Course detail

Autonomous Driving 1

FEKT-MPA-AD1Acad. year: 2024/2025

In the first part of the course, students will learn about the history and current state of autonomous driving in the world. This will be followed by a detailed description of in-vehicle assistant solutions and an analysis of the area of safety in key process automation. Then, sensors for autonomous driving and data processing will be discussed with the aim of building a model of the surrounding environment and a solution for relative and global vehicle localization. In the next part, methods for obstacle recognition, simulation of environmental dynamics, lane recognition, automatic braking and more will be explained and practiced.

Language of instruction

English

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

prof. Ing. Luděk Žalud, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Entry knowledge

Fundamentals of signal processing and electronics. 

Rules for evaluation and completion of the course

A part of the course are laboratoriy excersises, in which it is possible to obtain 0 - 30 points. To successfully finish the course, the student have to pass a final written exam in which he/she will obtain a clasification of 0 - 70 points.
The definition of controlled teaching and the method of its implementation is specified by a regulation issued by the lecturer responsible for the course annually. Laboratory exercises are mandatory.

Aims

The aim of the course is to acquaint students with the concept, principles and general architecture of the autonomous driving system for current and future road vehicles. Discuss approaches to solving selected problems in the field of autonomous driving, get acquainted with the methods and tools used for data processing and analysis in the process of automation of vehicle control. Explain the principle of automatization of the navigation process for vehicles on roads and possible solutions of behaviors of vehicles in various traffic situations.

A graduate of the subject will be able to prove: 

  1. Knowledge of SAE classification
  2. Knowledge of driving assistants at SAE level 1/2
  3. Knowledge of basic types of sensors for locating and detecting obstacles
  4. Knowledge of basic self-localization algorithms
  5. Knowledge of basic principles of vehicle simulation 

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

SJAFRIE, H. Introduction to Self-Driving Vehicle Technology, Boca Raton: Chapman and Hall/CRC, 2020, ISBN: 9780367321253 (EN)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme MPA-AEE Master's 2 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

prof. Ing. Luděk Žalud, Ph.D.

Laboratory exercise

26 hod., compulsory

Teacher / Lecturer

prof. Ing. Luděk Žalud, Ph.D.