Artificial Intelligence is already impacting Manufacturing, Retail, Marketing, Healthcare, Food industries and more. Today we will take an in-depth look at another industry, that with proper AI expertise from development companies could be disrupted.
Transportation is an industry that helps humanity with moving people their belongings from one location to the other. While doing that, this industry had experienced countless twists, turns, breakthroughs, and setbacks to get to the place where it is now. The year 1787 was the defining one for this industry because steamboat was introduced and changed everything. Transportation was not limited by the animal-drawn carts anymore. Years later more inventions followed like bicycles, trains, cars, and planes.
In the year 2019, we have another milestone reached - vehicles can now move and navigate without human assistance at all. Recent technological advancements made it possible. Of course, one of these technologies is Artificial Intelligence, which already helps transportation lowering carbon emissions and reducing financial expenses.
We already can say, that AI successfully transferred from Sci-Fi movies and TV shows to become our reality, despite many of us still don’t realize it. AI provides machines with human intelligence, to a certain degree, of course. Machines now can mimic humans, automate tasks, and learn from experience. Repetitive tasks can now be easily handled by machines. The learning feature will eventually lead AI to take on critical-thinking jobs and make informed and reasonable decisions. The world is watching, that’s why there are major investments going into the transportation sector. P&S Intelligence predicts that the global market for AI in transportation will reach 3.5 billion dollars by the year 2023. How did it get to that point? Let’s look at history.
In the 1930s there were the first mentions of self-driving cars concepts, in science fiction books of course. Since the 1960s AI developers were dealing with the challenge to build them, and while in the 2000s there were autonomous vehicles on Mars, self-driving cars were still prototypes in laboratories. So many factors occur on the road like traffic and actions of pedestrians, that what made driving in the city complex.
While in 2000 some prototypes existed, there were few predictions they would get to mass production by 2015. However, in 2004 the very fast progress in Machine Learning for perception tasks and the evolution of the industry launched speedy progress which ultimately led us to this point. Google’s autonomous vehicles and Tesla’s semi-autonomous cars are already on the streets now. Google’s cars logged 300,000 miles without an accident and a total of 1,500,000 miles without any human input at all.
Tesla is offering the self-driving capability to existing cars with the software update but this approach is questionable. The problem with semi-autonomous that human drivers are expected to engage when they are most needed, but they tend to rely too much on AI capabilities. This led to the first traffic fatality with an autonomous car in June 2016, which brought attention to this problem.
Very soon sensing algorithms will surpass greatly human capabilities necessary for driving. Automated perception is already close to human’s, for recognition and tracking. Algorithm improvements in higher-level reasoning will follow, leading to a wide adoption of self-driving cars in 2020.
While autonomous vehicles are the major part of our topic, there are more use cases we can talk about.
While the level of adoption of Artificial Intelligence in different industries and countries varies, there is no denying that technology is a perfect fit for transportation. Look at the following examples.
Companies around the world are already starting to implement autonomous buses to the infrastructure of the city, the best-known cases are from China, Singapore, and Finland. But different city infrastructures, weather conditions, road surfaces, etc., make AI applications of autonomous buses very dependent on the environment.
Local Motors from the United States of America presented Olli - an electric shuttle that doesn’t need a driver. This company provides low volume manufacturing of the open-source vehicle design, relying on the variety of the micro-factories. Watson Internet of Things (IoT) for Automotive from IBM is the heart of the processes in Olli. The smart electric shuttle can transport people to the requested places, giving comments on local sights and answer questions on how it operates. There were five APIs from Watson IoT for Automotive platform: Text to Speech, Speech to Text, Entity Extraction, Conversation, and Natural Language Classifier.
Artificial Intelligence is already implemented in resolving the problems in traffic control and traffic optimization area. More than that, we can also trace some use cases, were AI is dealing with prediction and detection of traffic accidents and conditions. This is achieved by combining traffic sensors and cameras.
Surtrac from Rapid Flow is originated from the Robotics Institute at Carnegie Mellon University. Surtrac system was first tested in the Pittsburgh area. The idea of this system is installing a network of nine traffic signals in the three biggest roads. The reported results are: the reduction of the travel time by more than 25% and wait times by 40%. After this success, the local Pittsburgh government joined forces with Rapid Flow install up to 50 traffic signals to other parts of the city.
Stricter emission regulations from the government and environmental challenges are forcing the industry to change. The International Transport Forum (ITF) reports that using autonomous trucks will save costs, improve road safety and lower emissions.
A startup called Otto (now known as Uber Advanced Technologies Group after the $680 million purchase in 2017) was responsible for the first-ever delivery by autonomous truck in 2016. The truck was delivering 50,000 cans of Budweiser for the 120 miles distance. A Chinese startup TuSimple performed a level 4 test of the driverless truck for 200 miles in 2015. The truck’s system was trained using deep learning, simulating tens of millions of miles.
General Electric has presented smart locomotives, to boost overall efficiency and the economic benefits of their rail transport solutions. GE’s locomotives are equipped with sensors and cameras, which gathers data for a Machine Learning application. The information is aggregated on the edge gateway, providing decision-making in real-time. General Electric already improved speed and accuracy in detecting things. Their first project resulted in a 25% reduction in locomotive failure.
Benefits of AI in Transportation
So here are some benefits that could come from implementing Artificial Intelligence in the transportation industry:
In 2016 a call of proposals was released by the United States Department of Transportation (USDoT), asking medium-size cities to start imagining smart city infrastructure for transportation. The best city to do that is planned to receive 40 million dollars for the demonstration of AI potential in their city. Meanwhile, the US transportation research board claims that there following application of AI on transportation is emerging: city infrastructure design and planning, demand modeling for cargo and public transport and travel behavioral models. However, one of the major restraints of innovation is the privacy issue. Government and legal regulations could limit the speed of innovation and adoption in the industry.
AI innovation is closer than we think. According to the International Institute for Sustainable Development, the tests of completely autonomous trains for long distances are already running. 2.2 to 3.1 million driver jobs could be in danger of replacement in the USA by self-driving vehicles. On-demand car services like Uber will switch to driverless vehicles as soon as they can. Do you want to learn more about Artificial Intelligence and Machine Learning development? AI & ML technologies could elevate your business to an entirely new level. There are plenty of companies providing AI expertise. We researched evaluating
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