GRADUATE SCHOOL
M.SC. in Electrical and Electronics Engineering (Without Thesis)
EEE 527 | Course Introduction and Application Information
| 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;
|
| Course Description | In this course, localization, object recognition, tracking, sensor fusion, mapping, avoiding obstructions in autonomous vehicles will be explained and Python based perception, motion planning and navigation techniques using Robot operating System (ROS) environment will be taught. |
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Introduction to Autonomus Driving, Sensing, Perception, Object Recognition & Tracking, ROS | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap1 |
| 2 | Sensing and Perceiving the Environment using wheel encoders, GPS, IMU, Ultrasonic Sensor & LIDAR | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap2 |
| 3 | Introduction to Robot Operating System (ROS) | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap1 |
| 4 | Introduction to Robot Operating System (ROS) Running ROS on Riders Cloud Platform | https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn |
| 5 | Creating And Configuring ROS Messages, publishers, subscribers and topics | https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn |
| 6 | ROS services, client – server applications | https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn |
| 7 | Kalman and Extended Kalman Filters, sensor fusion | https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn |
| 8 | Map based Navigation using ROS: Navigating a Autonomous Vehicle using Gazebo and RVIZ simulators in ROS | https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn |
| 9 | Tuning Navigation Stack Parameters, Pose of Vehicle And Transformation in 2D and 3D Reference Frames | https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn |
| 10 | Prediction & Routing, Traffic Prediction, Lane Level Routing | Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap5 |
| 11 | Project work on TurtleBot3 using LIDAR, IMU, and Ultrasonic Sensor and Image Processing | TurtleBot3 Burger available in Mechatronics Lab |
| 12 | Project work on TurtleBot3 using LIDAR, IMU, and Ultrasonic Sensor and Image Processing | TurtleBot3 Burger available in Mechatronics Lab |
| 13 | Project work on TurtleBot3 using LIDAR, IMU, and 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 |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project |
1
|
45
|
| Seminar / Workshop | ||
| Oral Exams | ||
| 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 |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
5
|
80
|
| Study Hours Out of Class |
0
|
||
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
0
|
||
| Presentation / Jury |
0
|
||
| Project |
1
|
50
|
50
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
22
|
22
|
| Final Exam |
1
|
25
|
25
|
| Total |
225
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
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. |
X | ||||
| 2 | Is well-informed about contemporary techniques and methods used in Electrical and Electronics Engineering and their limitations. |
X | ||||
| 3 | Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines. |
X | ||||
| 4 | Is informed about new and upcoming applications in the field and learns them whenever necessary. |
X | ||||
| 5 | Defines and formulates problems related to Electrical and Electronics Engineering, develops methods to solve them and uses progressive methods in solutions. |
X | ||||
| 6 | Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs. |
X | ||||
| 7 | Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process. |
X | ||||
| 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. |
X | ||||
| 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. |
X | ||||
| 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. |
|||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest