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Masterpraktikum - Planning Robust Behavior for Autonomous Driving (IN2106, IN4251)

Course 0000005307 in WS 2019/20

General Data

Course Type practical training
Semester Weekly Hours 6 SWS
Organisational Unit Informatics 6 - Chair of Robotics, Artificial Intelligence and Real-time Systems (Prof. Knoll)
Lecturers Klemens Esterle
Patrick Hart
Alexander Lenz
Responsible/Coordination: Alois Christian Knoll

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 (31.07.2019) There are some available seats in this seminar for WS2019/20. If you're interested to be registered please write an email to "esterle(at)" In this practical course, you will work on one of the remaining key challenges in autonomous driving: Robust behavior generation in the face of behavioral uncertainty. Given routing information, a static map and the motion history of all other agents, behavior planning deals with the problem of finding a continuous, collision-free and dynamically feasible time-dependent motion while considering traffic regulations, social conventions and time constraints. Scenarios with high interactions between many participants, such as merging in dense traffic, require the negotiation with other participants. To achieve robust behavior, the unknown intentions of other participants need to be reliably estimated and incorporated into the planning process. Handling such behavioral uncertainty is computationally demanding due to an exponentially increasing set of possible maneuvering options. Though several approaches have been proposed in the past, no method has demonstrated all necessary requirements for autonomous driving at SAE level 3 and above. In this practical course, we develop and implement, in teams, state-of-the-art behavior generation algorithms for autonomous vehicles. We select methods from different fields, such as Deep Reinforcement Learning, Search-Based Methods and Formal Methods. In a final contest, we will compare the different developed algorithms and draw conclusions. The implementation is based on our open source simulation platform ( providing visualization and data handling. Thus, students can fully concentrate on designing and improving their behavior generation module. Students should have significant experience with Python and C++. We are a group of Phd students working working on behavior planning. For that, we work both in simulation and on our own prototype vehicle ( The practical course will take place at fortiss (Research Institute of the Free State of Bavaria associated with Technical University of Munich). Please send us an email to stating your motivation. Please attach your CV and your transcript of records. Visit for more information.
Links E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

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