Advanced Practical Course Evolutionary Generation of Test Scenarios for Autonomous Driving (IN2106, IN4201)
Course 0000001442 in WS 2016/7
General Data
| Course Type | practical training |
|---|---|
| Semester Weekly Hours | 6 SWS |
| Organisational Unit | Informatics 4 - Chair of Software & Systems Engineering (Prof. Pretschner) |
| Lecturers |
Florian Hauer Responsible/Coordination: Alexander Pretschner |
| Dates |
Assignment to Modules
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IN2106: Master-Praktikum / Advanced Practical Course
This module is included in the following catalogs:- Further Modules from Other Disciplines
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | The complexity of modern driver assistance systems increases continuously. This makes testing them more and more difficult. Significant problems are caused by the large input space and complex interactions with the environment. On the one hand, human testers cannot easily identify all relevant test scenarios (they can be non-intuitive), and on the other hand, the number of possible test scenarios is very large so that they cannot all be used. A possible solution is to generate and select test scenarios algorithmically. In this practical course, we want to implement and evaluate an evolutionary approach [1] using the example of a parking assistant. For this, we will first implement abstract models of the parking assistant and the environment and then experimentally evaluate the possibilities and limitations of the approach. We will use MATLAB, since MATLAB provides according libraries and visualization capabilities. This practical course offers the opportunity to learn about model-based testing and evolutionary algorithms. Additionally, you can deepen your knowledge about MATLAB which is of high importance in the fields of scientific computing, machine learning, avionics, and the automotive industry. |
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| Links |
E-Learning course (e. g. Moodle) TUMonline entry |