Automotive Mechatronics and Intelligent Vehicles (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) Sufficient knowledge in mathematics, science and engineering related to their branches; and the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
2) The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
3) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
4) Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
5) Ability to design experiments, conduct experiments, collect data, analyze and interpret results to examine engineering problems or discipline-specific research topics.
6) The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
7) Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9) Conform to ethical principles, and standards of professional and ethical responsibility; be informed about the standards used in engineering applications.
10) Awareness of applications in business, such as project management, risk management and change management; awareness of entrepreneurship, and innovation; information about sustainable development.
11) Information about the universal and social health, environmental and safety effects of engineering applications and the ways in which contemporary problems are reflected in the engineering field; awareness of the legal consequences of engineering solutions.
12) Knowledge on advanced calculus, including differential equations applicable to automotive engineering; familiarity with statistics and linear algebra; knowledge on chemistry, calculus-based physics, dynamics, structural mechanics, structure and properties of materials, fluid dynamics, heat transfer, manufacturing processes, electronics and control, design of vehicle elements, vehicle dynamics, vehicle power train systems, automotive related regulations and vehicle validation/verification tests; ability to integrate and apply this knowledge to solve multidisciplinary automotive problems; ability to apply theoretical, experimental and simulation methods and, computer aided design techniques in the field of automotive engineering; ability to work in the field of vehicle design and manufacturing.

Course - Learning Outcome Relationship

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

	Program Outcomes	Level of Contribution
1)	Sufficient knowledge in mathematics, science and engineering related to their branches; and the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.	2
2)	The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.	4
3)	The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)	3
4)	Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.	3
5)	Ability to design experiments, conduct experiments, collect data, analyze and interpret results to examine engineering problems or discipline-specific research topics.	2
6)	The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.	4
7)	Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.	2
8)	Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.	1
9)	Conform to ethical principles, and standards of professional and ethical responsibility; be informed about the standards used in engineering applications.	4
10)	Awareness of applications in business, such as project management, risk management and change management; awareness of entrepreneurship, and innovation; information about sustainable development.	3
11)	Information about the universal and social health, environmental and safety effects of engineering applications and the ways in which contemporary problems are reflected in the engineering field; awareness of the legal consequences of engineering solutions.	2
12)	Knowledge on advanced calculus, including differential equations applicable to automotive engineering; familiarity with statistics and linear algebra; knowledge on chemistry, calculus-based physics, dynamics, structural mechanics, structure and properties of materials, fluid dynamics, heat transfer, manufacturing processes, electronics and control, design of vehicle elements, vehicle dynamics, vehicle power train systems, automotive related regulations and vehicle validation/verification tests; ability to integrate and apply this knowledge to solve multidisciplinary automotive problems; ability to apply theoretical, experimental and simulation methods and, computer aided design techniques in the field of automotive engineering; ability to work in the field of vehicle design and manufacturing.	3

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