| Course Unit Code | 460-4133/01 |
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| Number of ECTS Credits Allocated | 5 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | Second Year |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| GAU01 | Ing. Jan Gaura, Ph.D. |
| Summary |
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| 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. |
| Learning Outcomes of the Course Unit |
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| 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. |
| Course Contents |
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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. |
| Recommended or Required Reading |
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| Required Reading: |
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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 |
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 Reading: |
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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. |
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. |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials, Project work |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 45 | 23 |
| Examination | Examination | 55 | 28 |