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  • Artificial Intelligence
  • Ryan Ayers
  • JAN 03, 2018

Four Ways AI Will Improve Our Transportation System

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Artificial intelligence has been a far-off dream for decades—but recent advances in AI have shown that we could soon be using it to solve some of society’s biggest problems.  Almost two years ago, in January of 2016, AI beat the world’s best player of the game GO, which is incredibly complex and has more potential moves than we have atoms in the universe. This shows that computers are now able to surpass human capabilities in some complex tasks, even if that task is a game. AI has gotten better at voice and face recognition, and error rates are getting lower and lower, signaling the start of widespread AI use.

While playing games and tagging Facebook photos are interesting and promising AI accomplishments, more practical applications of AI are on the horizon. Those applications will include helping the transportation industry to solve some of our biggest challenges in getting from place to place. Here are 4 important ways AI will help improve our transportation system.

1. Urban Design

Urbanization has been growing in the last few decades, and has caused skyrocketing housing prices, pollution, and increased congestion on roadways. 50% of the global population already lives in cities, and that percentage is expected to increase to 70% over the next 40 years. Public transportation is an essential service in urban areas, as pollution and traffic from growing numbers of personal vehicles becomes an even greater issues. AI can help offset these realities of urbanization by optimizing essential urban design.

Easing congestion in cities involves massive amounts of data, collected by sensors and cameras. AI can assist with urban design and traffic control in several ways, including adjusting variable speed zones based on traffic, traffic light timing, and smart pricing for vehicle tolls.

2. Car Communication

Key to the future of autonomous vehicles is car communication. Avoiding collision is, of course, a top priority for self-driving car developers, and these efforts have been largely successful. However, mini communications take place between drivers, cyclists, and pedestrians all the time—we often don’t even register how often we make eye contact with others on the road. So how can an automated vehicle safely navigate the many hazards of the road? AI advancements are making it possible.

Drive.ai is the most prominent company using deep learning to solve these communication obstacles facing self-driving cars, and see these vehicles as “social robots”. This AI advancement, and ability to pick up the nuances of human cues will be essential during the inevitable transition period: some cars on the road will be autonomous, and some will not. The AI needs to be able to not only navigate cues on the road, but provide them to drivers, ensuring safety for everyone. Drive.ai isn’t building autonomous vehicles; they’re outfitting existing vehicles with the sensors and other hardware they need to become autonomous, making it easier than ever to improve car communication.

3. Manufacturing

We’ve been quickly refining manufacturing techniques and technology since the first Industrial Revolution, and AI will be key in the next evolution of manufacturing. 3D printing and AI will mean that creating better forms of transportation will be more efficient and more dynamic. Automation and real-time feedback from vehicles on the road will help manufacturers optimize their products quickly. Instead of waiting for vehicles (both personal and commercial) to be shipped from overseas, they will be able to be manufactured locally, cutting down on wait times and resulting in better manufacturing without human intervention.

4. Optimization Through Machine Learning

In a nutshell, machine learning describes the ability of machines to learn without being given directives via programming. Machine learning is essential for future transportation systems, since self-driving cars need to be able to adapt to their environment, and will almost certainly not have programming to deal with every single situation the vehicle could encounter on the road. As more autonomous vehicles get on the road, they will use the information they gain through everyday driving situations to optimize themselves. AI is getting smarter thanks to innovative programmers—but it’s also making our transportation system better by building on the building blocks these programmers provided.

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