Curriculum
- 4 Sections
- 36 Lessons
- 4 Days
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- Fundamentals of AUTOSAR and Non-AUTOSAR Frameworks9
- 1.1What is AUTOSAR? Purpose, architecture, and evolution.
- 1.2Non-AUTOSAR systems: Definition, applications, and limitations.
- 1.3Key components: RTE (Runtime Environment), Basic Software (BSW), and Application Layer.
- 1.4AUTOSAR vs. Non-AUTOSAR: Pros, cons, and selection criteria.
- 1.5Compatibility challenges and transition scenarios.
- 1.6Impact on automotive software development and validation.
- 1.7Case study: Migration from Non-AUTOSAR to AUTOSAR in legacy systems.
- 1.8Examples of Non-AUTOSAR applications in low-complexity ECUs.
- 1.9Workshop: Mapping AUTOSAR architecture onto a given automotive software use case.
- AUTOSAR Standards, Tools, and Development Process9
- 1.1Classic AUTOSAR vs. Adaptive AUTOSAR.
- 1.2Methodology: Software Component Description, ARXML files, and ECU Configuration.
- 1.3Standards for safety (ISO 26262) and cybersecurity (ISO 21434).
- 1.4AUTOSAR development tools: Vector DaVinci, EB tresos, and ETAS ISOLAR.
- 1.5Integrating software components using RTE.
- 1.6Debugging and validation workflows in AUTOSAR environments.
- 1.7Case study: Implementation of AUTOSAR in ADAS systems.
- 1.8Discussion: Real-life issues during integration and how they were resolved.
- 1.9Practical exercise: Configuring an AUTOSAR-based ECU using industry tools.
- Non-AUTOSAR Development and Hybrid Systems9
- 2.1Structure and design principles of Non-AUTOSAR systems.
- 2.2Handling scalability, flexibility, and performance.
- 2.3Impact on testing, debugging, and maintainability.
- 2.4Co-existence of AUTOSAR and Non-AUTOSAR systems in a vehicle.
- 2.5Middleware solutions and communication interfaces.
- 2.6Examples of hybrid ECUs in powertrain and infotainment.
- 2.7Case study: Hybrid ECUs in electrification and autonomous systems.
- 2.8Discussion: Practical issues in integrating AUTOSAR and Non-AUTOSAR modules.
- 2.9Interactive Exercise: Design a hybrid system architecture for a multi-ECU vehicle platform.
- Future Trends and Problem-Solving9
- 3.1AUTOSAR Adaptive for connected and autonomous vehicles.
- 3.2Impact of AI, machine learning, and over-the-air updates.
- 3.3Advancements in Non-AUTOSAR architectures for edge computing.
- 3.4Strategies for reducing software complexity in hybrid systems.
- 3.5Optimizing runtime performance and memory utilization.
- 3.6Managing cross-platform compatibility.
- 3.7Case study: Transitioning from legacy Non-AUTOSAR to Adaptive AUTOSAR for EVs.
- 3.8Discussion: Challenges in implementing cybersecurity measures in hybrid systems.
- 3.9Capstone Activity: Hands-on debugging of a hybrid system simulation.