Automotive Vision / Fahrzeugsehen

  • type: Lecture
  • semester: Summer Term
  • place:

    online

  • time:

    Monday, 10:00 - 12:00 weekly

  • start: 19.04.2021
  • lecturer:

    Dr. rer. nat Martin Lauer

     

  • sws: 3

Exam Results

--> Exam Results 07.09.2021

Exam

The next exam Automotive Vision will be held at 7 September 2021, 08:00-09:00. The registration is open until 15 August 2021. Please register online via the campus management system, if possible. If an online registration is impossible, get an exam registration sheet from the student service center. Submit a scan of the exam registration sheet by e-mail to infoDul7∂mrt kit edu . Find further information on ILIAS

Overview

Machine perception and interpretation of the environment for the basis for the generation of intelligent behaviour. Especially visual perception opens the door to novel automotive applications. First driver assistance systems can already improve safety, comfort and efficiency in vehicles. Yet, several decades of research will be required to achieve an automated behaviour with a performance equivalent to a human operator. The lecture addresses students in mechanical engineering and related subjects who intend to get an interdisciplinary knowledge in a state-of-the-art technical domain. Machine vision, vehicle kinematics and advanced information processing techniques are presented to provide a broad overview on seeing vehicles. Application examples from cutting-edge and future driver assistance systems illustrate the discussed subjects.

Content

  1. Driver assistance systems
  2. Binocular vision
  3. Feature point methods
  4. Optical flow/tracking in images
  5. Tracking and state estimation
  6. Self-localization and mapping
  7. Lane recognition
  8. Behavior recognition

Ideally, you have previously attended “Measurement and Control Systems/Grundlagen der Mess- und Regelungstechnik” or basic knowledge of measurement and control systems and system theory from a lecture from other faculties. Previous knowledge from the "Machine Vision" lecture is helpful, but not essential.