Artificial Intelligence (AI) in Automotive Training Course

This instructor-led, live training in Sweden (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 Sweden (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.

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

• Grasp the fundamentals of AI and deep learning as applied to autonomous vehicles.
• Apply computer vision methods for real-time object detection and lane keeping.
• Employ reinforcement learning for decision-making within self-driving systems.
• Incorporate sensor fusion methods to enhance perception and navigation.
• Construct deep learning models to anticipate and analyse driving scenarios.

This instructor-led, live training in Sweden (online or onsite) 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.

AUTOSAR (AUTomotive Open System ARchitecture) represents a global partnership among automotive manufacturers, suppliers, and tool developers dedicated to standardizing the software architecture for automotive electronic control units (ECUs).

This instructor-led live training, available online or on-site, targets intermediate to advanced automotive software developers looking to design, develop, and integrate software using both AUTOSAR Classic and Adaptive platforms, with a specific emphasis on ADAS (Advanced Driver Assistance Systems).

Upon completing this training, participants will be equipped to:

• Comprehend the architectures of AUTOSAR Classic and Adaptive platforms, along with their primary distinctions.
• Develop and configure automotive software components utilizing AUTOSAR-compliant tools.
• Integrate and test ADAS software components within AUTOSAR Adaptive environments.
• Implement best practices concerning safety, security, and performance optimization for automotive systems.

Course Format

• Interactive lectures and discussions.
• Practical exercises using industry-standard AUTOSAR tools.
• Project-based learning coupled with simulations of automotive use cases.

Customization Options

• For those interested in a tailored training session for this course, please reach out to us to make arrangements.

This instructor-led live training in Sweden (online or onsite) is primarily aimed at engineers who wish to use Autosar to design automotive components.

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

• Install and configure Autosar.
• Set up a workflow.
• Navigate smoothly in the Autosar environment.
• Work efficiently.

This instructor-led, live training (available online or on-site) is designed for intermediate-level embedded software developers and automotive engineers looking to leverage the AUTOSAR Classic Platform to develop, integrate, and test standardized software components for electronic control units (ECUs).

Upon completing this training, participants will be capable of:

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

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

Designing and implementing AUTOSAR OS and the communication stack (COM stack).

Utilizing CANoe or comparable tools for simulation, testing, and diagnostics within an AUTOSAR environment.

This instructor-led, live training (online or onsite) is aimed at intermediate-level embedded software developers or automotive engineers who wish to understand and configure AUTOSAR OS (based on OSEK/VDX) and the COM Stack to enable reliable task scheduling and communication in automotive ECUs.

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

• Grasp the AUTOSAR OS architecture and scheduling policies
• Implement and manage tasks, events, alarms, and counters
• Describe and configure the COM Stack layers, including PDUR and communication services
• Explain protocol stacks (CAN, LIN, FlexRay, Ethernet) and how AUTOSAR interfaces with them
• Configure OS and COM modules using industry tools (Vector DaVinci or ETAS ISOLAR)
• Simulate and validate task and communication flow in an AUTOSAR-based ECU

This instructor-led, live training in Sweden (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 Sweden (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 Sweden (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 Sweden (online or onsite) 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.
• Analyse 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 Sweden (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 Sweden (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:

• Grasp the core principles and challenges of multi-sensor data fusion.
• Apply sensor fusion algorithms to achieve real-time autonomous navigation.
• Combine data from LiDAR, cameras, and RADAR to improve perception capabilities.
• Assess and evaluate fusion system performance under diverse conditions.
• Create practical solutions for reducing sensor noise and aligning data.

This instructor-led, live training in Sweden (online or onsite) is designed for intermediate-level engineers, automotive professionals, and IoT specialists aiming to grasp the critical role of sensors in self-driving cars, with a focus on LiDAR, radar, cameras, and sensor fusion techniques.

Upon completing this training, participants will be able to:

• Identify and understand the various types of sensors employed in autonomous vehicles.
• Analyze sensor data to support real-time vehicle perception and decision-making processes.
• Apply sensor fusion techniques to enhance vehicle accuracy and safety.
• Optimize sensor placement and calibration to improve overall autonomous driving performance.

This instructor-led, live training in Sweden (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.