Power Electronics and Clean Energy Systems (English) with thesis
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

General course introduction information

Course Code:

AUTO526

Course Name:

Autonomous Vehicles

Course Semester:

Fall

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	10

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Department Elective

Course Level:

Master

TR-NQF-HE:7. Master`s Degree

QF-EHEA:Second Cycle

EQF-LLL:7. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Prof. Dr. RAMAZAN NEJAT TUNCAY

Course Lecturer(s):

Dr. İSMAİL BAYEZİT

Course Assistants:

Course Objective and Content

Course Objectives:

Self-driving vehicles are poised to become one of the most pervasive and impactful applications of autonomy, and have received a great deal of attention recently.

This course considers problems in perception, navigation, planning and control, and their systems-level integration in the context of self-driving vehicles through an open-source curriculum for autonomy education that emphasizes hands-on experience. Integral to the course, students will collaborate to implement concepts covered in lecture on a low-cost autonomous vehicle with the goal of navigating a model town complete with roads, signage, traffic lights, obstacles, and citizens.

Course Content:

The course will cover the theory and application of probabilistic techniques for autonomous mobile robotics with particular emphasis on their application in the context of self-driving vehicles. Topics include probabilistic state estimation and decision making for mobile robots; stochastic representations of the environment; dynamics models and sensor models for mobile robots; algorithms for mapping and localization; planning and control in the presence of uncertainty; cooperative operation of multiple mobile robots; mobile sensor networks; deep learning for perception; imitation from expert trajectories; reinforcement learning.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
1) Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study
Field Specific Competence
1) Akıllı şehirlerde mobilitenin zorluklarını belirlenmesi
2) Model the technological components of a cyberphysical system
3) Use the technology of autonomous vehicles
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	Autonomy architectures	Course Notes
2)	Sensors, models, and representations	Course Notes
3)	Computer vision	Course Notes
4)	Nonlinear filtering and state estimation (Bayes filter, Kalman filter, particle filter, SLAM)	Course Notes
5)	Nonlinear filtering and state estimation (Bayes filter, Kalman filter, particle filter, SLAM)	Course Notes
6)	Navigation and planning (mission planning, motion planning and control basics)	Course Notes
7)	Navigation and planning (mission planning, motion planning and control basics)	Course Notes
8)	Complex perception pipelines (use of object detection, reading traffic signs, and tracking)	Course Notes
9)	Complex perception pipelines (use of object detection, reading traffic signs, and tracking)	Course Notes
10)	Tools for making robots work (Docker, ROS, Git, network basics)	Course Notes
11)	Reinforcement learning and sim2real transfer	Course Notes
12)	Deep learning for perception	Course Notes
13)	Deep learning for perception	Course Notes
14)	Deep learning for perception	Course Notes

Sources

Course Notes / Textbooks:	Ders Notları
References:	Course Notes

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	4	3
Program Outcomes
1) Reaches the information in the field of power electronics and clean energy systems in depth through scientific researches; evaluates the knowledge, interprets and implements.
2) Has the extensive information about current techniques and their constraints in the field of Power Electronics .
3) Using limited or missing data, completes the information through scientific methods and applies; integrates the information from different disciplines.
4) Aware of new and emerging applications of his/her profession; learn and examine them if needed.
5) Builds the Power Electronics problems, develops methods to solve and implements innovative ways for solution.
6) Develops new and/or original ideas and methods; develops innovative solutions for the design of a process, system or component.
7) Designs and implements the analytical, modeling and experimental-based researches; resolves the complex situations encountered in this process and interprets.
8) Leads multi-disciplinary teams, develops solution approaches to complex situations and takes responsibility.
9) Uses at least one foreign language at the general level of European Language Portfolio B2 and communicates effectively in oral and written language.
10) Presents the process and results of the work in national and international media systematically and clearly in written or oral language.
11) Describe the social and environmental dimensions of Power Electronics Engineering applications.
12) In the stages of data collection, interpretation and publication as well as all professional activities, he/she considers the social, scientific and ethical values.

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Reaches the information in the field of power electronics and clean energy systems in depth through scientific researches; evaluates the knowledge, interprets and implements.
2)	Has the extensive information about current techniques and their constraints in the field of Power Electronics .
3)	Using limited or missing data, completes the information through scientific methods and applies; integrates the information from different disciplines.	4
4)	Aware of new and emerging applications of his/her profession; learn and examine them if needed.
5)	Builds the Power Electronics problems, develops methods to solve and implements innovative ways for solution.	3
6)	Develops new and/or original ideas and methods; develops innovative solutions for the design of a process, system or component.
7)	Designs and implements the analytical, modeling and experimental-based researches; resolves the complex situations encountered in this process and interprets.	1
8)	Leads multi-disciplinary teams, develops solution approaches to complex situations and takes responsibility.	1
9)	Uses at least one foreign language at the general level of European Language Portfolio B2 and communicates effectively in oral and written language.
10)	Presents the process and results of the work in national and international media systematically and clearly in written or oral language.	2
11)	Describe the social and environmental dimensions of Power Electronics Engineering applications.
12)	In the stages of data collection, interpretation and publication as well as all professional activities, he/she considers the social, scientific and ethical values.

Learning Activity and Teaching Methods

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Homework
Application
Individual Project
Presentation
Reporting

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 30
Midterms	1	% 30
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Project	1	175	175
Midterms	1	24	24
Final	1	48	48
Total Workload			289