Masterpraktikum - Planning Robust Behavior for Autonomous Driving (IN2106, IN4251)
Course 0000005307 in WS 2020/1
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 |
Alexander Lenz Responsible/Coordination: Alois Christian Knoll |
| 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 | Update 15.07.2020: Please send an email stating your motivation for the course to as.praktikum@fortiss.org. Please also include your CV and your transcript of records. -------------------------------------------------------------- IMPORTANT: Pre Course Meeting Practical Course BARK on Mi., 15. Juli 2020 13:00 - 14:00 (CEST) -------------------------------------------------------------- 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. For more information, visit https://bark-simulator.github.io/. We invite all interested candidates to a preliminary presentation of the practical course to clarify remaining questions and present possible topics in detail. Pre Course Meeting Practical Course BARK Mi., 15. Juli 2020 13:00 - 14:00 (CEST) Join the meeting via: https://global.gotomeeting.com/join/440332845 Or join via phone: +49 721 9881 4161 Code: 440-332-845 |
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| Links |
E-Learning course (e. g. Moodle) TUMonline entry |
Equivalent Courses (e. g. in other semesters)
| Semester | Title | Lecturers | Dates |
|---|---|---|---|
| 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. |
|
| SS 2019 | Masterpraktikum - Planning Robust Behavior for Autonomous Driving (IN2106, IN4251) |
Lenz, A.
Responsible/Coordination: Knoll, A. |
singular or moved dates |