Online or onsite, instructor-led live Autonomous Vehicles (AVs) training courses demonstrate through interactive hands-on practice how to use cutting-edge technologies and algorithms to develop, optimize, and implement autonomous driving systems.
AVs training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. 名古屋 onsite live Autonomous Vehicles (AVs) trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
This instructor-led, live training in 名古屋 (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 名古屋 (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 名古屋 (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.
This instructor-led, live training in 名古屋 (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.
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 名古屋 (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 名古屋 (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 名古屋 (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.
This instructor-led, live training in 名古屋 (online or onsite) is aimed at 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 instructor-led, live training in 名古屋 (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.
