Automotive SPICE (A-SPICE) - Introduction Training Course

This instructor-led, live training in Nigeria (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 course explores the application of AI, with a particular focus on Machine Learning and Deep Learning, within the automotive industry. It guides participants in identifying technologies that can be (potentially) applied across various scenarios in a vehicle, ranging from basic automation and image recognition to autonomous decision-making processes.

This instructor-led, live training in Nigeria (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 development partnership comprising 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, is designed for intermediate to advanced automotive software developers seeking to design, develop, and integrate software using AUTOSAR Classic and Adaptive platforms, with a particular focus on ADAS (Advanced Driver Assistance Systems).

Upon completing this training, participants will be able to:

• Comprehend the architectures of AUTOSAR Classic and Adaptive platforms and identify 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 regarding safety, security, and performance optimization for automotive systems.

Format of the Course

• Interactive lectures and discussions.
• Practical, hands-on experience with industry-standard AUTOSAR tools.
• Project-based learning involving the 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 Nigeria (online or onsite) is primarily aimed at engineers who wish to use AUTOSAR to design automotive components.

Upon completing this training, participants will be able to:

• Install and configure AUTOSAR.
• Set up a workflow.
• Navigate the AUTOSAR environment with ease.
• Work efficiently.

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

By the end of this course, 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 OS and communication stack (COM stack).

Utilize 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 who want to grasp and configure the AUTOSAR OS (based on OSEK/VDX) and the COM Stack to facilitate reliable task scheduling and communication within automotive ECUs.

Upon completing this training, participants will be capable of:

• Grasping the AUTOSAR OS architecture and scheduling policies
• Implementing and managing tasks, events, alarms, and counters
• Describing and configuring the COM Stack layers, including PDUR and communication services
• Explaining protocol stacks (CAN, LIN, FlexRay, Ethernet) and how AUTOSAR interacts with them
• Configuring OS and COM modules using industry-standard tools (Vector DaVinci or ETAS ISOLAR)
• Simulating and validating task and communication flow in an AUTOSAR-based ECU

This instructor-led, live training in Nigeria (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 Nigeria (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 Nigeria (online or onsite) targets beginner-level professionals seeking to investigate the ethical dilemmas and legal frameworks associated with autonomous vehicles.

Upon completing this training, participants will be able to:

• Grasp the ethical implications of AI-driven decision-making in autonomous vehicles.
• Analyze global legal frameworks and policies governing self-driving cars.
• Examine liability and accountability issues arising from autonomous vehicle accidents.
• Evaluate the balance between innovation and public safety within autonomous driving regulations.
• Discuss real-world case studies involving ethical dilemmas and legal disputes.

This instructor-led, live training in Nigeria (online or onsite) is designed for intermediate-level professionals seeking a comprehensive grasp of EV powertrain architectures, battery chemistry, battery management systems (BMS), and the factors influencing energy efficiency in electric vehicles.

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

• Comprehend the structure and function of EV powertrains.
• Analyse various battery chemistries and their applications in EVs.
• Apply battery management techniques to improve performance and safety.
• Evaluate energy efficiency across different EV configurations.

This instructor-led, live training in Nigeria (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 Nigeria (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 Nigeria (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 Nigeria (online or onsite) is designed for intermediate-level network engineers and automotive IoT developers who aim to comprehend and deploy V2X communication technologies for autonomous vehicles.

Upon completing this training, participants will be capable of:

• Gaining a solid grasp of the core concepts behind V2X communication.
• Examining V2V, V2I, V2P, and V2N communication models.
• Deploying V2X protocols such as DSRC and C-V2X.
• Creating simulations for connected vehicle ecosystems.
• Managing cybersecurity and privacy concerns within V2X networks.