Almost everybody has experienced artificial intelligence of one level or the other by using everyday things around them. The next big thing everybody is looking forward to is revolution in the automated mobility industry. In 2016, Apple Chief Executive Tim Cook described the challenge of building autonomous vehicles as “the mother of all” AI projects.
While big players like Google, Uber, and Tesla are competing with other each and other prominent companies, investing billions to come up with a commercially successful fleet of driverless cars, AI experts believe that it may take many a year before self-driven vehicles can successfully conquer the unpredictability of traffic.
AI plays the main role, as always
An autonomous car can be defined as a vehicle capable of navigating itself without human help, using various sensors to perceive the surrounding environment accurately. They can make use of a variety of techniques including radar, laser light, GPS, odometry, and computer vision.
Complex algorithms, cameras and LIDAR sensors are made use of to create a digital world that orients the self-driven car on the road and helps identify fellow cyclists, vehicles and pedestrians. It is extremely difficult to design and produce such systems. They must be programmed to cope with an almost limitless number of variables found on roads. The autonomous vehicle industry therefore looks to machine-learning as the basis for autonomous systems. That is because huge amounts of computing power are required to interpret all of the data harvested from a range of sensors and then enact the correct procedures for constantly changing road conditions and traffic situations.
Deep learning and computer vision systems can be ‘trained’ to drive and develop decision-making processes like a human. Humans naturally learn by example and this is exactly what computers are taught to do as well, ‘think like humans’.
What is deep learning? – Deep learning is a method that uses layered machine-learning algorithms to extract structured information from massive data sets. It is a key technology behind driver-less cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. Each self-driven car is programmed to capture data for map generation, deep learning and driving tasks while they move along the traffic.
Autonomous vehicle industry developments
Google launched its self-driving car project in 2009, being one of the first to invest in this stream. Sensing that autonomous vehicle technology can open up a huge market and disrupting the current one, other tech-giants like Intel, IBM and Apple as well as cab hailing companies- Uber and Lyft and car makers have joined the race.
Alphabet’s Waymo, the self-driving technology development company was launched in December 2016. Waymo has been testing its vehicles Arizona for a little more than a year now. Places like California, Michigan, Paris, London, Singapore, Beijing among others regularly witness test-drives by self-driven cars.
The ground reality
While test-drives have become common in these places, the people have not yet adjusted to it. Research conducted by British luxury car maker Land Rover shows that 63% of people mistrust the concept of driverless cars. They are programmed to drive conservatively. While under the right conditions, it can eliminate aspects of human error and unpredictability like speeding, texting, drunken driving, when they move along with human drivers, the same unpredictability can confuse the autonomous cars. This could lead to accidents as well as a general mistrust over the technology. In March 2018, a self-driving Uber Volvo XC90 operating in autonomous mode struck and killed a woman named Elaine Herzberg in Tempe, Arizona. It is clear from regular reporting of accidents that happen during test-drives that autonomous car technology has a long way to go. Even after succeeding to avoid accidents, self-driven cars will have to face more than a decade long transition period, where humans have to accept this technology as well as give up driving.
This blog was written by our Content Writing Intern – Rona Sara George. Click on the name to view her LinkedIn profile.
Author: Xaltius (Rona Sara George)
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