Module Handbook

  • Dynamischer Default-Fachbereich geändert auf MV

Notes on the module handbook of the department Mechanical and Process Engineering

Die hier dargestellten veröffentlichten Studiengang-, Modul- und Kursdaten des Fachbereichs Maschinenbau und Verfahrenstechnik ersetzen die Modulbeschreibungen im KIS und wuden mit Ausnahme folgender Studiengänge am 28.10.2020, bzw. am 13.01.2021 verabschiedet.


Module MV-MEC-M230-M-7

Introduction to Autonomous Systems (M, 4.0 LP)

Module Identification

Module Number Module Name CP (Effort)
MV-MEC-M230-M-7 Introduction to Autonomous Systems 4.0 CP (120 h)


CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in WiSe
Level [7] Master (Advanced)
Language [EN] English
Module Manager
Area of study [MV-MEC] Mechatronics in Mechanical and Automotive Engineering
Reference course of study [MV-88.808-SG] M.Sc. Computational Engineering
Livecycle-State [NORM] Active


Type/SWS Course Number Title Choice in
Presence-Time /
SL SL is
required for exa.
PL CP Sem.
2V+1U MV-MEC-86693-K-7
Introduction to Autonomous Systems
P 42 h 78 h - - PL1 4.0 WiSe
  • About [MV-MEC-86693-K-7]: Title: "Introduction to Autonomous Systems"; Presence-Time: 42 h; Self-Study: 78 h

Examination achievement PL1

  • Form of examination: written exam (Klausur) (60-90 Min.)
  • Examination Frequency: each semester
  • Examination number: 10231 ("Introduction to Autonomous Systems")

Evaluation of grades

The grade of the module examination is also the module grade.


1. Motivation for autonomous systems technology with automated driving as use case: Overview of ADAS and Automated Driving (including different SAE automation levels), Architectures for ADAS/Automated Driving, Sensor technologies, Perception, Sensor Fusion, Localisation, Planning and Motion control

2. Perception and Sensor Fusion Primer: Environment Perception - Range-based sensors (radar, lidar, ultrasonic), Image based sensors, Object detection and classification (conventional, machine learning based), Different levels of fusion – advantages, disadvantages and design selection (low level, feature level, object level), Evidence fusion

3. State Estimation Techniques: Fundamentals of probability and statistics (probability distributions, properties), Bayesian statistics, Bayes Filtering, Kalman Filter (Linear, Extended Kalman Filter, Unscented Kalman Filter), Sequential Monte Carlo (Particle Filter), Programming demo

4. Object Tracking I: Motivation and types, Tracking Architecture, Motion Models, Measurement Models, Data Association, Track Management, Programming demo

5. Object Tracking II: Multiple Model Tracking, Object Tracking Metrics, Programming demo

6. Applied Reinforcement Learning

Competencies / intended learning achievements

1. Lecture:

Students are able to

  • understand the major components and modules involved in autonomous systems
  • describe fundamental concepts of different sensor technologies
  • explain the overall concepts of object detection and classification
  • understand different sensor fusion techniques and state estimation methods
  • understand the technical steps involved in multi-object tracking in detail

2. Programming demos:

Students are able to

  • get a sense of real sensor data from radars, lidars and camera
  • get an idea of how to implement a basic object detection pipeline
  • acquainted with implementation details of object tracking in autonomous systems


  • Probabilistic Robotics, MIT Press 2005. Authors: S. Thrun, W. Burgard, and D. Fox
  • Introduction to Autonomous Mobile Robots, MIT Press 2004. Authors: R. Siegwart and I.R. Nourbakhsh
  • Tracking and Data Fusion – A handbook of algorithms, YBS Publishing 2011. Authors: Yaakov Bar-Shalom, Peter K. Wilett, Xin Tian

Requirements for attendance of the module (informal)

  • Linear Algebra
  • Probability and Statistics
  • Fundamentals of robotics

Requirements for attendance of the module (formal)


References to Module / Module Number [MV-MEC-M230-M-7]

Module-Pool Name
[GS-CVT-ME-2022-E-MPOOL-6] Catalog Electives Mechanical Engineering 2022
[GS-CVT-ME-E-MPOOL-6] Catalog Electives Mechanical Engineering
[MV-ALLG-2022-MPOOL-6] Wahlpflichtmodule Master allgemein 2022
[MV-ALL-MPOOL-6] Wahlpflichtmodule allgemein
[MV-CE-2022-MPOOL-6] Wahlpflichtmodule M.Sc. Computational Engineering 2022
[MV-CE-MPOOL-6] Wahlpflichtmodule Computational Engineering
[MV-FT-2022-MPOOL-6] Wahlpflichtmodule M.Sc. Fahrzeugtechnik 2022
[MV-FT-MPOOL-6] Wahlpflichtmodule Fahrzeugtechnik
[MV-MB-INF-2022-MPOOL-6] Wahlpflichtmodule M.Sc. Maschinenbau mit angewandter Informatik 2022
[MV-MBINFO-MPOOL-6] Wahlpflichtmodule Maschinenbau mit angewandter Informatik