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Practical: Search-Based Test Scenario Generation for Autonomous Driving (IN0012; IN2106, IN4228)

Course 0000002081 in WS 2017/8

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
Thomas Hutzelmann

Assignment to Modules

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 the approach of search-based test scenario generation for testing advanced driver assistance systems and autonomous systems, using the example of adaptive cruise control and lane keeping assistant (similar to [1] and [2]). Our scientific goal is to confirm the applicability of the approach in the given context. We will use MATLAB, since MATLAB provides according libraries and visualization capabilities, and the industrial simulation software CarMaker from the company IPG Automotive, which is the industrial cooperation partner for this practical course. This practical course offers the opportunity to learn about search-based test generation techniques. Additionally, you can deepen your knowledge about MATLAB (high importance in the fields of scientific computing, machine learning, avionics, and the automotive industry) and CarMaker (industrially used in many fields of automotive engineering).
Links E-Learning course (e. g. Moodle)
TUMonline entry
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