Hybrid Embedded/Cloud systems: bridging real-time and resource-intensive tasks
Modern vehicles are increasingly equipped with Hybrid Embedded/Cloud systems, a design approach that enhances the capabilities of Electronic Control Units (ECUs) by integrating cloud connectivity. This combination enables real-time operations within the vehicle while leveraging the cloud for more complex, resource-intensive tasks.
Let’s break down how these systems work and explore an example of how they can be applied.
How Hybrid Embedded/Cloud systems work
ECUs are specialized computers in vehicles, each responsible for tasks like managing the engine, monitoring battery health, or enabling driver-assistance features. Traditionally, these systems operated in isolation, managing vehicle functionality within a closed network.
However, with the advent of internet connectivity, ECUs can now communicate with Cloud platforms, creating a hybrid system. In this setup:
The ECU manages real-time operations, such as processing sensor data and making immediate decisions required for vehicle safety and performance.
The Cloud handles resource-intensive tasks like analyzing data from multiple vehicles, retraining AI models, and deploying updates.
This division of labor allows each system to focus on what it does best: immediate responsiveness from the ECU and large-scale data processing in the cloud.
A practical example: Adaptive Cruise Control
A good illustration of Hybrid Embedded/Cloud systems is the Adaptive Cruise Control feature, which adjusts a vehicle's speed in response to traffic conditions.
Watch this video for a short example of how adaptive cruise control works.
Here’s how such a system could leverage hybrid architecture:
The ECU processes real-time data from sensors like cameras, radars, and LiDAR to make immediate decisions about speed adjustments or emergency braking. This ensures the system reacts quickly to changing road conditions.
The ECU sends data collected during the trip to the cloud, where it is aggregated with similar data from other vehicles. The cloud analyzes this information to identify patterns, improve performance, and optimize AI models.
The cloud deploys the newly retrained AI model.
Whenever possible, without disrupting real-time operations, the ECU downloads the new model and replaces the old inference model with the updated one.
While this is one possible implementation, hybrid systems offer flexibility and could support a range of other vehicle functions.
Why hybrid systems are important
The integration of embedded systems and cloud computing offers several advantages:
Real-time responsiveness: critical safety features can function instantly via the ECU.
Continuous improvement: data aggregated from multiple vehicles enables constant refinement of AI models in the cloud.
Scalability: cloud-based updates benefit not just a single vehicle but entire fleets, improving overall system performance.
This hybrid approach is increasingly important as vehicles adopt advanced driver-assistance systems and move closer to full autonomy.
Final thoughts
Hybrid Embedded/Cloud systems are redefining the way vehicles operate, combining the immediacy of real-time processing with the power of cloud-based analytics and AI. While adaptive cruise control is just one example of this technology, the possibilities extend to numerous other applications across various industries.
