460-4133/02 – Autonomous Driving (AJ)

Gurantor departmentDepartment of Computer ScienceCredits5
Subject guarantorIng. Jan Gaura, Ph.D.Subject version guarantorIng. Jan Gaura, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Year2Semesterwinter
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
GAU01 Ing. Jan Gaura, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2

Subject aims expressed by acquired skills and competences

The subject familiarizes students with the techniques used in the autonomous driving of vehicles. By passing the course students will get an overview of modern methods of environment mapping, navigation and decision making in self-driving vehicles.

Teaching methods

Lectures
Tutorials
Project work

Summary

In particular, the following topics will be discussed: Methods and concepts of autonomous vehicle control using different sensors. This includes acquaintance with LIDAR sensors, camera systems, their use in vehicle environment analysis, interest detection and environmental prediction and space mapping. The surrounding area information is then used to plan further steps of an autonomous vehicle.

Compulsory literature:

LIU, Shaoshan, LI, Liyun, TANG, Jie, WU, Shuang, GAUDIOT, Jean-Luc. Creating Autonomous Vehicle Systems. Morgan & Claypool Publishers, 2018. ISBN: 1681730073 MCGRATH, Michael. Autonomous Vehicles: Opportunities, Strategies, and Disruptions. Independently published, 2018. ISBN-13: 978-1980313854

Recommended literature:

HERRMANN, Andreas, Walter BRENNER a Rupert STADLER. Autonomous driving: how the driverless revolution will change the world. Bingley, UK: Emerald Publishing, 2018. ISBN 978-1787148345. LIPSON, Hod a Melba KURMAN. Driverless: intelligent cars and the road ahead. Cambridge, Massachusetts: The MIT Press, 2016. ISBN 978-0262035224.

Way of continuous check of knowledge in the course of semester

Hand in tasks completed during exercises. Hand in of a project. Pass of final exam.

E-learning

Other requirements

No additional demands are required.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

Lectures: 1. Car navigation using GPS, RDS-TMC. 2. Introduction to autonomous driving, definition of basic concepts. 3. Sensors for autonomous driving, cameras, LIDAR, ultrasound. 4. Sensor data fusion. 5. Machine learning tools in autonomous driving. 6. Detecting objects around the vehicle. 7. Space mapping around the vehicle. 8. Methods of localization in known environment - particle filter. 9. Methods of localization in unknown environment - SLAM. 10. Motion models of other objects around vehicles. 11. Route planning, shortest path algorithms. 12. Search for possible route paths. 13. Predicting the route of other objects. 14. Behavioral planning, trajectory generation. Exercises: 1. Working with the GPS geolocation system and radio transmission of traffic information using RDS-TMC. 2. Working with cameras, setting parameters and storing data. 3. Work with LIDAR sensor and ultrasonic sensors. 4. Processing of sensor data and their fusion into subsequent analysis. 5. Introduction to the software framework of machine learning. 6. Detection of interest objects around a vehicle using machine learning. 7. Creating map data based on sensor data. 8. Use of particle filter to locate a vehicle in a known environment. 9. SLAM techniques to locate a vehicle in an unknown environment. 10. Motion models of other objects around a vehicle. 11. Implement algorithms for finding the shortest path. 12. Methods of searching space of possible routes and their visualization. 13. Methods of predicting the route of other objects and their visualization. 14. Behavioral planning, trajectory generation. Project: In the project, students will implement the selected problem of autonomous vehicle driving using available data.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 45  23
        Examination Examination 55  27
Mandatory attendence parzicipation: Every student has to obtain at least the minimum number of points for each task.

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2020/2021 (N0716A060002) Automotive Electronic Systems SPA P English Ostrava 2 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner