Problem Solving Methodology (8D Method / 5 Whys / Ishikawa) Training Course

This instructor-led, live training in Mexico (available online or onsite) is designed for 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 course examines the application of AI—specifically Machine Learning and Deep Learning—within the automotive sector. It guides learners in identifying which technologies can be effectively applied across various vehicle scenarios, ranging from basic automation and image recognition to autonomous decision-making.

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

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

• Grasp the core principles of AI and deep learning within the realm of autonomous vehicles.
• Apply computer vision methods for real-time object detection and lane keeping.
• Leverage reinforcement learning to enhance decision-making processes in self-driving systems.
• Incorporate sensor fusion strategies to improve perception and navigation accuracy.
• Develop deep learning models to forecast and analyze various driving scenarios.

AUTOSAR (AUTomotive Open System ARchitecture) is a global development partnership among automotive manufacturers, suppliers, and tool developers that standardizes the software architecture for automotive electronic control units (ECUs).

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

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

• Understand the architectures of AUTOSAR Classic and Adaptive and their key differences.
• Develop and configure automotive software components using AUTOSAR-compliant tools.
• Integrate and test ADAS software components within AUTOSAR Adaptive environments.
• Apply best practices for safety, security, and performance optimization in automotive systems.

Format of the Course

• Interactive lectures and discussions.
• Hands-on practice with industry-standard AUTOSAR tools.
• Project-based learning and simulation of automotive use cases.

Course Customization Options

• To request customized training for this course, please contact us to arrange.

This instructor-led, live training in Mexico (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 onsite) is designed for intermediate-level embedded software developers and automotive engineers who want 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 able to:

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

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

Design and implement AUTOSAR 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 (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 Mexico (online or in-person) is designed for safety engineers and automotive safety professionals at an advanced level who want to create comprehensive safety strategies for autonomous vehicles. The program covers hazard analysis, functional safety assessments, and adherence to international standards.

Upon completing this training, participants will be able to:

• Identify and evaluate safety risks linked to autonomous driving systems.
• Perform hazard analysis and risk assessment using industry standards.
• Implement validation and verification methods for AV systems.
• Apply functional safety standards, such as ISO 26262 and SOTIF.
• Create risk mitigation strategies to address safety challenges in autonomous vehicles.

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