Artificial Intelligence (AI) in Automotive Training Course

This instructor-led, live training in Botswana (available online or onsite) targets advanced-level robotics engineers and AI researchers who aim to deploy sophisticated path planning algorithms to elevate autonomous vehicle performance.

By the end of this training, participants will be capable of:

• Understanding the theoretical basis of advanced path planning algorithms.
• Implementing algorithms like RRT*, A*, and D* for real-time navigation.
• Optimizing path planning for obstacle avoidance and dynamic environments.
• Merging path planning algorithms with sensor data to enhance accuracy.
• Evaluating the performance of various algorithms in practical contexts.

This instructor-led, live training in Botswana (available online or on-site) is designed for advanced data scientists, AI specialists, and automotive AI developers aiming to build, train, and optimize AI models for autonomous driving applications.

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

• Comprehend the fundamentals of AI and deep learning as they apply to autonomous vehicles.
• Deploy computer vision techniques for real-time object detection and lane following.
• Apply reinforcement learning to drive decision-making in self-driving systems.
• Implement sensor fusion techniques to improve perception and navigation.
• Develop deep learning models for the prediction and analysis of driving scenarios.

This instructor-led, live training in Botswana (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 Botswana (online or onsite) is primarily designed for engineers who wish to utilise AUTOSAR for designing automotive components.

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

• Install and configure AUTOSAR.
• Establish a workflow.
• Navigate the AUTOSAR environment with ease.
• Work effectively and efficiently.

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

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

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

Comprehend 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 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.

Upon completion 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 Botswana (online or onsite) is designed for senior safety engineers and automotive safety experts seeking to establish robust safety strategies for autonomous vehicles. The curriculum covers hazard analysis, functional safety assessments, and adherence to international standards.

Upon completion of this training, participants will be capable of:

• Recognising and evaluating safety risks linked to autonomous driving systems.
• Carrying out hazard analysis and risk assessments in accordance with industry standards.
• Implementing validation and verification methods for AV systems.
• Applying functional safety standards, including ISO 26262 and SOTIF.
• Formulating risk mitigation strategies to address AV safety challenges.

This instructor-led, live training in Botswana (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 Botswana (online or onsite) is designed for professionals at the introductory level who wish to investigate the ethical dilemmas and legal structures governing self-driving cars.

Upon completing this training, participants will be equipped to:

• Grasp the ethical consequences of AI-driven choices in autonomous vehicles.
• Analyze international legal frameworks and policies that regulate self-driving vehicles.
• Scrutinise liability and accountability issues arising from autonomous vehicle incidents.
• Assess the equilibrium between technological innovation and public safety within autonomous driving legislation.
• Debate real-world case studies highlighting ethical dilemmas and legal conflicts.

This instructor-led, live training in Botswana (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 Botswana (online or onsite) targets beginner-level professionals and enthusiasts keen on grasping the fundamental concepts, technologies, and applications of autonomous vehicles.

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

• Grasp the core components and working principles of autonomous vehicles.
• Investigate the role of AI, sensors, and real-time data processing in self-driving systems.
• Analyze various levels of vehicle autonomy and their practical applications.
• Examine the ethical, legal, and regulatory aspects of autonomous mobility.
• Gain hands-on experience with autonomous vehicle simulations.

This instructor-led, live training in Botswana (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 Botswana (online or onsite) targets intermediate-level engineers, automotive professionals, and IoT specialists who aim 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.
• Analyse 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 guided, live training session in Botswana (available online or in-person) targets network engineers and automotive IoT developers with an intermediate skill set who aim to comprehend and deploy V2X communication technologies for autonomous vehicles.

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

• Grasp the core principles of V2X communication.
• Evaluate V2V, V2I, V2P, and V2N communication models.
• Deploy V2X protocols including DSRC and C-V2X.
• Create simulations for connected vehicle ecosystems.
• Tackle cybersecurity and privacy issues within V2X networks.