ISTANBUL OKAN UNIVERSITY INFORMATION PACKAGE / COURSE CATALOGUE
| PhD in Mechatronic Engineering (English) with a bachelor's degree | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
General course introduction information
| Course Code: | AUTO526 | ||||||||
| Course Name: | Autonomous Vehicles | ||||||||
| Course Semester: | Fall | ||||||||
| Course Credits: |
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| Language of instruction: | EN | ||||||||
| Course Requisites: | |||||||||
| Does the Course Require Work Experience?: | No | ||||||||
| Type of course: | Department Elective | ||||||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||||||
| Course Coordinator : | Prof. Dr. RAMAZAN NEJAT TUNCAY | ||||||||
| Course Lecturer(s): |
Dr. İSMAİL BAYEZİT |
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| 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;
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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 |
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|---|---|---|---|---|---|---|
| Program Outcomes | ||||||
| 1) Knowledge and ability to apply the interdisciplinary synergetic approach of mechatronics to the solution of engineering problems | ||||||
| 2) Ability to design mechatronic products and systems using the mechatronics approach | ||||||
| 3) Knowledge and ability to analyze and develop existing products or processes with a mechatronics approach | ||||||
| 4) Ability to communicate effectively and teamwork with other disciplines | ||||||
| 5) Understanding of performing engineering in accordance with ethical principles | ||||||
| 6) Understanding of using technology with awareness of local and global socioeconomic impacts | ||||||
| 7) Approach to knowing and fulfilling the necessity of lifelong learning | ||||||
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Knowledge and ability to apply the interdisciplinary synergetic approach of mechatronics to the solution of engineering problems | 1 |
| 2) | Ability to design mechatronic products and systems using the mechatronics approach | |
| 3) | Knowledge and ability to analyze and develop existing products or processes with a mechatronics approach | |
| 4) | Ability to communicate effectively and teamwork with other disciplines | 1 |
| 5) | Understanding of performing engineering in accordance with ethical principles | |
| 6) | Understanding of using technology with awareness of local and global socioeconomic impacts | |
| 7) | Approach to knowing and fulfilling the necessity of lifelong learning |
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 | ||