Artificial Intelligence (AI) in Automotive Training Course

This live, instructor-led training in Norway (available online or onsite) is designed for advanced robotics engineers and AI researchers aiming to utilize complex path planning algorithms to improve autonomous vehicle performance.

Upon completion, participants will be able to:

• Comprehend the theoretical basis of advanced path planning algorithms.
• Apply algorithms such as RRT*, A*, and D* for real-time navigation tasks.
• Optimize planning processes for obstacle avoidance in dynamic settings.
• Merge path planning with sensor data to boost accuracy.
• Test the performance of various algorithms in practical situations.

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

Upon completion, participants will be equipped to:

• Grasp the core principles of AI and deep learning as they apply to autonomous vehicles.
• Apply computer vision methods to achieve real-time object detection and lane tracking.
• Leverage reinforcement learning to enhance decision-making capabilities in self-driving systems.
• Combine sensor fusion techniques to improve environmental perception and navigation accuracy.
• Develop deep learning models capable of forecasting and analyzing various driving scenarios.

This instructor-led, live training in Norway (online or on-site) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

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

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

This instructor-led, live training in Norway (online or onsite) is primarily designed for engineers who wish to utilize AUTOSAR to design automotive components.

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

• Install and configure AUTOSAR.
• Establish a workflow.
• Navigate the AUTOSAR environment with ease.
• Work efficiently.

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

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

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

Gain a comprehensive understanding of the AUTOSAR layered architecture and its basic software modules (BSW).

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

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

This instructor-led, live training (delivered online or onsite) is designed for intermediate-level embedded software developers and automotive engineers who aim to master the configuration of AUTOSAR OS (based on OSEK/VDX) and the COM Stack to ensure reliable task scheduling and communication within automotive ECUs.

Upon completing this training, participants will be capable of:

• Gaining a comprehensive understanding of the AUTOSAR OS architecture and scheduling policies
• Implementing and managing tasks, events, alarms, and counters
• Describing and configuring the COM Stack layers, such as PDUR and communication services
• Explaining protocol stacks (CAN, LIN, FlexRay, Ethernet) and their interface with AUTOSAR
• Configuring OS and COM modules using industry-standard tools (Vector DaVinci or ETAS ISOLAR)
• Simulating and validating task and communication flows in an AUTOSAR-based ECU

This instructor-led, live training in Norway (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 Norway (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 Norway (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 delivered in Norway (either 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.
• Analyze 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 Norway (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 Norway (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 Norway (online or onsite) is designed for intermediate-level engineers, automotive professionals, and IoT specialists who wish to gain insight into the role of sensors in self-driving cars. The curriculum covers LiDAR, radar, cameras, and sensor fusion techniques.

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

• Comprehend the various types of sensors utilized in autonomous vehicles.
• Analyze sensor data to support real-time vehicle perception and decision-making.
• Implement sensor fusion techniques to enhance vehicle accuracy and safety.
• Optimize sensor placement and calibration to improve autonomous driving performance.

This instructor-led, live training in Norway (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.