The Future of AI in Transportation

AI in transportation is extremely popular right now, but what does this look like, and how can it change things for the better?

It should come as no surprise that generative AI is the next big thing for a lot of industries as it’s seen as a credible way to streamline efficiency and workforces – enabling specialists to focus on more business-critical tasks.

However, introducing GenAI into any way of working requires organizations to have proper generative AI services on hand, which is why GenAI is currently the big trend in the manufacturing software development sphere.

And one specific area of manufacturing where generative AI is making waves is transportation. But how is AI being used to improve public transportation? What role does AI play in autonomous vehicle technology? How can AI contribute to traffic management and congestion reduction? How does AI enhance safety in transportation?

The following blog will answer these questions, but before it does, let’s first define what exactly is meant by “AI in transportation?”

Read more: AI in Media and Entertainment

How can AI change the transportation industry?

What is AI in transportation? AI in transportation refers to the use of artificial intelligence technologies to improve efficiency, ensure safety and manage traffic flows. It enables autonomous vehicle operation, optimizes public transit routes and schedules, and enhances, real-time traffic management.

In short, this means introducing and improving things like:

• Self-driving cars: are the most high-profile example of AI in transportation as most people are aware of the efforts that companies like Google and Tesla are making in this area, though there is still work to be done. However, as technology evolves, these vehicles will be more accepted and reliable thanks to complicated AI-backed networks of sensors, including cameras embedded in vehicles that can accurately track external obstacles, which will have a huge effect on public safety and transportation as they will significantly reduce accidents and human error.
• Traffic light management: computer vision machine learning enables traffic lights and other sign networks to absorb and analyze vast amounts of data to detect road blockages and reduce congestion in an area by redirecting traffic to less populated routes. They can also analyze times between pedestrian lights and lights meant for motorists to ensure motorists are given enough time to stop their vehicles before any pedestrian lights in the area turn green, which will significantly reduce the number of motor accidents, especially at busy intersections.
• Driver monitoring: this refers to GenAI being used to monitor a driver’s pose or facial expressions and warns them to pull over and rest if any of the data it analyzes shows signs of fatigue. Additionally, AI is also used to monitor a driver’s environment for any distractions that will take their attention off the road. The main distraction is, of course, using a phone while driving, but these intelligent systems also monitor the amount of chatting to passengers, as this can also divert the driver’s attention from the road.
• Travel time predictions: nobody likes traffic jams or delays while traveling – whether it be to the office or Disneyland. But it turns out that local governments don’t like these delays either as it costs $32.9 billion USD in the US alone. However, by reviewing the historic data collected by the various cameras leveraged by various GenAI systems embedded in many different types of vehicles, data from previous journeys can be compared. This includes data on any obstacles such as common areas where traffic jams frequently occur for example. This data can then be used to predict travel times as accurately as possible by taking these obstacles into account, which of course can be doubly important to public transport providers or workers in the trucking industry.
• Pedestrian monitoring: finally, this outline of the benefits of AI in transportation comes full circle as once again self-driving cars make an appearance on this list. Only this time the focus is on what AI can do to monitor pedestrian behavior to ensure the “self-driving” part of “self-driving car” becomes a reality. However, the issue here is that people are unpredictable, which means the computer vision machine learning system these vehicles are based on needs to train itself on vast amounts of data if it wants to drive on a real road, in the real world. This is not impossible of course, but it does mean that self-driving cars are still a few years away even with the support of the smartest AI on the market today.

There are other areas that could be added here, of course, but regardless of what area of transportation GenAI is applied to, human error and costs will be reduced significantly for car manufacturers and road safety organizations alike. Additionally, it will increase efficiency across the board, ensuring traffic systems work better and travel is safer for everyone all around.

Use cases of AI in transportation

These are just a few examples of how AI is being used in transportation today, but what about some real-world examples? In this regard, there are four main areas that have a wealth of examples to draw from, and for the sake of brevity, this blog will only give one example from each area. They are:

• Autonomous driving vehicles: one of the main companies operating in this area is Waymo, which was previously Google’s way of exploring the potential of autonomous vehicles but has now become its own company. Today, Waymo design and build autonomous vehicles by putting safety at the heart of everything they do through the 360-degree technology they have created to detect external obstacles before they become a problem for driver and vehicle alike.
• Car manufacturing: nearly every car manufacturer has implemented generative AI somewhere into their ways of working – from finance and accounting to manufacturing – but this article will focus specifically on what General Motors has done with GenAI in their manufacturing processes. GM have added predictive analytics into their manufacturing processes to analyze production history and identify patterns and problem areas to increase efficiency and quality across the board.
• Driving assistance: a fast-growing area of interest in the world of driving assistance is making voice assistants more engaging, more life-like and more intelligent. Sapient X is one of the companies currently working on improving how your car sounds when it provides information. It does this by leveraging speech recognition technology, natural language processing and smart avatars to help the assistant they offer you understand more about the context around data as well as how to interpret complex sentences, a driver’s current emotional state, or any given user’s personal preferences.
• Autonomous delivery: last mile delivery is also another area where AI can be leveraged effectively, which is why Kiwibot is building its own last mile delivery network that enables users to place restaurant orders through an app and track their delivery in real-time from pick up to when it arrives at their home.

This list is by no means exhaustive, but it does give you some idea of how GenAI is being leveraged today with more new avenues sure to open for this technology as soon as the systems driving it become even more intelligent.

Software Mind’s experience in AI development services

All the above is why the future looks bright for AI. Today is the worst AI will ever be, and if it can do all this at its worst point, imagine what it could do for car manufacturers or local governments in five or ten years.

At Software Mind, we know that implementing AI into manufacturing and other industries can be challenging – not only because the technology can be hard to understand, but also because of the worry it can foster in your workforce.

That’s why when you talk to our experienced experts you will not only understand the benefits AI can deliver to your business and how to implement it into your way of working at speed, but your workforce will also understand its limits and stop worrying about AI replacing them.

Our cross-functional software development team is happy to talk about what AI can do for you wherever you are, so reach out today using this form to learn more.