The OPA3L System and Testconcept for Urban Autonomous Driving
by , , , , , , , , , , , ,
Abstract:
The development of autonomous vehicles for urban driving is widely considered as a challenging task as it requires intensive interdisciplinary expertise. The present article presents an overview of the research project OPA3L (Optimally Assisted, Highly Automated, Autonomous and Cooperative Vehicle Navigation and Localization). It highlights the hardware and software architecture as well as the developed methods. This comprises algorithms for localization, perception, high- and low-level decision making and path planning, as well as model predictive control. The research project contributes a cross-platform holistic approach applicable for a wide range of real-world scenarios. The developed framework is implemented and tested on a real research vehicle, miniature vehicles, and a simulation system.
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PDF URL: https://ieeexplore.ieee.org/document/9922416
Reference:
The OPA3L System and Testconcept for Urban Autonomous Driving (Andreas Folkers, Constantin Wellhausen, Matthias Rick, Xibo Li, Lennart Evers, Verena Schwarting, Joachim Clemens, Philipp Dittmann, Mahmood Shubbak, Tom Bustert, Gabriel Zachmann, Kerstin Schill, Christof Büskens), In 25th IEEE International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2022.
Bibtex Entry:
@INPROCEEDINGS{folkers2022opa3l,
	author={Folkers, Andreas and Wellhausen, Constantin and Rick, Matthias and Li, Xibo and Evers, Lennart and Schwarting, Verena and Clemens, Joachim and Dittmann, Philipp and Shubbak, Mahmood and Bustert, Tom and Zachmann, Gabriel and Schill, Kerstin and Büskens, Christof}, 
	booktitle={25th IEEE International Conference on Intelligent Transportation Systems (ITSC)}, 
	title={The {OPA3L} System and Testconcept for Urban Autonomous Driving}, 
	year={2022}, 
	pages={1949-1956}, 
	publisher={IEEE},
	doi={10.1109/ITSC55140.2022.9922416},
	url={https://ieeexplore.ieee.org/document/9922416},
	keywords={opa3l},
	abstract={The development of autonomous vehicles for urban driving is widely considered as a challenging task as it requires intensive interdisciplinary expertise. The present article presents an overview of the research project OPA3L (Optimally Assisted, Highly Automated, Autonomous and Cooperative Vehicle Navigation and Localization). It highlights the hardware and software architecture as well as the developed methods. This comprises algorithms for localization, perception, high- and low-level decision making and path planning, as well as model predictive control. The research project contributes a cross-platform holistic approach applicable for a wide range of real-world scenarios. The developed framework is implemented and tested on a real research vehicle, miniature vehicles, and a simulation system.}
}