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--- en:safeav:as:general [2025/10/17 08:48] – [Middleware and Frameworks] agrisnik
+++ en:safeav:as:general [2025/10/17 08:57] (current) – [Middleware and Frameworks] agrisnik
@@ Line 33: / Line 33: @@
 | Control / Execution | Translate plans into commands for actuators | PID, MPC, low-level control loops |
 | Communication / Coordination | Handle data sharing between systems or fleets | Vehicle-to-vehicle (V2V), swarm coordination |
+Depending on functional tasks system’s architecture is split into multiple layers to abstract functionality and technical implementation as discussed above. Below is a schema of a generic architecture to get a better understanding of typical tasks at different layers.
+<figure Generic Autonomous System Architectures>
+{{ :en:safeav:as:rtu_ch1_figure1.png?400| Edge IoT system' architecture}}
+<caption>Generic Autonomous System Architecture </caption>
+</figure>
+===== The Role of AI and Machine Learning =====
+Modern autonomous systems increasingly integrate machine learning (ML) techniques for perception and decision-making. Deep neural networks enable real-time object detection, semantic segmentation, and trajectory prediction ((LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444)). However, these data-driven methods also introduce architectural challenges:
+  * Increased computational load requiring edge GPUs or dedicated AI accelerators.
+  * The need for robust validation and explainability to ensure safety.
+  * Integration with deterministic control modules in hybrid architectures.
+Thus, many systems adopt hybrid designs, combining traditional rule-based or dynamics-based control with data-driven inference modules, balancing interpretability and adaptability

en/safeav/as/general.1760690911.txt.gz · Last modified: 2025/10/17 08:48 by agrisnik