Masterpraktikum - Planning Robust Behavior for Autonomous Driving (IN2106, IN4251)
Course 0000002591 in SS 2019
General Data
Course Type | practical training |
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Semester Weekly Hours | 6 SWS |
Organisational Unit | Informatics 6 - Chair of Robotics, Artificial Intelligence and Real-time Systems (Prof. Knoll) |
Lecturers |
Alexander Lenz Responsible/Coordination: Alois Christian Knoll |
Dates |
3 singular or moved 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 | 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, Imitation-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. We invite all interested candidates to a preliminary presentation of the practical course to clarify remaining questions and present possible topics in detail. It will take place in room 03.07.011 on the Feb, 05 from 11:00 to 11:45. For more information, visit https://www6.in.tum.de/en/teaching/ws1920/seminar-robust-behavior-generation-for-autonomous-vehicles/ |
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Links |
E-Learning course (e. g. Moodle) TUMonline entry |
Equivalent Courses (e. g. in other semesters)
Semester | Title | Lecturers | Dates |
---|---|---|---|
WS 2020/1 | Masterpraktikum - Planning Robust Behavior for Autonomous Driving (IN2106, IN4251) |
Lenz, A.
Responsible/Coordination: Knoll, A. |
|
SS 2020 | Masterpraktikum - Planning Robust Behavior for Autonomous Driving (IN2106, IN4251) |
Kessler, T.
Lenz, A.
Responsible/Coordination: Knoll, A. |
|
WS 2019/20 | Masterpraktikum - Planning Robust Behavior for Autonomous Driving (IN2106, IN4251) |
Lenz, A.
Responsible/Coordination: Knoll, A. |