Artificial Intelligence in Automotive Engineering

Mechanical Engineering

About this course

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

Learning outcomes

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.


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;

Course requirements

• Attendance of the lecture "Basic of Motor Vehicle Contstruction" • Basic knowledge in Python


In the lecture, the theoretical basics of the course are 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.

Link to more information

  • Credits
    ECTS 5
  • Contact hours per week
  • Instructors
    Markus Lienkamp, Frank Diermeyer
  • Mode of instruction
  • Course coordinator
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There are currently no offerings available for students of CTU (Czech Republic)