Mastery of advanced machine and deep learning architectures specialized for automotive applications.In-depth understanding of computer vision methods for semantic segmentation and vehicle perception.Training for the development of intelligent predictive maintenance and vehicle diagnostics systems.Integration of reinforcement learning paradigms into high-level vehicle decision-making frameworks.Critical analysis of computational efficiency for running AI algorithms on automotive edge hardware.

Design novel neural network topologies for real-time multi-modal vehicle sensor data processing. Develop physics-informed machine learning models combining vehicle dynamics and AI.Independently create predictive algorithms for behavior and trajectory forecasting of road users.Optimize complex AI models for deployment on resource-constrained embedded automotive ECUs.Generate innovative scientific solutions for intelligent vehicle energy and powertrain management.Critically evaluate the safety, robustness, and explainability (XAI) of automotive AI models.

Lectures are based on consultation with students in accordance with the previously issued research tasks.

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There is no pre condition.

Aleksendrić D., Carlone P. Soft computing in the Design and Manufacturing of Composite Materials, Elsevier, 2015. Aleksendrić D. Intelligent systems engineering (book in preparation), Faculty of Mechanical Engineering University of Belgrade, 2026.(In Serbian). Aleksendrić D. Miljković Z. Artifical neural networks- collection of solved tasks with theory, Faculty of Mechanical Engineering University of Belgrade, 2009.(In Serbian). Savaresi S., Taneli M. Active Braking Control Systems Design for Vehicles, Springer 2010. Li L., Wang F.Y. Advanced Motion Control and Sensing for Intelligent Vehicles, Springer, 2007. Bishop R. Intelligent Vehicles Technology and Trends, Artech House INC, 2005. Vachtsevanos G., Lewis F.L.,Roemer M., Hess A., Wu B. Intelligent Fault Diagnosis and Prognosis for Engineering Systems,John Wiley&Sons INC,2006. Mas F.R., Zhang Q., Hansen A. Mechatronics and Intelligent Systems for Off-Road Vehicles, Springer, 2010.

Total assigned hours: 65

New material: 40
Elaboration and examples (recapitulation): 10

Auditory exercises: 0
Laboratory exercises: 0
Calculation tasks: 0
Seminar paper: 0
Project: 0
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 0
Review and grading of seminar papers: 5
Review and grading of the project: 0
Test: 0
Test: 0
Final exam: 10

Activity during lectures: 0
Test/test: 0
Laboratory practice: 0
Calculation tasks: 0
Seminar paper: 70
Project: 0
Final exam: 30
Requirement for taking the exam (required number of points): 0

Aleksendrić D., Carlone P. Soft computing in the Design and Manufacturing of Composite Materials, Elsevier, 2015. 978-1-78242-179-5