ISO 26262 Automotive Functional Safety Training Course

This instructor-led live training in Uzbekistan, offered online or on-site, is tailored for advanced robotics engineers and AI researchers looking to implement sophisticated path planning algorithms to boost autonomous vehicle capabilities.

By the conclusion of this training, participants will be capable of:

• Comprehending the theoretical basis of advanced path planning algorithms.
• Implementing RRT*, A*, and D* algorithms for real-time navigation.
• Optimizing path planning for obstacle avoidance in dynamic environments.
• Merging path planning algorithms with sensor data for greater accuracy.
• Evaluating the performance of different algorithms in real-world scenarios.

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

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

• Comprehend the basics of AI and deep learning within the scope of autonomous vehicles.
• Deploy computer vision techniques for real-time object detection and lane following.
• Apply reinforcement learning to drive decision-making in self-driving systems.
• Integrate sensor fusion methods to enhance perception and navigation.
• Develop deep learning models for the prediction and analysis of driving scenarios.

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 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.

Objectives

• Gaining knowledge of ISO 27001:2023
• Gaining knowledge on how to audit in accordance with the standard
• Getting to know good practices

Objectives

• Acquire comprehensive knowledge of ISO 27001:2023.
• Understand how to conduct audits in compliance with the standard.
• Familiarize yourself with industry best practices.

Objectives

• Understanding the updates introduced in the ISO 27001:2023 edition
• Learning how to conduct audits in compliance with the standard
• Exploring industry best practices

The ISO/IEC 27001 Transition training course enables participants to thoroughly understand the differences between ISO/IEC 27001:2013 and ISO/IEC 27001:2022. In addition, participants will acquire knowledge on the new concepts presented by ISO/IEC 27001:2022.

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 designed for IT professionals at the intermediate to expert level who wish to upgrade their skills and qualifications in information security or related fields.

Upon completing this training, participants will be able to:

• Identify the differences between ISO/IEC 27001:2013 and ISO/IEC 27001:2022.
• Acquire the knowledge and skills necessary to plan and execute the transition from the 2013 to the 2022 version of the standard efficiently.
• Apply this knowledge in practical scenarios, enabling a seamless transition within their respective organizations.