LEARNING OUTCOMES
After the successful completions of the course, students will be able to:
- Learn the characteristics of autonomous vehicles and driving assistant systems
- Realize the benefits of applying artificial intelligence to transport
- Understand real-time management systems and telematics
- Solve simple applications in a traffic simulation program
General Competences
After the successful completions of the course, students acquire the following knowledge and skills:
- Search for analysis and synthesis of data and information, with the use of the necessary technology
- Adaptation to new situations
- Autonomous work
- Production of free, creative and inductive thinking
- Exercise criticism and self-criticism
- Work in an interdisciplinary environment
SYLLABUS
Theoretical part of the Course
- Introduction to the application of artificial intelligence in transport.
- Autonomous vehicles
- Driving assistant systems
- Telematics in transport
- Network optimization
- Real-time management systems
- Centralized and distributed controls and decision-making methods
- Applied statistical standardization
- Stated Preference Surveys
- Traffic simulation software
Lab Part of the Course
Preparation of a group theme (groups of 4 people) based on a questionnaire survey with the method of stated preference analysis.
STUDENT PERFORMANCE EVALUATION
Language of evaluation: Greek
Theoretical part of the Course
• Written exam (70%)
Lab Part of the Course
• Partial and Overall Presentation of a semester topic (30%)
ATTACHED BIBLIOGRAPHY
1. Autonomous Driving, Technical, Legal and Social Aspects, by Markus Maurer, Christian Gerdes, Barbara Lenz, Hermann Winner, SpringerLInk edition
2. Marsland, S. (2014). Machine learning: an algorithmic perspective. CRC press.
3. Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., & Held, P. (2013). Computational intelligence: a methodological introduction. Springer Science & Business Media.
4. Karlaftis, M. G. and Vlahogianni, E. I. (2011). Statistics versus Neural Networks in Transportation Research: Differences, Similarities and Some Insights, Transportation Research Part C: Emerging Technologies, 19(3), 387-399.
5. Engelbrecht, A. P. (2007). Computational intelligence: an introduction. John Wiley & Sons.
6. TRB (2007). Artificial Intelligence in Transportation: Information for Application, Transportation Research Circular E-C113, Transportation Research Board, Washington DC.
7. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.