| Course Name | Principles of Autonomous Vehicle Design |
| Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
|---|---|---|---|---|---|
| EEE 527 | Fall/Spring | 3 | 0 | 3 | 7.5 |
| Prerequisites | None | |||||
| Course Language | English | |||||
| Course Type | Elective | |||||
| Course Level | Second Cycle | |||||
| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | ||||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | This course aims at introducing the concepts of how autonomous cars operate and teaching the state of the art technologies required for localization, sensor fusion, SLAM, avoiding obstructions, recognizing the road lane markings, traffic signs, traffic prediction, lane level routing, reliability and security. |
| Learning Outcomes | The students who succeeded in this course;
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| Course Description | Autonomous vehicle localization, driving, sensing, object recognition, tracking, sensor fusion, mapping, avoiding obstructions. Python based detection, recognition and classification techniques including computer vision, machine learning and convolutional neural networks (CNN). Communicating the microcomputer with the sensors and controlling the actuators using Robot operating System (ROS). |
| Related Sustainable Development Goals | |
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| Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
| Week | Subjects | Required Materials |
| 1 | Introduction to Autonomus Driving, Sensing, Perception, Object Recognition & Tracking, ROS, Deep Learning | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap1 |
| 2 | Localization & Mapping using GNSS, UWB, RFID & LIDAR | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap2 |
| 3 | Visual Odometry, wheel encoders, sensor fusion, reduction of odometry errors | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap2 |
| 4 | Perception in Autonomous Driving, Detection, Segmentation, Optical Flow | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap3 |
| 5 | Deep Learning in Autonomous Driving Perception, Convolutional Neural Networks | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap4 |
| 6 | Detection and Classification using Artificial Neural Networks and Machine Learning | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap4 |
| 7 | Python Based Detection and Classification using OpenCV, Tensor Flow, Keras, Scipy & Numpy | https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn |
| 8 | Python Based Detection and Classification using OpenCV, Tensor Flow, Keras, Scipy & Numpy | https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn |
| 9 | Prediction & Routing, Traffic Prediction, Lane Level Routing | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap5 |
| 10 | Robot Operating System, Communicating the microcomputer with the sensors and controlling the actuators | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap6 |
| 11 | Robot Operating System, Syatem Reliability, Resource Management & Security | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap8 |
| 12 | Project work on TurtleBot3 using LIDAR, UWB, RFID, Ultrasonic Sensor and Image Processing | TurtleBot3 Burger available in Mechatronics Lab |
| 13 | Project work on Turtlebot3 using LIDAR, UWB, RFID, Ultrasonic Sensor and Image Processing | TurtleBot3 Burger available in Mechatronics Lab |
| 14 | Project Presentations | |
| 15 | Review of the Course | |
| 16 | Final Exam |
| Course Notes/Textbooks | 1. Creating Autonomous Vehicle Systems, Shaoshan Liu, Liyun Li, Jie Tang, Shuang Wu, Jean-Luc Gaudiot, Morgan & Claypool Publishers, 2017 |
| Suggested Readings/Materials | 1. Markus Maurer · J. Christian Gerdes Barbara Lenz · Hermann Winner, Autonomous Driving, Springer open, 2016 |
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project | 1 | 45 |
| Seminar / Workshop | ||
| Oral Exam | ||
| Midterm | 1 | 25 |
| Final Exam | 1 | 30 |
| Total |
| Weighting of Semester Activities on the Final Grade | 2 | 70 |
| Weighting of End-of-Semester Activities on the Final Grade | 1 | 30 |
| Total |
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
| Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | 5 | |
| Study Hours Out of Class | 0 | ||
| Field Work | |||
| Quizzes / Studio Critiques | |||
| Portfolio | |||
| Homework / Assignments | |||
| Presentation / Jury | |||
| Project | 1 | 50 | |
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms | 1 | 22 | |
| Final Exams | 1 | 25 | |
| Total | 225 |
| # | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
| 1 | Accesses information in breadth and depth by conducting scientific research in Electrical and Electronics Engineering; evaluates, interprets and applies information | |||||
| 2 | Is well-informed about contemporary techniques and methods used in Electrical and Electronics Engineering and their limitations | |||||
| 3 | Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines | |||||
| 4 | Is informed about new and upcoming applications in the field and learns them whenever necessary. | |||||
| 5 | Defines and formulates problems related to Electrical and Electronics Engineering, develops methods to solve them and uses progressive methods in solutions. | |||||
| 6 | Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs. | |||||
| 7 | Designs and implements studies based on theory, experiments and modeling; analyses and resolves the complex problems that arise in this process. | |||||
| 8 | Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations; can work independently and takes responsibility. | |||||
| 9 | Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale. | |||||
| 10 | Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. | |||||
| 11 | Is knowledgeable about the social, environmental, health, security and law implications of Electrical and Electronics Engineering applications, knows their project management and business applications, and is aware of their limitations in Electrical and Electronics Engineering applications. | |||||
| 12 | Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. Adheres to the principles of research and publication ethics. | |||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
