Autonomous Vehicles (AVs) Training in Belgrade

This instructor-led, live training in Belgrade (online or onsite) is designed for beginners in the field who want to investigate the ethical dilemmas and legal structures surrounding autonomous vehicles.

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

• Grasp the ethical implications of AI-driven decision-making in autonomous vehicles.
• Analyze international legal frameworks and policies governing self-driving cars.
• Examine liability and accountability in cases of autonomous vehicle accidents.
• Assess 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 Belgrade (online or onsite) is aimed at beginner-level professionals and enthusiasts who wish to understand the fundamental concepts, technologies, and applications of autonomous vehicles.

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

• Understand the key components and working principles of autonomous vehicles.
• Explore the role of AI, sensors, and real-time data processing in self-driving systems.
• Analyze different levels of vehicle autonomy and their real-world applications.
• Examine the ethical, legal, and regulatory aspects of autonomous mobility.
• Gain hands-on exposure to autonomous vehicle simulations.

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

Upon completing this training, participants will be equipped to:

• Grasp the core concepts of V2X communication.
• Evaluate V2V, V2I, V2P, and V2N communication models.
• Implement V2X protocols, including DSRC and C-V2X.
• Create simulations for connected vehicle environments.
• Tackle cybersecurity and privacy issues within V2X networks.

This instructor-led, live training in Belgrade (online or onsite) is designed for intermediate-level engineers, automotive professionals, and IoT specialists who want 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.
• Analyze sensor data for real-time vehicle perception and decision-making.
• Implement sensor fusion techniques to improve vehicle accuracy and safety.
• Optimize sensor placement and calibration for enhanced autonomous driving performance.

This instructor-led, live training in Belgrade (online or onsite) is aimed at advanced-level safety engineers and automotive safety professionals who wish to develop comprehensive safety strategies for autonomous vehicles, including hazard analysis, functional safety assessments, and compliance with international standards.

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

• Identify and assess safety risks associated with autonomous driving systems.
• Conduct hazard analysis and risk assessment using industry standards.
• Implement safety validation and verification methods for AV systems.
• Apply functional safety standards, such as ISO 26262 and SOTIF.
• Develop risk mitigation strategies for AV safety challenges.

This instructor-led, live training in Belgrade (online or onsite) is aimed at advanced-level sensor fusion specialists and AI engineers who wish to develop multi-sensor fusion algorithms and optimize 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.
• Analyze and evaluate fusion system performance under various conditions.
• Develop practical solutions for sensor noise reduction and data alignment.

This instructor-led, live training in Belgrade (online or onsite) is aimed at intermediate-level AI developers and computer vision engineers who wish to build robust vision systems for autonomous driving applications.

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

• Understand the fundamental concepts of computer vision in autonomous vehicles.
• Implement algorithms for object detection, lane detection, and semantic segmentation.
• Integrate vision systems with other autonomous vehicle subsystems.
• Apply deep learning techniques for advanced perception tasks.
• Evaluate the performance of computer vision models in real-world scenarios.

Facilitated by an expert, this live training in Belgrade (online or in person) is designed for advanced robotics engineers and AI specialists who aim to deploy complex path planning algorithms to improve the functionality of autonomous vehicles.

Upon completion of this program, attendees will be capable of:

• Gaining a deep understanding of the theoretical basis behind advanced path planning algorithms.
• Deploying algorithms like RRT*, A*, and D* for real-time navigation tasks.
• Refining path planning processes to manage obstacle avoidance and dynamic environments effectively.
• Combining path planning algorithms with sensor inputs to boost precision.
• Assessing the performance of different algorithms within real-world contexts.

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

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

• Comprehend the fundamentals of AI and deep learning within 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.