Automotive Cyber Security Fundamentals Training Course

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

AUTOSAR (AUTomotive Open System ARchitecture) is a global collaboration among automotive manufacturers, suppliers, and tool developers aimed at 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 who aim to design, develop, and integrate software utilizing AUTOSAR Classic and Adaptive platforms, with a specific emphasis on ADAS (Advanced Driver Assistance Systems).

Upon completing this training, participants will be capable of:

• Grasping the architectures of AUTOSAR Classic and Adaptive, along with their principal distinctions.
• Creating and configuring automotive software components employing AUTOSAR-compliant tools.
• Integrating and testing ADAS software components within AUTOSAR Adaptive environments.
• Implementing best practices concerning safety, security, and performance optimization for automotive systems.

Course Format

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

Course Customization Options

• For inquiries regarding customized training for this course, please reach out to us to arrange details.

This instructor-led live training in Taiwan (online or onsite) is primarily aimed at engineers who wish to utilize AUTOSAR for designing 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 onsite) targets intermediate-level embedded software developers and automotive engineers seeking to utilize the AUTOSAR Classic Platform for developing, integrating, and testing standardized software components for electronic control units (ECUs).

Upon completing this training, participants will be equipped to:

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

Grasp the AUTOSAR layered architecture and its basic software modules (BSW).

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

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

This instructor-led, live training (available online or onsite) is designed for intermediate-level embedded software developers and automotive engineers seeking to master the configuration of AUTOSAR OS (rooted in OSEK/VDX) and the COM Stack to ensure robust task scheduling and communication within automotive ECUs.

Upon completing this training, participants will be capable of:

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

This instructor-led, live training in Taiwan (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 Taiwan (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 Taiwan introduces the system architectures, operating systems, networking, storage, and cryptographic issues that should be considered when designing secure embedded systems.

By the end of this course, participants will have a solid understanding of security principles, concerns, and technologies. More importantly, participants will be equipped with the techniques needed for developing safe and secure embedded software.

This instructor-led, live training in Taiwan (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 Taiwan (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.
• 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 Taiwan (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 Taiwan (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 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 Taiwan (online or onsite) targets intermediate-level engineers, automotive professionals, and IoT specialists seeking to comprehend the role of sensors in self-driving cars, including LiDAR, radar, cameras, and sensor fusion techniques.

Upon completing this training, participants will be able to:

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

This instructor-led, live training in Taiwan (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.