Artificial Intelligence: How it Could Transform Transportation
Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making—and already it is transforming every walk of life. In a recent Brookings report, Darrell West, director and vice president of governance studies, and John R. Allen, president of The Brookings Institution, discuss AI’s application across a variety of sectors, including transportation, address issues in its development, and offer recommendations for getting the most out of AI while still protecting important human values.
Most people are not very familiar with the concept of AI, according to the report. A 2017 survey of 1,500 senior business leaders in the United States showed that only 17 percent said they were familiar with the concept, and many weren’t sure what it was or how it would affect their companies.
In essence, AI systems have the ability to learn and adapt to make decisions. In transportation, semi-autonomous vehicles have tools that let drivers and vehicles know about conditions like congestion, potholes, and construction. Vehicles can utilize the experience of other vehicles on the road, without human involvement, and their shared experience is immediately transferable to other vehicles equipped with AI. “Advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions,” the report said.
In the case of fully autonomous vehicles, “advanced systems can completely control the car or truck, and make all the navigational decisions,” the report noted.
AI and machine learning are producing major innovations in transportation. Brookings research has found that over $80 billion was invested in autonomous vehicle technology between August 2014 and June 2017.
Autonomous vehicles include features like automated vehicle guidance and braking, lane-changing systems, the use of cameras and sensors for collision avoidance, and the use of AI to analyze information in real time.
Light detection and ranging systems (LIDAR) and AI are key to navigation and collision avoidance. LIDAR technology, first developed in the early 1990s, uses 1.064 nanometer wavelength laser light pulses to gauge distances by measuring the time delay between transmission of the pulse and detection of the reflected signal. More recent advances in computing allow for the processing of LIDAR’s large data sets, as are software tools that facilitate the visualization and exploitation of that data.
LIDAR systems are mounted on the top of vehicles, and, along with other sensors placed round the vehicle, provides information that keeps cars and trucks in their own lanes, helps them avoid other vehicles, and applies brakes and steering to avoid accidents.
“Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change,” the report noted. “This means that software is the key—not the physical car or truck itself.”
The push towards autonomous vehicles suffered a setback in March 2018 when an autonomous Uber car in Arizona hit and killed a pedestrian. “Both industry and consumers want reassurance that the technology is safe and able to deliver on its stated promises,” the report concluded. “Unless there are persuasive answers, this accident could slow AI advancements in the transportation sector.”