What Is Intelligent Edge?
Benefits of Intelligent Edge
When a vehicle needs to make a safety decision, such as whether to engage braking automatically in an emergency, it must do this using as much data and the most sophisticated modeling possible. Intelligent Edge means the vehicle will have sufficient local computing power to achieve this. The result will be greater safety when driving because sending the data to a central server and waiting for results would inject too much latency for a real-time response.
Reducing latency will also improve less critical systems such as infotainment or voice recognition. Instead of waiting for server responses, users will receive immediate feedback from their inputs. This will give users a much more pleasant experience rather than a frustrating delay.
Another benefit is security. When data is sent to a central server, it is open to interception in transit. While this can be mitigated with an encrypted pathway, if the data never leaves the vehicle’s local environment in the first place, there can be no mass attempt to compromise it. Keeping data local also assists compliance with data privacy rules such as the European GDPR or Californian CCPA.
A further potential benefit is reducing the cost of centralized services and infrastructure. If a lower volume of data is sent back to the server, the connectivity infrastructure won’t require so much bandwidth. If the server is processing less data, there will also be a reduced need for centralized compute infrastructure, reducing server costs.
Examples of Intelligent Edge
Some automotive applications of Intelligent Edge are here now, and some are emerging. ADAS is the most significant current application of Intelligent Edge. While the software controlling ADAS will have been developed centrally and updates may have been deployed using data from real-world driving, it reacts locally to its sensor input. This requires the car to have significant computing power on board.
A future application of Intelligent Edge in automotive will be during accidents. If a car breaks down in poor visibility, it can share its location with other vehicles. This would notify the drivers of those other nearby vehicles to be cautious when approaching that location.
Another future possibility will be enabling vehicles to work with data from shared sensors. For example, advanced adaptive cruise control wouldn’t just react to the car in front slowing down but could begin to feather its speed as soon as the one in front of that is detected as putting its brakes on. With so many rear-end shunts being caused by unexpectedly encountering stopped or slowing traffic, this could drastically improve safety and smooth the flow of congested roads.
How Intelligent Edge Works
Modern vehicles contain a burgeoning array of sensors that produce an ever-increasing amount of data. In a thin client scenario, this would be sent back to the central server for processing, and then the results would appear on the local device. Intelligent Edge equips the vehicle’s onboard computing with sufficient processing power to interpret and act upon the sensor data it receives without needing centralized assistance.
An Intelligent Edge device is not disconnected from the central server but can act without needing a connection. It may also communicate with nearby devices of its type directly without going via the centralized server. The Intelligent Edge device could even be processing AI/ML models, such as for autonomous driving, improving the behavior models it has been programmed with.
Intelligent Edge Vs. Cloud Computing
Over the last decade, cloud computing has caused a revolution in how infrastructure is delivered. The prevalence of fast connectivity has enabled mobile devices to access sophisticated applications through the cloud from anywhere. But no server-based system can deliver the immediate response of a local system. Increasing data demands also put undue pressure on bandwidth. Intelligent Edge solves this problem by only sending necessary data to the cloud.
A hybrid system that harnesses the Intelligent Edge and potent centralized compute capabilities can create Distributed AI/ML configurations. This enables low-latency action from the vehicle and allows it to contribute data centrally that will improve future services. Current public beta testing of autonomous driving systems takes this approach.
The Connected Autonomous Shared Electrified (CASE) future of vehicles will entail a blend of Intelligent Edge in automotive with sophisticated cloud-based services. Platforms such as BlackBerry IVY are specifically designed to unlock the benefits of this hybridized future.
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