Artificial Intelligence in Automotive Engineering
This Module is offered by Chair of Automotive Technology (Prof. Lienkamp).
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective
MW2378 is a semester module
in German or English language
at Master’s level
which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
The lecture covers all relevant aspects in the field of "Artificial Intelligence" and "Machine Learning". In addition, all theoretical aspacets will be related to automotive technology topics. 1. Introduction: Historic overviw, overview Machine Learning topics, self driving cars 2. Computer-Vision: Feature Extraktion, Color detection, Canny Edge Detection, Hough Lines, Stereovision 3. Supervised Learning - Lineare Regression: Random Sampling & Consensus 4. Supervised Learning - Classification: Decision Trres, Support Vector Machines, k-nearest Neighbours. 5. Unsupervised Learning - Clustering: Decision Trees, k-Means 6. Path Finding: A* Search 7. Introduction to Neuronal Networs: Perceptron, Loss Function, Activation Function 8. Deep Neuronal Networks: Backpropagation, Different Layers 9. Convolutional Neuronal Networks: Paramter, Filter, Visualization, Pooling 10. Recurrent Neuronal Networks 11. Reeinforcemente Learning 13. AI-Development: Hyperparamter, Training on CPU and GPU, Inference
After the lecture and the excercise the student has an holistic overview in the topic of Artifical Intelligence and Mchine Learning. The sutdent is able to selecte a Machine Learning method for a specific problem. Especially the student is able to solve current problems in the field of automotive technoloy with machine learning methods.
• Attendance of the lecture "Basic of Motor Vehicle Contstruction"
• Basic knowledge in Python
Courses and Schedule
Learning and Teaching Methods
In the lecture, the content of the course is taught by means of a lecture and presentation. More complex issues are derived and illustrated using tablet PCs. During the lecture questions are explicitly asked which expect a transfer payment from the students and which give the students the opportunity to speak and discuss a possible solution. The aim is to deepen the overview of the mechanical processes and to achieve the transfer for applying the mechanical processes to further problems. The lecture also explains simple code examples that can be actively programmed by the students. These code examples are primarily in the field of automotive engineering, which enables the students to work on special problems in the field of automotive engineering with machine learning methods.
After each lecture unit, corresponding learning and programming tasks are handed over to the students in the form of a homework assignment, which deal with the subject matter of the learning unit and serve as preparation for the examination. For example, this is the detection of lanes in Chapter 2 Computer Vision or the detection of vehicles in Chapter 4 by Support Vector Machines. These programming tasks teach the students how machine learning methods can be converted into appropriate code and at the same time how to apply this to problems in vehicle technology.
Lecture, Presentation, Tablet-PC and Beamer
Christopher M. Bishop Neural Networks for Pattern Recognition, 1995
Tom M. Mitchell, Machine Learning, 1997
Christopher M. Bishop, Pattern Recognition and Machine Learning, 2007
David Barber, Bayesian Reasoning and Machine Learning, 2012
Michael Nielsen Neural Networks and Deep Learning, 2014
Pendelten et. al, Perception, Planning, Control, and Coordination for Autonomous Vehicles, Machines 2017, 5(1), 6; https://doi.org/10.3390/machines5010006
Description of exams and course work
In a written exam (duration 90 min) on the one hand the taught contents are applied to the basics of machine learning procedures as well as to various problems from vehicle technology and to be transferred to further tasks. For example, the students should prove in the exam that they have understood the basic mathematics behind the mechanical procedures and can apply them accordingly. The students should also be able to prove that they can select suitable machine learning methods for various problems in vehicle technology and implement them with the appropriate code. A calculator is allowed iin the exam.
By completing the homework after the lecture and submitting 50.00 % correct results (calculated from the average of the percentage points achieved over all individual homework assignments), a grade bonus for the exam can be achieved.
There is a possibility to take the exam in the following semester.