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)Raj, A., & Saxena, P. (2022). Software architectures for autonomous vehicle development: Trends and challenges. IEEE Access, 10, 54321–54345.) </caption> </figure> Data 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 1). 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. 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, accountability, and explainability. 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, run models, and simulate missions. | MATLAB/Simulink, Gazebo, CARLA, AirSim. | | 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.
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