Automotive Training in Central

This instructor-led, live training in Central (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 Central (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 Central (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 Central (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 Central (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 Central (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 Central covers designing and developing automotive software with AUTOSAR Classic and Adaptive platforms. Participants will gain hands-on skills to configure components, integrate ADAS software, and apply safety best practices using industry-standard tools.

This instructor-led, live training in Central (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 Central (online or onsite) is aimed at intermediate-level automotive engineers and technicians who wish to gain hands-on experience in testing, simulating, and diagnosing ECUs using Vector tools like CANoe and CANape.

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

• Understand the role and function of ECUs in automotive systems.
• Set up and configure Vector tools such as CANoe and CANape.
• Simulate and test ECU communication on CAN and LIN networks.
• Analyze data and perform diagnostics on ECUs.
• Create test cases and automate testing workflows.
• Calibrate and optimize ECUs using practical approaches.

This instructor-led, live training in Central (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 Central (online or onsite) is aimed at intermediate-level automotive engineers and embedded systems developers who wish to understand the theoretical aspects of ECUs, focusing on Vector-based tools and methodologies used in automotive design and development.

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

• Understand the architecture and functions of ECUs in modern vehicles.
• Analyze communication protocols used in ECU development.
• Explore Vector-based tools and their theoretical applications.
• Apply model-based development principles to ECU design.

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

This instructor-led, live training in Central (online or onsite) is aimed at beginner-level quality engineers who wish to learn how to use Quality Core Tools to ensure product quality in the manufacturing industry.

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

• Understand the importance and integration of the Quality Core Tools.
• Master the concepts and application of APQP processes to facilitate effective product quality planning.
• Identify potential failures in products and processes, understand their impact on product quality, and implement actions to mitigate risks.
• Use statistical methods to monitor and control manufacturing processes, ensuring product quality and process efficiency.

This instructor-led, live training in Central (online or onsite) is aimed at beginner-level quality control professionals who wish to learn the fundamentals of Statistical Process Control (SPC) and apply it in real-world scenarios.

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

• Understand the fundamentals of Statistical Process Control (SPC).
• Use basic SPC tools such as control charts, histograms, Pareto charts, and scatter diagrams to monitor process performance.
• Create and interpret various types of control charts for variable and attribute data to detect and analyze process variations.
• Calculate and interpret process capability indices.

The "Automotive SPICE (A-SPICE) - Introduction" workshop focuses on reviewing the Automotive SPICE process assessment model, discussing its structure, classification, and interpretative guidelines. Participants will gain skills to understand the evaluation process from the perspective of project managers and stakeholders. The course covers the significance of ASPICE in the market, compliance, different process groups, and ASPICE levels through real-world implementation examples.

This instructor-led, live training in Central (online or onsite) is aimed at engineers who wish to safeguard connected vehicles from cyber attacks.

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

• Implement cybersecurity in automotive systems.
• Choose the most suitable technologies, tools, and approaches.

This instructor-led, live training in Central (online or onsite) is aimed at mostly engneers 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 (online or onsite) is aimed at intermediate-level embedded software developers and automotive engineers who wish to use AUTOSAR Classic Platform to develop, integrate, and test standardized software components for electronic control units (ECUs).

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

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

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

Design and implement AUTOSAR OS and communication stack (COM stack).

Use CANoe or similar tools for simulation, testing, and diagnostics in 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:

• Understand 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