Autonomous Vehicles and Artificial Intelligence

NUMMER:	211044
KÜRZEL:	autoVehAndAI
MODULBEAUFTRAGTE:R:	Prof. Dr. Thorsten Berger
DOZENT:IN:	Prof. Thorsten Berger, Dr. Sven Peldszus
FAKULTÄT:	Fakultät für Informatik
SPRACHE:	Englisch
SWS:	4
CREDITS:	5
ANGEBOTEN IM:	jedes Sommersemester

AKTUELLE TERMINE

Verwendung des Moduls (in anderen Studiengängen):
B.Sc. Informatik, B.Sc. Angenwandte Informatik, M.Sc. Angewandte Informatik
Stellenwert der Note für die Endnote: 5 / 170
(Im Studiengang werden Module im Umfang von 170 CP benotet und 10 CP nicht benotet)

LERNFORM

Lecture with practical exercises

LERNZIELE

Learning goals:
∙ Understanding requirements on autonomous vehicles
∙ Understanding the architecture of autonomous vehicles
∙ Ability to build a self-driving car with ROS2
∙ Understanding and applying quality assurance for autonomous vehicles

INHALT

Autonomous driving is the future of individual mobility and all major manufacturers are
working on fully autonomous vehicles. While there are robust and good solutions for the
individual problems in autonomous driving, the main challenge lies in their integration. Altogether, an autonomous vehicle’s software is the biggest problem. Therefore, the key in
self-driving vehicles is about getting the software right. In this course, we will investigate the
different aspects of self-driving vehicles as well as the importance and application of artificial
intelligence in this domain. The course will primarily focus on the following topics:
∙ Requirements on autonomous vehicles
∙ Architecture of autonomous vehicles
∙ Operation systems and frameworks for robotic systems
∙ Specification and Implementation of autonomous vehicles based on ROS2
∙ Artificial intelligence for autonomous vehicles
∙ Simulation of autonomous vehicles
∙ Localization and perception
∙ Mission planning
∙ Quality assurance for autonomous vehicles In the course’s lecture, we provide the required theoretical background and practically apply the course’s content in exercises by
building a self-driving robot.

VORAUSSETZUNGEN CREDITS

Bestandene Modulabschlussprüfung und Teilnahme an allen Zwischengesprächen

EMPFOHLENE VORKENNTNISSE

The Software Engineering lecture or a comparable course.
Programming experiences e.g. as part of other courses.

LITERATUR

S. Liu, L. Li, J. Tang, S. Wu, J.-L. Gaudiot. Creating Autonomous Vehicle Systems, Morgan and Claypool Publishers, 2020

SONSTIGE INFORMATIONEN

Anmeldungen bis zum 27.03.22 über: https://evastud.uv.ruhr-uni-bochum.de/evasys/
online.php?p=avai-lecture
Das Modul kann entweder im fortgeschrittenen Bachelorstudium oder im Masterstudium belegt werden.