700.385 (24S) Lab of Autonomous Driving Cars

Sommersemester 2024

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Erster Termin der LV
05.03.2024 16:00 - 17:30 Online Off Campus
Nächster Termin:
07.05.2024 16:00 - 17:30 Online Off Campus

Überblick

Lehrende/r
LV-Titel englisch Lab of Autonomous Driving Cars
LV-Art Kurs (prüfungsimmanente LV )
LV-Modell Onlinelehrveranstaltung
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 3.0
Anmeldungen 14 (12 max.)
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 05.03.2024
eLearning zum Moodle-Kurs
Seniorstudium Liberale Ja

Zeit und Ort

Liste der Termine wird geladen...

LV-Beschreibung

Intendierte Lernergebnisse

Upon completing this course, students will achieve the following comprehensive learning outcomes:

  • Foundation of Autonomous Driving: Provide students with a solid understanding of the fundamentals of Autonomous Driving, including its historical context and the core components involved.

  • Technological Background: Explore the underlying technology and principles that drive Autonomous Driving Cars, enabling students to grasp the innovations and advancements in this field.

  • System Components: Familiarize students with the various essential components of Autonomous Driving Cars, giving them insights into the integral parts that make these vehicles autonomous.

  • Autonomy Methods Overview: Provide an overview of diverse methodologies used to achieve autonomy in Autonomous Driving Cars, covering sensor fusion, localization, perception, and decision-making algorithms.

  • Practical Machine Vision and ML: Equip students with practical skills in implementing Machine Vision and Machine Learning algorithms tailored for Autonomous Driving, ensuring hands-on experience.

  • Simulation and Real-Time Testing: Enable students to simulate and rigorously test algorithms using game engines, generating real-time data to assess and refine autonomous systems effectively.

Lehrmethodik inkl. Einsatz von eLearning-Tools

  • Interactive Lectures and presentationon the topic with examples.
  • Practical Implementation.
  • In-class activities and discussions
  • Assignments
  • Online Resources on Moodle
  • Collaborative Projects
  • CARLA Simulator

Inhalt/e

1. Theoretical Understanding of Autonomous Vehicles:

  • Gain familiarity with the various levels of Self-Driving Cars.
  • Acquire in-depth knowledge of the underlying technology behind Autonomous Driving Cars.
  • Develop insights into the diverse applications of Autonomous Driving Cars across various industries.
  • Master control, path planning, and tracking techniques relevant to Autonomous Driving.
  • Attain expertise in Image Processing as applied to Self-Driving Cars.

2. Proficiency in Deep Learning and Machine Vision for Autonomous Driving Cars:

  • Learn Semantic Segmentation techniques for analyzing scenes effectively.
  • Understand Lane Detection algorithms to ensure safe driving.
  • Gain proficiency in Object Detection methods for identifying obstacles and other vehicles.
  • Master steering control strategies for precise vehicle maneuvering.

3. Practical Application using the CARLA Simulator:

  • Apply the acquired knowledge practically by utilizing the CARLA simulator.
  • Implement machine learning techniques and computer vision to simulate and assess Autonomous Driving Cars in a virtual environment.
  • Gain hands-on experience that enhances your ability to develop and evaluate autonomous systems effectively.

                                  

Erwartete Vorkenntnisse

Prior Knowledge Expected:

  • Knowledge of Python programming.
  • Background in Deep Learning and Machine Vision.
  • Ability to work with COLAB or Jupyter notebook for implementation.

Recommended Courses:

  • Fundamentals of Image Processing
  • Machine Learning in Intelligent Transportation
  • Practical Introduction to Neural Networks and Deep Learning
  • Artificial Vision
  • Tutorium in Machine Learning, TensorFlow, PyTorch Basics

Curriculare Anmeldevoraussetzungen

none

Literatur

Will be uploaded on Moodle

Prüfungsinformationen

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Prüfungsmethode/n

  • Assignments: 

    • Homeworks
    • In-class activities
    • Homework presentations

  • Group Presentations: 

    • Presentation on selected topics of Autonomous Driving Cars (Week 7 or Week 8)

  • Final Project

Prüfungsinhalt/e

  • Understanding Autonomous Cars
  • Utilizing simple tasks in Computer Vision and Machine learning for Autonomous Driving Cars. 
  • Using the CARLA Simulator with implemented topics thoughout the semester

Beurteilungskriterien/-maßstäbe

  1. Understanding of the Topic:

    • Proper comprehension of course material.

  2. Homework and Final Project Implementation:

    • Effective completion of implementation tasks.

  3. Active Learning Throughout the Semester:

    • Active participation and engagement in class activities.

Beurteilungsschema

Note Benotungsschema

Position im Curriculum

  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Technical Complements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.385 Lab of Autonomous Driving Cars (2.0h KS / 3.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Fach: Technical Complements (NC, ASR) (Wahlfach)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.385 Lab of Autonomous Driving Cars (2.0h KS / 3.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 22W.1)
    • Fach: Information and Communicatons Enginnering: Supplements (Wahlfach)
      • 1.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 16) ( 0.0h VC, KS / 14.0 ECTS)
        • 700.385 Lab of Autonomous Driving Cars (2.0h KS / 3.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 22W.1)
    • Fach: ICE- Supplements (Wahlfach)
      • 2.3b Ausgewählte Lehrveranstaltungen (siehe Curriculum Seite 18) ( 0.0h VC, KS / 14.0 ECTS)
        • 700.385 Lab of Autonomous Driving Cars (2.0h KS / 3.0 ECTS)
  • Bachelorstudium Robotics and Artificial Intelligence (SKZ: 295, Version: 22W.1)
    • Fach: Robotics & AI Applications (Wahlfach)
      • 8.1 Robotics & AI Applications ( 0.0h VO, VC, UE, KS / 12.0 ECTS)
        • 700.385 Lab of Autonomous Driving Cars (2.0h KS / 3.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2023
  • 700.385 KS LAB on Autonomous Driving Cars (2.0h / 3.0ECTS)
Wintersemester 2021/22
  • 700.385 KS LAB on Autonomous Driving Cars (2.0h / 3.0ECTS)
Wintersemester 2020/21
  • 700.385 KS LAB on Autonomous Driving Cars (2.0h / 3.0ECTS)