AI Disruptions from Autonomous Vehicles Humans set out to build self-driving car

Humans set out to build self-driving cars to avoid 30,000 accidents each year and the cost of traffic to consumers and the trucking industry. The AI of the Autonomous Vehicles has different plans for our future driverless world.

Americans spend 7 billion hours commuting to work costing a total of 3 Billion gallons of fuel or $160 billion. But the cars remain parked 95% of the time. 500,000 trucks in America contribute to 7% congestion and spend $28 Billion in fuel cost. Statistica projects the global revenues from driverless cars to be $42 billion with a US share of $16 billion by 2021. The car sensors market is expected to be $26 billion by 2030. In this article, I will show you how the AI powering the self-driving car has plans to affects all industries and our way of life by 2030 by creating a cascade of disruptions beyond the self-driving car.

Driverless Technology is the foundation of the Car AI

All the focus of self-driving cars has been on perfecting the autonomous vehicle technology. John Deere in Europe has been selling autonomous tractors since 2008. In 2016 the LUTZ driverless Pod started testing in Milton Keyes and GATEway a driverless shuttle is testing in Greenwich. Google has spent eight years of testing to teach the car to drive among humans and follow human road rules and behaviors. Cars learn to understand the roads, traffic rules, and to handle crossing pedestrians and other vehicles using AI. They use Machine Learning, Deep Learning, Computer Vision, Affective Computing and Cognitive Technologies. This Artificial Intelligence is disrupting several industries and areas of life beyond the driverless cars.

Top AI Disruptions from Driverless cars

  1. Computer Vision technology has been developing to help the vehicle detect lanes. Computer Vision does not stop at the car and is used in Robotic vision, medicine, and Home Security. Robots use the same computer vision to see. Doctors can use computer vision to see inside a human body using small IoT pills called nanobots. Home security cameras capture videos that are processed using Computer Vision and AI to make sense of patterns to spot intruders.

  2. Deep Learning technology is used teach the car to identify traffic signs and other vehicles on the road. These CNN, the neural networks have applications beyond the car in many other applications such as image recognition such as Google Photos. Delta airlines uses facial recognition to speed passenger bag drops in Minnesota which is based on the same CNN technology algorithms.

  3. Car AI has to learn the nuances of human communication using a technology called affective computing. This same technology will help machines interact better with people in factories and as social robots in home and hospitals.

  4. The car has to communicate with the road, traffic lights, parking lots and other city infrastructure such as bridges. As the driverless car AI thinks in machine speed, the city infrastructure with its IoT sensors will become Cognitive AI creating new conveniences.

  5. Cognitive Predictive technologies use data to make predictions to make the AI from the car smarter. In Dec 2016, a Tesla Model S car driving under driver assist autopilot mode was able to predict an accident two cars ahead of a Tesla beyond the visible view of a driver and safely park the Tesla and saving a collision in Netherlands. It used Tesla Autopilot’s Forward Collision Warning technology. Predictive algorithms are very pervasive with a far-reaching disruption across many industries such as farming, manufacturing, and transportation.

So as we watch the self-driving car become smarter to drive to keep us safer, the Car AI is getting ready to transform our lives across several industries to help ease us to the world of 2030 where the driverless car will be accompanied by robots and other cognitive IoT making our lives smarter.

About the Author: Sudha Jamthe is the CEO of IoT Disruptions and Author of “2030 The Driverless World”. When not teaching at Stanford CSP, she can be seen chasing self-driving cars in Silicon Valley.