matthias and Clemens, Joachim and Sommer, Laura and Schill, Kerstin and B\"uskens, Christof},
	title = {Autonomous Driving Based on Nonlinear Model Predictive Control and	Multi-Sensor Fusion},
	booktitle = {10th IFAC Symposium on Intelligent Autonomous Vehicles (IAV2019)},
	year = {2019},
	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.}
}