Design Failure Mode and Effects Analysis (DFMEA) Training Course

This instructor-led live training in Uzbekistan (online or onsite) targets advanced-level robotics engineers and AI researchers who wish to implement sophisticated path planning algorithms to enhance autonomous vehicle performance.

By the end of this training, participants will be able to:

• Understand the theoretical foundations of advanced path planning algorithms.
• Implement algorithms such as RRT*, A*, and D* for real-time navigation.
• Optimize path planning for obstacle avoidance and dynamic environments.
• Integrate path planning algorithms with sensor data for enhanced accuracy.
• Evaluate the performance of various algorithms in practical scenarios.

This program explores the application of AI—particularly Machine Learning and Deep Learning—within the automotive industry. It enables participants to identify which technologies can be (potentially) applied across various automotive scenarios, ranging from basic automation and image recognition to autonomous decision-making processes.

This live, instructor-led training in Uzbekistan (online or onsite) is designed for advanced-level data scientists, AI specialists, and automotive AI developers who aim to build, train, and optimize AI models for autonomous driving applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of AI and deep learning in the context of autonomous vehicles.
• Implement computer vision techniques for real-time object detection and lane following.
• Utilize reinforcement learning for decision-making in self-driving systems.
• Integrate sensor fusion techniques for better perception and navigation.
• Build deep learning models to predict and analyze driving scenarios.

AUTOSAR (AUTomotive Open System ARchitecture) is a global partnership among automotive manufacturers, suppliers, and tool developers that establishes standardized software architectures for automotive electronic control units (ECUs).

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced automotive software developers aiming to design, develop, and integrate software using AUTOSAR Classic and Adaptive platforms, with a specific focus on ADAS (Advanced Driver Assistance Systems).

Upon completing this training, participants will be able to:

• Grasp the AUTOSAR Classic and Adaptive architectures along with their key distinctions.
• Create and configure automotive software components using AUTOSAR-compliant tools.
• Integrate and test ADAS software components within AUTOSAR Adaptive environments.
• Implement best practices for safety, security, and performance optimization in automotive systems.

Course Format

• Interactive lectures and discussions.
• Practical exercises using industry-standard AUTOSAR tools.
• Project-based learning with simulations of automotive use cases.

Customization Options

• For customized training requests, please contact us to arrange.

This instructor-led live training in Uzbekistan (online or on-site) is primarily aimed at engineers who wish to use AUTOSAR to design automotive components.

By the end of this training, participants will be able to:

• Install and configure AUTOSAR.
• Set up a workflow.
• Navigate smoothly in the AUTOSAR environment.
• Work efficiently.

This instructor-led, live training (available online or onsite) is designed for intermediate-level embedded software developers and automotive engineers aiming to leverage the AUTOSAR Classic Platform to develop, integrate, and test standardized software components for electronic control units (ECUs).

Upon completing this training, participants will be able to:

Install and configure AUTOSAR development tools (such as DaVinci Developer, EB Tresos, or ETAS ISOLAR-A/B).

Grasp the AUTOSAR layered architecture and its basic software (BSW) modules.

Design and implement the AUTOSAR Operating System (OS) and communication stack (COM stack).

Utilize CANoe or comparable tools for simulation, testing, and diagnostics within an AUTOSAR environment.

This instructor-led live training (available online or on-site) is designed for intermediate-level embedded software developers and automotive engineers who aim to grasp and configure AUTOSAR OS (based on OSEK/VDX) along with the COM Stack to ensure reliable task scheduling and communication within automotive ECUs.

Upon completing this training, participants will be equipped to:

• Comprehend the AUTOSAR OS architecture and its scheduling policies
• Implement and manage tasks, events, alarms, and counters
• Describe and configure COM Stack layers, including PDUR and communication services
• Explain protocol stacks (CAN, LIN, FlexRay, Ethernet) and how AUTOSAR interfaces with them
• Configure OS and COM modules using industry-standard tools (Vector DaVinci or ETAS ISOLAR)
• Simulate and validate task and communication flows in an AUTOSAR-based ECU

This instructor-led, live training in Uzbekistan (delivered either online or on-site) is designed for advanced-level safety engineers and automotive safety professionals seeking to develop comprehensive safety strategies for autonomous vehicles. The course covers hazard analysis, functional safety assessments, and adherence to international standards.

Upon completion of this training, participants will be able to:

• Identify and evaluate safety risks linked to autonomous driving systems.
• Conduct hazard analysis and risk assessment in line with industry standards.
• Implement safety validation and verification methods for AV systems.
• Apply functional safety standards, including ISO 26262 and SOTIF.
• Develop risk mitigation strategies to address AV safety challenges.

This guided, live training in Uzbekistan (online or in-person) targets intermediate-level AI developers and computer vision engineers who aim to build robust vision systems for autonomous driving applications.

Upon completion of this training, participants will be capable of:

• Grasping the core principles of computer vision within autonomous vehicles.
• Developing algorithms for object detection, lane recognition, and semantic segmentation.
• Combining vision systems with other autonomous vehicle components.
• Utilizing deep learning methods for sophisticated perception tasks.
• Assessing the performance of computer vision models in practical situations.

This instructor-led, live training in Uzbekistan (online or onsite) is designed for beginner-level professionals who wish to explore the ethical dilemmas and legal frameworks surrounding autonomous vehicles.

By the end of this training, participants will be able to:

• Understand the ethical implications of AI-driven decision-making in autonomous vehicles.
• Analyze global legal frameworks and policies regulating self-driving cars.
• Examine liability and accountability in the event of autonomous vehicle accidents.
• Evaluate the balance between innovation and public safety in autonomous driving laws.
• Discuss real-world case studies involving ethical dilemmas and legal disputes.

This instructor-led, live training in Uzbekistan (online or on-site) is aimed at intermediate-level professionals who wish to gain a comprehensive understanding of EV powertrain architectures, battery chemistry, battery management systems (BMS), and the factors affecting energy efficiency in electric vehicles.

By the end of this training, participants will be able to:

• Understand the structure and function of EV powertrains.
• Analyse different battery chemistries and their applications in EVs.
• Implement battery management techniques to enhance performance and safety.
• Evaluate energy efficiency in various EV configurations.

This instructor-led, live training in Uzbekistan (online or onsite) is designed for beginners and enthusiasts who want to grasp the core concepts, technologies, and applications of autonomous vehicles.

By the end of this training, participants will be able to:

• Understand the essential components and operational principles of autonomous vehicles.
• Explore the significance of AI, sensors, and real-time data processing in self-driving systems.
• Analyze various levels of vehicle autonomy and their practical applications.
• Examine the ethical, legal, and regulatory considerations surrounding autonomous mobility.
• Gain practical experience through autonomous vehicle simulations.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at advanced-level sensor fusion specialists and AI engineers who wish to develop multi-sensor fusion algorithms and optimise real-time navigation in autonomous systems.

By the end of this training, participants will be able to:

• Understand the fundamentals and challenges of multi-sensor data fusion.
• Implement sensor fusion algorithms for real-time autonomous navigation.
• Integrate data from LiDAR, cameras, and RADAR for perception enhancement.
• Analyse and evaluate fusion system performance under various conditions.
• Develop practical solutions for sensor noise reduction and data alignment.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level engineers, automotive professionals, and IoT specialists who wish to understand the role of sensors in self-driving cars, covering LiDAR, radar, cameras, and sensor fusion techniques.

By the end of this training, participants will be able to:

• Understand the different types of sensors used in autonomous vehicles.
• Analyse sensor data for real-time vehicle perception and decision-making.
• Implement sensor fusion techniques to improve vehicle accuracy and safety.
• Optimise sensor placement and calibration for enhanced autonomous driving performance.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level network engineers and automotive IoT developers who wish to understand and implement V2X communication technologies for autonomous vehicles.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of V2X communication.
• Analyze V2V, V2I, V2P, and V2N communication models.
• Implement V2X protocols such as DSRC and C-V2X.
• Develop simulations for connected vehicle environments.
• Address cybersecurity and privacy challenges in V2X networks.