See TUMonline

After participating in the module, students have an in-depth insight into the basic interrelationships of autonomous vehicles. The students are able to explain the most important hardware components (sensors, actuators, computing platforms) of autonomous vehicles and how they function and interact. Furthermore, the students are able to understand the challenging tasks of autonomous driving (environment perception, path planning, control) and their basic problems in order to be able to analyze real measurement data and develop suitable algorithms to solve these problems.

The lecture covers the basic concepts and principles underlying the development and operation of autonomous vehicles. Numerous variants and applications of autonomous vehicles are highlighted, in the context of the lecture we focus on autonomous road vehicles. Overall, the lecture provides a broad overview of the major concepts and technologies driving the development of autonomous vehicles and their impact on society. In the lecture, we learn about the hardware components used in autonomous vehicles, such as sensors, actuators, and computing platforms. We teach in the lecture the fundamental problems that need to be solved for the development of autonomous vehicles, such as environment perception, localization, path planning, decision making, and control. We then discuss the software modules used to control the behavior of autonomous vehicles. In addition, we cover the use of simulations in the development and testing of autonomous vehicles, as well as the legal and ethical considerations that must be taken into account when deploying autonomous vehicles in the real world. The lecture will conclude with a discussion of current research and development trends in autonomous vehicles and possible future developments.

No content prerequisites are required.

In the lecture, the teaching content is conveyed by means of lecture and presentation (Power Point). In the process, more complex issues are derived and illustrated using a tablet PC. During the lecture, explicit questions are posed that expect a transfer performance from the students and in which the students are given the opportunity to speak up and discuss a possible solution. In this way, the challenging tasks of autonomous driving are to be deepened and the transfer to the application of hardware and software to various other applications (e.g. robots in agriculture) is to be achieved. Also explained in the lecture are simple situational examples that must be solved by autonomous vehicles. These example tasks can be actively solved by the students. These examples are primarily in the area of road vehicles (e.g. street intersection in the city center), which subsequently enables the students to analyze and evaluate further problems of other autonomous systems (e.g. robots in agriculture). A weekly office hour is offered for answering questions about the individual appointments and homework, which can be attended in presence or online (announcement of the appointment via Moodle).

The module examination takes the form of a written exam (duration 90 min, permitted aids: calculator). The fundamentals of the hardware and software of autonomous vehicles are examined on the basis of short questions. By means of comprehension questions and transfer questions, the participants show, for example, that they have understood the individual software components of autonomous vehicles, can analyze real measurement data and can analyze the behavior of autonomous vehicles.

Pendleton et. al, Perception, Planning, Control, and Coordination for Autonomous Vehicles, Machines 2017, 5(1), 6; https://doi.org/10.3390/machines5010006 M. Maurer, B. Lenz, H. Winner, and J. C. Gerdes, Autonomous Driving: Technical, Legal and Social Aspects. s.l.: Springer, 2016. M. H. Daniel Watzenig, Ed., Automated Driving: Springer International Publishing, 2017. A. Faisal, T. Yigitcanlar, M. Kamruzzaman, and G. Currie, “Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy,” JTLU, vol. 12, no. 1, 2019, doi: 10.5198/jtlu.2019.1405.