0000005307 20W 6SWS PR Masterpraktikum - Planning Robust Behavior for Autonomous Driving (IN2106, IN4251)   Hilfe Logo

LV - Detailansicht

Wichtigste Meldungen anzeigenMeldungsfenster schließen
Allgemeine Angaben
Masterpraktikum - Planning Robust Behavior for Autonomous Driving (IN2106, IN4251) 
practical training
Winter semester 2020/21
Informatics 6 - Chair of Robotics, Artificial Intelligence and Real-time Systems (Prof. Knoll)
(Contact information)
Allocations: 1 
Angaben zur Abhaltung
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
  • English
  • German
Für die Anmeldung zur Teilnahme müssen Sie sich in TUMonline als Studierende*r identifizieren.
Online information
e-learning course (moodle)