Autonomous Vehicles (AVs) Training in Prague

This instructor-led, live training in Prague (online or onsite) is aimed at 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 Prague (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 Prague (online or onsite) targets intermediate network engineers and automotive IoT developers aiming to understand and apply V2X communication technologies for autonomous vehicles.

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

• Grasp the core principles of V2X communication.
• Evaluate V2V, V2I, V2P, and V2N communication models.
• Deploy V2X protocols including DSRC and C-V2X.
• Creating simulations for connected vehicle ecosystems.
• Resolving cybersecurity and privacy issues within V2X networks.

This instructor-led, live training in Prague (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 guided, live training session in Prague (online or in person) targets advanced safety engineers and automotive safety experts aiming to create thorough safety strategies for autonomous vehicles. The curriculum covers hazard analysis, functional safety assessments, and adherence to international standards.

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

• Recognizing and evaluating safety risks linked to autonomous driving systems.
• Performing hazard analysis and risk assessment in accordance with industry standards.
• Applying safety validation and verification techniques for autonomous vehicle (AV) systems.
• Utilizing functional safety standards, including ISO 26262 and SOTIF.
• Creating risk mitigation strategies to address safety challenges in AVs.

This instructor-led live training, available in Prague (online or onsite), is designed for 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 Prague (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:

• Gain a solid understanding of 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.

This live, instructor-led training in Prague (online or onsite) is designed for advanced robotics engineers and AI researchers looking to implement refined path planning algorithms to improve autonomous vehicle performance.

By the end of the course, participants will be able to:

• Understand the core theoretical principles behind advanced path planning algorithms.
• Apply algorithms like RRT*, A*, and D* for real-time navigation tasks.
• Optimize path planning strategies for obstacle avoidance and dynamic environments.
• Combine path planning with sensor data to enhance accuracy.
• Evaluate algorithm performance in real-world scenarios.

This instructor-led, live training in Prague (online or onsite) is aimed at advanced-level data scientists, AI specialists, and automotive AI developers who wish 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.