Just a decade ago, the prospect of self-driving vehicles taking over the highways still seemed a little far-fetched. While there are still a number of difficulties facing the widespread implementation of autonomous vehicles, both the tech and automotive industries increasingly see them as the inevitable future of transportation. Whether it takes ten or twenty years, self-driving vehicles are coming. The real question is how quickly network infrastructures can adapt to prepare for the massive challenges this technology presents. One way or another, edge computing will be a critical part of this future.
It’s taken for granted that autonomous vehicles will generate a lot of data, but it’s worth taking a moment to think about just how much data can be expected in the future. Estimates vary, but a single, self-driving test vehicle can produce a staggering 30 terabytes of data in a single day of driving. There are over 250 million automobiles on the road in the US alone, so even if only a small percentage of them are replaced by autonomous models in the next few years (the 10 million expected to hit the road by 2020 would represent less than 1% of the total), the amount of data being generated will be immense.
Much of this data will be unstructured and will need to be run through powerful analytics programs to produce actionable data with any value to businesses. Edge computing architectures will help to prioritize what data needs to remain on the edge to be processed by the vehicle’s onboard computing power and what data should be relayed back to data centers for analysis. Edge data centers will serve a critical role in this network, functioning as a relay station and providing extra computing power for mission critical analytics that need to remain near end users.
The autonomous vehicle is the ultimate Internet of Things (IoT) device. Powerful enough to handle onboard computing tasks and well-connected enough to interface with multiple networks and devices, they will be in constant communication with the world around them and making split second decisions based upon the information flooding in from an array of sophisticated sensors. No connection will be more important, however, than the machine to machine (M2M) communication between a self-driving car and the other vehicles on the road.
Every autonomous vehicle broadcasts critical data on weather changes and road conditions, allowing other vehicles to learn about potential hazards like detours, debris, accidents, and flooding early enough to adjust accordingly. Much of this data will be able to be sent and received between the vehicles themselves, without requiring them to interface with distant cloud servers. This communication effectively turns every vehicle on the highway into an extension of every other vehicle’s sensors, providing the very best information possible on the highway environment. With so much data pouring in, of course, it will be more important than ever for vehicles to have ready access to edge data centers to offload non-mission critical data for later analysis.
It’s one thing for a computer game to experience lag when connecting to a central server; while latency could mean the difference between victory and defeat, it’s still only a game and no actual lives are at stake. In an autonomous vehicle, however, even a few milliseconds of delay can result in an accident and catastrophic loss of life. The stakes are simply too high to allow the vehicles’ networks to be plagued by lag. Self-driving cars need to react immediately to changing road conditions; they can’t simply come to a stop while waiting for instructions or recommendations from a distant cloud server analyzing data.
Edge computing offers the only real solution to this problem. In fact, the heavy investment in autonomous vehicle research has been one of the reasons so many tech companies are pushing to improve and expand their edge computing architectures. By colocating servers and computing resources in versatile edge facilities located in both high traffic areas and more far-flung areas with limited bandwidth access, companies can ensure that their autonomous vehicles are able to access the data they need with minimal latency to make decisions quickly. As IoT devices, self-driving cars also have the ability to make their own decisions without relying on guidance from servers located in distant data centers.
For autonomous vehicles to reach their full potential, high traffic urban areas will need to step up their IoT game. Sensors relaying information about every thing from road conditions to real-time reports on congestion will allow fully IoT integrated “smart cities” to provide self-driving cars with valuable information to help them make better, more efficient decisions. By creating such a data rich environment with ready access to local data center computing resources, cities can help to leverage the full potential of IoT devices and edge networks.
While autonomous vehicles get most of the press, many cities are already making significant investments to prepare for the self-driving future. And it’s not just the wealthiest, most populous cities taking the first steps. In July of 2018, for example, Columbus, OH announced its ambitious Connected Vehicle Environment program, which is itself one of nine projects in the Smart Columbus initiative to help the city transform its transportation and network infrastructure. Funded by a $40 million grant from the U.S. Department of Transportation after winning the Smart City Challenge in 2016, these programs are just one example of how cities are positioning themselves to capitalize on the potential of autonomous vehicle technology.
The expansion of edge computing networks and IoT devices will continue to put strain on existing cloud infrastructures. Increasingly, the old system of connecting multiple devices to centralized servers is no longer sufficient to meet the demands of innovative new technology. While edge data centers are a good solution for local networks, autonomous vehicles present a unique problem in that they have tremendous range. Companies will need to find ways to extend their network reach to maintain connectivity with self-driving vehicles no matter where they go. This will be especially critical for the coming revolution in autonomous trucking, which will likely become commonplace long before consumer grade vehicles.
Fog computing expands the reach of networks by encompassing not only centralized cloud resources, but also the processing power of edge data centers and IoT devices. Finding ways to expand this reach with innovations like micro data centers and colocating computing resources at the base of cellular towers will be a vital step in making the future of autonomous vehicles both possible and practical.
Edge computing practices continue to find new potential uses every year as the number of IoT devices grows. With the widespread adoption of autonomous vehicles seemingly poised to take place sometime within the next decade, it will be critical for data centers to extend their reach and capabilities to facilitate this revolution in transportation. While the self-driving future may not be here quite yet, now is the time to put the necessary data infrastructure in place to ensure that the implementation of these remarkable IoT devices will be as smooth as possible.