Are Cloud-Based Architectures the Key to Scalable and Intelligent Autonomous Transportation?

At times, autonomous transportation seems like an unreachable goal. Automakers and AI companies have discussed the possibilities for years, yet we’re not much closer to self-driving cars now than in 2020. Cloud-based architectures could help get the transportation industry nearer to that goal.

No single technology will solve the self-driving riddle. Vehicle autonomy is highly complex, so when fully driverless cars do emerge, it’ll be a culmination of cutting-edge tech. That said, the cloud could be an important but overlooked part of that puzzle.

Where Are All the Self-Driving Cars?

Back in 2018, Tesla CEO Elon Musk predicted the company would have fully autonomous cars by 2019, but clearly, that didn’t happen. Autonomous features like adaptive cruise control and lane correction have become more common, but full self-driving remains an elusive target. AI has made massive strides since then, and there’s more driving data for these cars to use than ever, so why aren’t they a reality yet?

There are multiple obstacles in the way. Most notably, traffic. AI is best at logical, data-based tasks. Traffic — despite the many laws trying to make it otherwise — is unpredictable. The AI in self-driving cars must anticipate billions of possible scenarios, determine which is most likely, and act on it in fractions of a second. That requires a lot of data and a lot of processing power.

Poor road conditions and other environmental factors could also hinder the sensors these AI models rely on. Considering how a single mistake on the road could cost lives, people aren’t always excited to take any risks with the technology, either. Consequently, there are social and political barriers, too.

How Cloud Architecture Could Help

Cloud architecture can’t address all of these obstacles, but it helps with a few of them. If the transportation industry embraced the cloud, it could get them one step closer to full autonomy.

Boosting Processing Power

One of the biggest ways cloud architecture aids self-driving vehicles is by providing more computing power. It’s not exactly easy to put a supercomputer in a car and keep it practical and affordable. However, cloud providers design their data centers to support advanced technologies and be scalable, offering a solution.

The cloud lets vehicles connect to computing resources they wouldn’t be able to fit within the car itself. That way, the car’s onboard hardware no longer limits the kinds of processes it can run. The cloud model also reduces the financial barrier to entry for using these kinds of resources, helping keep autonomous cars affordable.

Self-driving cars still need some onboard computing capacity to support them in remote areas where they may lack a strong connection. So, while cities are the most unpredictable driving environments, they are the easiest places to implement these kinds of cloud connections. Autonomous vehicles would have the most processing power where they need it the most.

Letting Cars Teach Each Other

Easier data-sharing is another advantage of the cloud for autonomous driving. If self-driving cars connect to a common cloud to share their traffic data, they’d accelerate each other’s machine learning.

Reliable AI requires a lot of data, and autonomous driving models require more than most because of how varied road conditions are. It’s highly unlikely a single car will experience enough of these conditions to learn to adapt to all possible scenarios on its own. If hundreds or thousands of vehicles pull from each other’s experiences in the cloud, they’d learn much more in less time.

Cloud communications could also help autonomous vehicles navigate. Smart city infrastructure can improve traffic flow with 95% accuracy by directing traffic according to real-time data across the city. If self-driving cars access this data and share info about traffic in their area with others through the cloud, they’d enable safer, more efficient navigation.

Equipping Autonomous Driving for Future Demands

It’s also important to consider the cloud’s scalability. Experts say hyper-scalability through the cloud could drive over $1.7 trillion in value by 2030, making it the most valuable of the cloud’s benefits. That same scalability could future-proof self-driving cars as the pace of innovation rises.

Over time, researchers will discover ways to improve autonomous vehicles’ safety, versatility, and accuracy. As these changes come, cars’ onboard hardware may grow outdated before they can capitalize on these innovations. The cloud removes that barrier by making it easier for transportation companies to scale their computing resources.

Cars could access cutting-edge AI technology regardless of their onboard power by relying on cloud connections. The companies developing self-driving algorithms could expand their own resources faster by using the cloud instead of expensive on-premise data centers. In either case, cloud computing makes it easier to capitalize on new technologies.

Autonomous Transportation Needs the Cloud

It will still likely be a long time before fully autonomous cars are possible, much less common. Taking advantage of the cloud could shorten that timeline, though.

Self-driving cars need massive computing resources, shared information, and scalability. Cloud architectures provide all three. While the cloud isn’t the only technology necessary for scalable, intelligent, autonomous transportation, they are a crucial step in that journey.