Course detail

Autonomous Driving 2

FEKT-MPA-AD2Acad. year: 2023/2024

In this more advanced course, students will learn about the history and current state of autonomous driving around the world and possible concepts for the future. This will be followed by a description of advanced in-vehicle assistant solutions and an analysis of the area of safety in key process automation. Sensors for autonomous driving and data processing will then be discussed with the aim of building a model of the surrounding environment and solutions for relative and global vehicle localization and relative and global mapping, including environmental dynamics problems. In the next section, methods for live obstacle recognition and classification, simulation of environmental dynamics, lane recognition, automatic braking, etc. will be explained and practiced.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

prof. Ing. Luděk Žalud, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Entry knowledge

Completion of the course Autonomous driving 1

Rules for evaluation and completion of the course

The subject includes laboratories, from which it is possible to obtain 0-30 points. In order to successfully complete the course, the student will take a final written exam, from which he/she will receive 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 advanced autonomous driving systems for current and future road vehicles, especially with a higher degree of autonomy. We will discuss approaches to solving selected problems in the field of autonomous driving, introduce the methods and tools used for data processing and their analysis in the process of automating vehicle control. We will explain the basic essence of the automation of the navigation process for vehicles on roads and the possible starting points for the behavior of vehicles in a variation of traffic situations.

A graduate of the subject will be able to prove: 

  1. Knowledge of advanced systems for self-localization of vehicles
  2. Knowledge of vehicle functional safety
  3. Knowledge of obstacle detection, including live ones
  4. Knowledge in the field of autonomous creation of local and global 3D maps
  5. Knowledge of advanced obstacle avoidance and braking systems 

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, summer semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

prof. Ing. Luděk Žalud, Ph.D.

Laboratory exercise

26 hours, compulsory

Teacher / Lecturer

prof. Ing. Luděk Žalud, Ph.D.