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| en:safeav:softsys:developmentchalenges [2025/10/17 12:09] – created agrisnik | en:safeav:softsys:developmentchalenges [2025/10/17 12:15] (current) – agrisnik | ||
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| **Scalability and Software Complexity** | **Scalability and Software Complexity** | ||
| - | As systems evolve, the number of nodes, processes, and data streams grows exponentially. For instance, a modern L4 autonomous vehicle may contain >200 software nodes exchanging gigabytes of data per second | + | As systems evolve, the number of nodes, processes, and data streams grows exponentially. For instance, a modern L4 autonomous vehicle may contain >200 software nodes exchanging gigabytes of data per second. |
| Problems: | Problems: | ||
| * Dependency conflicts between packages. | * Dependency conflicts between packages. | ||
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| * Configuration Management Databases (CMDBs) to track software–hardware combinations. | * Configuration Management Databases (CMDBs) to track software–hardware combinations. | ||
| * Digital twins to test updates before live deployment. | * Digital twins to test updates before live deployment. | ||
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| + | <figure Continuous Integration and Maintenance Workflow | ||
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| + | D**ata Management and Scalability** | ||
| + | AI-driven autonomy relies on vast datasets for training, simulation, and validation. Managing, labelling, and securing this data is an ongoing challenge ((Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: | ||
| + | Issues: | ||
| + | * Storage and transfer of multi-terabyte sensor data. | ||
| + | * Bias and imbalance in datasets. | ||
| + | * Traceability of model versions and training data. | ||
| + | Approaches: | ||
| + | * Cloud data lakes with edge pre-processing. | ||
| + | * MLOps workflows for dataset versioning and reproducibility. | ||
| + | * Federated learning for privacy-preserving model updates. | ||
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| + | **Human–Machine Collaboration and Ethical Oversight** | ||
| + | Autonomy software doesn’t exist in isolation — it interacts with human operators, passengers, and society. Thus, software design must incorporate transparency, | ||
| + | Key Considerations: | ||
| + | * Human–machine interface (HMI) design. | ||
| + | * Ethical AI decision frameworks. | ||
| + | * Liability and failover protocols during edge cases. | ||
| + | |||
| + | ===== Lifecycle of an Autonomy Software Stack ===== | ||
| + | |||
| + | The software lifecycle typically follows a continuous evolution model: | ||
| + | |||
| + | ^ Phase ^ Purpose ^ Typical Tools ^ | ||
| + | | Design and Simulation | Define architecture, | ||
| + | | Implementation and Integration | Develop and combine software modules. | ROS 2, AUTOSAR, GitLab CI, Docker. | | ||
| + | | Testing and Validation | Perform SIL/HIL and system-level tests. | Jenkins, Digital Twins, ISO safety audits. | | ||
| + | | Deployment | Distribute to field systems with OTA updates. | Kubernetes, AWS Greengrass, Edge IoT. | | ||
| + | | Monitoring and Maintenance | Collect telemetry and update models. | Prometheus, Grafana, ROS diagnostics. | | ||
| + | |||
| + | The goal is continuous evolution with stability, where systems can adapt without losing certification or reliability. | ||
