Artificial Intelligence (AI) in Automotive Training Course

This instructor-led, live training in Australia (delivered online or on-site) is designed for advanced robotics engineers and AI researchers seeking to deploy sophisticated path planning algorithms to improve autonomous vehicle performance.

Upon completion, participants will be equipped to:

• Grasp the theoretical underpinnings of advanced path planning algorithms.
• Deploy algorithms such as RRT*, A*, and D* for real-time navigation.
• Refine path planning for effective obstacle avoidance and dynamic environments.
• Combine path planning algorithms with sensor data to boost accuracy.
• Assess algorithm performance across various practical scenarios.

This instructor-led, live training in Australia (online or on-site) is designed for senior data scientists, AI specialists, and automotive AI developers who aim to build, train, and optimise AI models for autonomous driving applications.

Upon completion 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.
• Utilise reinforcement learning for decision-making in self-driving systems.
• Integrate sensor fusion techniques for improved perception and navigation.
• Build deep learning models to predict and analyse driving scenarios.

This instructor-led, live training in Australia (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.

This instructor-led live training in Australia (online or onsite) is primarily designed for engineers who wish to utilise 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 within 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 wish to use the 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 (available online or onsite) is designed for intermediate-level embedded software developers or automotive engineers seeking to master the configuration of AUTOSAR OS (based on OSEK/VDX) and the COM Stack to facilitate reliable task scheduling and communication within automotive ECUs.

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

• Comprehend 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-standard tools (Vector DaVinci or ETAS ISOLAR)
• Simulate and validate task and communication flow in an AUTOSAR-based ECU

This instructor-led, live training in Australia (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 Australia (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 Australia (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 Australia (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 Australia (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.
• Analyse 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 Australia (online or onsite) is aimed at advanced-level sensor fusion specialists and AI engineers who wish to develop multi-sensor fusion algorithms and optimise 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.
• Analyse and evaluate fusion system performance under various conditions.
• Develop practical solutions for sensor noise reduction and data alignment.

This instructor-led live training in Australia (online or onsite) is designed for intermediate-level engineers, automotive professionals, and IoT specialists keen to grasp the critical role sensors play in self-driving cars. The curriculum encompasses LiDAR, radar, camera systems, and sensor fusion methodologies.

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

• Identify and understand the various types of sensors utilised in autonomous vehicles.
• Analyse sensor data to support real-time vehicle perception and informed decision-making.
• Implement sensor fusion strategies to enhance vehicle precision and safety.
• Optimise sensor positioning and calibration to boost autonomous driving performance.

This instructor-led, live training in Australia (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.
• Analyse 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.