| School/Faculty/Institute |
Faculty of Engineering |
| Course Code |
EE 487 |
| Course Title in English |
Applied Artificial Intelligence in Automotive Systems |
| Course Title in Turkish |
Otomotiv Sistemlerinde Uygulamalı Yapay Zeka |
| Language of Instruction |
EN |
| Type of Course |
Flipped Classroom |
| Level of Course |
Select |
| Semester |
Fall |
| Contact Hours per Week |
| Lecture: 4 |
Recitation: 2 |
Lab: 2 |
Other: |
|
| Estimated Student Workload |
150 hours per semester |
| Number of Credits |
6 ECTS |
| Grading Mode |
Standard Letter Grade
|
| Pre-requisites |
MATH 224 - Probability and Statistics for Engineering
MATH 211 - Linear Algebra
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| Co-requisites |
None |
| Expected Prior Knowledge |
Prior knowledge of Probability & Statistics, Control Systems, Linear Algebra is expected |
| Registration Restrictions |
Only Undergraduate Students |
| Overall Educational Objective |
Upon successful completion of the course, the learner is expected to:
1. Apply AI and machine learning techniques in automotive systems.
2. Work with sensor data to build supervised, unsupervised, and deep learning models.
3. Integrate AI models into vehicle control systems to enhance autonomous decision-making.
4. Design AI solutions for driver assistance, battery management, and autonomous driving.
|
| Course Description |
This course teaches the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in automotive systems. Topics covered include vehicle dynamics, driver behavior analysis, battery management, autonomous driving, and safety systems. Data processing, model training, and visualization are performed using MATLAB and related Toolboxes. Models such as Random Forest, LSTM, CNN, and Reinforcement Learning (DQN, PPO) are utilized. The integration of AI models into real-time vehicle systems is examined. Participants will gain the ability to develop AI/ML solutions for intelligent driving systems. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Apply AI and machine learning techniques in automotive systems
2) Work with sensor data to build supervised, unsupervised, and deep learning models
3) Integrate AI models into vehicle control systems to enhance autonomous decision-making
4) Design AI solutions for driver assistance, battery management, and autonomous driving
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| Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
4 |
| 1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
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| 2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
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| 3) An ability to communicate effectively with a range of audiences |
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| 4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
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| 5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives |
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| 6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
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| 7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
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Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
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Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
N |
|
| 2) |
An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
H |
Exam
|
| 3) |
An ability to communicate effectively with a range of audiences |
N |
|
| 4) |
An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
N |
|
| 5) |
An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives |
N |
|
| 6) |
An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
H |
Exam
|
| 7) |
An ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
N |
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| Prepared by and Date |
BARIŞ AYKENT , August 2025 |
| Course Coordinator |
EGEMEN BİLGİN |
| Semester |
Fall |
| Name of Instructor |
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Course Contents
| Week |
Subject |
| 1) |
Introduction to AI/ML in Automotive Applications |
| 2) |
Vehicle Dynamics State Prediction |
| 3) |
Predictive Maintenance |
| 4) |
Driver Behavior Classification |
| 5) |
AI-Powered Driver Attention Monitoring |
| 6) |
Battery State of Charge (SoC) Estimation |
| 7) |
Energy Consumption Prediction for EVs |
| 8) |
Adaptive Cruise Control (ACC) using Reinforcement Learning |
| 9) |
Torque Vectoring Control with Neural Networks |
| 10) |
Vehicle Stability Control with AI |
| 11) |
Deep Reinforcement Learning for Autonomous Driving |
| 12) |
AI-Based Road Surface Condition Estimation |
| 13) |
AI-Driven Braking and Collision Avoidance |
| 14) |
Review |
| 15) |
Final Exam/Project/Presentation Period |
| 16) |
Final Exam/Project/Presentation Period |
Information Package / Course Catalogue