Available courses

Intelligent Transportation Systems and Smart Mobility

This course provides the necessary background information about intelligent transportation systems (ITS), smart mobility and their relation. ITS training materials cover modules on architectures, sensor/communication tech, applications, policies, and ethics, focusing on connected/autonomous vehicles.

AIV Hardware Technology: Components and Industrial Computers

This course introduces hardware components and industrial computer systems used in autonomous intelligent vehicles (AIV). It systematically covers nine modules, including sensors, actuators, computing units (GPU, CPU), communication, data storage and power-energy systems, all essential for AIVs' autonomous and intelligent functions.

Autonomous and Intelligent Vehicles Sensor Technology

This course introduces sensor technologies used in autonomous vehicles and explains their functions, usage, collaboration and integration within systems. Detailed course covering modules on cameras, LiDAR, radar, ultrasonic, GPS/GNSS, ESC, AI/Computer Vision and Sensor Fusion.

AIV Integration: Software/Hardware Integration Methods

This course covers software and hardware interoperability in an autonomous vehicle systems, introducing embedded systems perspectives, operating systems, and infrastructure frameworks. Across seven modules, it explores methodologies for software and hardware validation, addressing integration challenges in intelligent vehicle development.

Connected and Autonomous Vehicle Technologies

Driver perception limits and reaction delays remain major traffic safety challenges in intelligent transportation systems. In order to increase perception limits and simultaneously decrease reaction delays, inter- vehicle communication (IVC) or road-to-vehicle communication (RVC) have been proposed. This course provides an introduction to the wireless technologies and standards used for connected and autonomous vehicles exploring V2V/V2I wireless standards and technologies (802.11p and C-V2X), focusing on concepts like VANETs and applications such as Adaptive Cruise Control to improve traffic safety.

Connected Vehicle Technologies Application: Case Studies

This course provides an overview of a selected number of key examples using Connected Vehicle Technologies and covers use cases across five modules, including on demand transport systems, speed adjustment at intersections (traffic lights and stop signs), obstacle and pedestrian warnings, mixed vehicle traffic and merging scenarios utilizing V2V and V2I communication.

Introduction to Applied Deep Learning in Autonomous Vehicles & Computer Vision

The course covers essentials of Artificial Intelligence, Machine Learning and Deep Learning with a specific focus on Computer Vision and image-based applications for autonomous vehicles (AVs) across eight modules. Key application-focused topics include Image Classification, Object Detection, Image Segmentation and Modeling Car Behavior in AVs ensuring up-to date application ways in different scenarios for autonomous vehicles, through the Computer Vision perspective.

AIV Software Technology and Architectures

This course provides an overview of Autonomous Intelligent Vehicle (AIV) software technology and architecture. It covers the evolution of automotive architectures, the need for new software-defined vehicle (SDV) approaches, and the role of DevOps in modern automotive software development.

Autonomous Vehicle (AV) Programming

This course provides the necessary background to begin programming tasks for autonomous driving functions. To this end, the curriculum covers autonomous vehicle hardware, real-time system classifications (Hard, Soft, and Firm), and modular software architectures for self-driving vehicles. Practical applications are emphasized through Robot Operating System 2 (ROS2) in C++ and Python, featuring hands-on examples in image data processing, CAN communication, and object detection using YOLOv8.

Sensor Fusion Methods and Software Implementation

This course introduces the essential methods and technical implementations of sensor fusion for autonomous vehicles, linking theoretical foundations with practical application. Course modules cover vehicle sensors and perception, Bayesian inference, Kalman filters, gating methods, data association, and multi-target tracking, featuring hands-on software implementation, real-world challenges, and simulation examples.

Path Planning Techniques

This course covers path planning methods for autonomous vehicles, from basic task classification to practical simulation. Key topics include route and motion planning, road representations, path optimization, parametric curve techniques including Bézier curves and polynomials, with practical applications demonstrated using MATLAB and Simulink.

Mapping and Environment Recognition

Course covers an introduction to different map types, map layers and map generation, along with the fundamental concepts of vehicle localization and navigation. The modules include specific localization methods using GNSS data with RTK, LiDAR point clouds with Normal Distribution Transform (NDT), and Simultaneous Localization and Mapping (SLAM).

Introduction to Vehicle Modeling

This course introduces core vehicle dynamics modeling for autonomous driving functions. Topics include vehicle kinematics, Ackermann steering, longitudinal and lateral dynamics, tire models, and engine modeling, with an emphasis on deployment in MATLAB/Simulink and simulation environments like IPG TruckMaker and dSpace ASM.

Introduction to Autonomous Wheeled Robot Control

This course introduces high-level control (HLC) architecture for autonomous wheeled robots, focusing on event-driven decision-making and finite state machines (FSM). Building upon low-level control fundamentals, it covers operational logic modeling for key autonomous behaviors including adaptive cruise control, convoy control, lane following, and intersection management, with practical insights drawn from the DARPA Urban Challenge.

Introduction to AIV Control - Low Level Control

This course introduces low-level control systems for autonomous vehicles, combining vehicle dynamics modeling with practical execution. It explores the distinction between high and low-level control, point-mass models, lateral control maneuvers, obstacle avoidance, and waypoint following techniques.

MIL/SIL/HIL Simulation Environment

This course covers simulation frameworks for validating and testing autonomous driving functions across the system development V-model. It details the development workflows and practical applications of Model-in-the-Loop (MIL), Software-in the-Loop (SIL), and Hardware-in-the-Loop (HIL) simulation, supported by illustrative examples including Driver-in-the-Loop (DIL) and Vehicle-in-the-Loop (VIL) methodologies.

Simulation and Testing Phase of Autonomous Vehicle

This course introduces testing environments, certification systematics, and accreditation frameworks for autonomous vehicles. It explores virtual and real-world test scenario design, compliance with ISO 34502 and PAS 1883 standards, and validation of core ADAS/AD functions including ACC, AEB, ALKS, and LKAS using CARLA simulator integration.

Innovation and Creativity Methodologies

This course covers systematic innovation methodologies and creative problem-solving techniques for product and system design. Key topics include the Function Follows Form principle alongside core inventive techniques such as Subtraction, Division, Multiplication, Task Unification, and Attribute Dependency to systematically break cognitive barriers and generate innovative solutions.

Innovative Idea Development Methodologies

This course provides a structured framework for generating, refining, and evaluating innovative ideas. It dispels common creativity myths while introducing the RIGHT Thinking model and neuroscience-based approaches to idea evaluation, alongside practical strategies for refining and pitching concepts within organizations.

Institutional Entrepreneurship

This course covers intrapreneurship strategies and best practices for driving innovation within established organizations. Key topics include innovation framework analysis, Minimum Viable Product (MVP) development, resource acquisition strategies, and real-world implementations, featuring case studies such as Adobe Kickbox and Cisco Innovate Everywhere.

Mentoring a Technological Entrepreneur

This course explores the role of mentorship in tech startup ventures. It covers strategies for identifying milestones, selecting suitable mentors, building effective mentor mentee relationships, and navigating mentorship programs and incubation ecosystems in Türkiye.

Technology Analysis and Technology Forecasting

This course covers the foundations and practical applications of technology forecasting and strategic decision-making. Key topics include quantitative and qualitative forecasting methodologies, major technological trend drivers, technology roadmapping, innovation management, and ethical considerations surrounding emerging technologies.

Managing Technology-Based Companies

This course explores operational and strategic leadership frameworks in the tech industry. It focuses on ten core domains, such as innovation management, Agile development and DevOps, talent retention, market analysis, data security, ecosystem partnerships, infrastructure scaling, ethical innovation, continuous learning, and customer-centric business design.

Team Building: Empowering Teams for Success

This course covers core principles of team building and leadership to optimize team dynamics and performance. Key topics include communication strategies, conflict resolution, performance enhancement, and methods for fostering a collaborative team environment.

Business Plan Preparation

This course focuses on developing actionable business plans for technology and growth-oriented ventures. It explores core elements such as market opportunity analysis, financial modeling, marketing strategy, and resource allocation to build sustainable business models.

Lean Design and Development

This course explores practical Lean methodologies applied to design and development workflows. It focuses on customer-centric problem solving, rapid prototyping, iterative testing, and continuous improvement strategies leveraging Agile principles.

Patent Search and Mapping Techniques

This course covers key strategies for patent search, mapping, and intellectual property analysis. Key topics include trademark rights, utility model applications, brand design concepts, and technical frameworks for protecting intellectual property within competitive markets.

Startup Financing and Exit Strategies

This course covers key financial frameworks for launching, funding, and scaling technology startups. Key topics include financing stages from Seed to Series C, equity and debt funding mechanisms, pitch deck development, startup financial management, investor relations, and exit strategy planning through IPOs and acquisitions.

Entrepreneurial Marketing and Sales

This course covers modern marketing and sales strategies tailored for entrepreneurial ventures and startups. Key topics include market research, customer segmentation, brand positioning, startup-focused sales funnels, digital marketing channels, and KPI tracking for evaluating marketing performance.

Innovative Product Market Analysis

This course covers core methodologies for analyzing competitive markets for innovative products. Key topics include competitive landscape analysis, customer insights, unique value proposition design, market sizing, forecasting techniques, regulatory considerations, and end-to-end go-to-market execution.