Vehicle-to-infrastructure technology offers urban drivers an additional layer of safety while navigating busy motorways. The opportunity the V2I communication systems represent has driven the U.S. Department of Transportation and Transport for London to both begin testing the usability of said technology on pocket motorways.
Even as these tests are taking place, however, V2I is evolving. Where IT and the broader IoT once relied solely on a distant Cloud to retain and assess data collected on the road, edge computing now offers the same communication systems the more immediate opportunity to analyze their surrounding environment.
While the increase in speed is nearly negligible to human understanding, it updates V2I and its usability. Instead of relying on distant servers, the self-driving cars of the future will be able to make their decisions based on information assessed in-house.
But How will edge computing impact IoT communication in the auto industry? Is it possible for an in-vehicle device to store the necessary data self-driving cars that need to operate safely?
Edge computing is defined as a process through which essential data is stored more closely to the computer that processes it. Unlike cloud computing, it forgoes the need for a centralized data hub and instead pushes data from storage to assessment more rapidly.
That said, successful edge computing requires smart devices, like autonomous cars, to more readily store the data that it collects. As such, edge computing also describes the optimization of smart devices for more intensive storage.
As has been touched on briefly already, edge computing is considered to be an evolution within the automotive industry because of the time it shaves off of V2I’s assessment of a vehicle’s surroundings. Even though that amount of time seems negligible, it actually adds to the safety of autonomous driving while also reducing the resources these vehicles need to operate safely on the road.
That said, the communication between a vehicle’s various parts, its internal computer, and its surrounding environment remain a complex process. An autonomous car’s motor, tyres, generators, and other parts must take the surrounding road conditions, weather, and additional information into account multiple times per second. That information must then be transferred to the computer existing within the vehicle.
When it comes to cloud computing, that data is then sent off to distant servers. Edge computing, however, would see car’s computers able to store that data and process it without outside assistance. Non-critical data can then be more readily off-loaded, whereas data shared with the vehicle from surrounding smart buildings can be immediately implemented into the car’s decisions.
At this point, it’s estimated that the average autonomous vehicle generates 30 terabytes of data on a daily basis. This presents both vehicles and smart infrastructure with a storage problem.
If, as predicted, 10 million self-driving cars will be operating on the roads in 2020, then the IoT that supports autonomous cars and their surroundings need to be prepared not only to offload non-essential data but to readily store that which could immediately impact the safety of a car’s passengers.
Current solutions suggest a pairing of edge computing and cloud computing to handle the massive amount of data autonomous cars produce. The collaboration would see relevant data remain in a car’s computer. At the same time, non-relevant data would be sent into cloud storage, where it would still be assessed even as it was classified as non-essential.
Why focus on this particular evolution within the realm of the autonomous car? Autonomous cars are seen as the next step in future roadway safety. Their ability to react to their environment free of the influence of human processing time offers a safer driving experience for passengers and pedestrians.
However, the autonomous cars currently in operation are far from perfect. A number have even caused scandals due to their apparent lack of safety.
The rise of IoT now offers the automobile industry a solution to that perceived problem. If the IoT can be safely implemented in international plants, then who’s to say it can’t make an urban living and driving a little safer? As it stands, the milliseconds’ autonomous cars save drivers could reduce accidents by 90%. Shave a few extra milliseconds off that time, and roadway fatalities may become a thing of the past.
The Evolution of Edge Computing
That said, edge computing’s partnership with V2I is restricted, for the moment, to a collaboration with cloud computing. Evolutions of the practice will require an increased optimization in data storage so that autonomous cars can better process the 30 terabytes of information provided to them by motorways on a daily basis.
Until then – and until the implementation of smart infrastructure into prominent urban areas – the existing collaboration between these two types of computing will continue.