Dubai, already home to the world's tallest building, is looking to get ahead by getting out of the driver's seat.
Dubai's ruler, Sheikh Mohammed bin Rashid Al Maktoum, announced plans Monday to have 25% of all car trips in this city-state in the United Arab Emirates to be driverless by 2030.
In a statement carried by the state-run WAM news agency, Sheikh Mohammed said it would cut down on costs and traffic crashes. He said the project would be a joint venture by Dubai's Roads and Transport Authority and the Dubai Future Foundation.
New technology aims to make driverless cars better
Researchers have developed two new smartphone-based systems that can accelerate development of driverless cars by identifying a user's location and orientation in places where GPS does not function.
These can also identify the various components of a road scene in real time on a regular camera or smartphone, performing the same job as sensors costing millions.
Although the systems cannot currently control a driverless car, the ability to make a machine "see" and accurately identify where it is and what it's looking at is a vital part of developing autonomous vehicles and robotics.
"Vision is our most powerful sense and driverless cars will also need to see but teaching a machine to see is far more difficult than it sounds," said professor Roberto Cipolla from University of Cambridge who led the research.
The first system, called SegNet, can take an image of a street scene it hasn't seen before and classify it, sorting objects into 12 different categories -- such as roads, street signs, pedestrians, buildings and cyclists -- in real time.
It can deal with light, shadow and night-time environments, and currently labels more than 90% of pixels correctly.
"Users can visit the SegNet website and upload an image or search for any city or town in the world, and the system will label all the components of the road scene. The system has been successfully tested on both city roads and motorways," the authors noted.
"It is remarkably good at recognizing things in an image because it has had so much practice," added Alex Kendall, PhD student.
SegNet was primarily trained in highway and urban environments, so it still has some learning to do for rural, snowy or desert environments -- although it has performed well in initial tests for these environments.
There are three key technological questions that must be answered to design autonomous vehicles: where am I, what's around me and what do I do next.
SegNet addresses the second question while a separate but complementary system answers the first by using images to determine both precise location and orientation.
SegNet was primarily trained in highway and urban environments, so it still has some learning to do for rural, snowy or desert environments -- although it has performed well in initial tests for these environments.
There are three key technological questions that must be answered to design autonomous vehicles: where am I, what's around me and what do I do next.
SegNet addresses the second question while a separate but complementary system answers the first by using images to determine both precise location and orientation.
The second localization system runs on a similar architecture to SegNet and is able to localize a user and determine their orientation from a single colour image in a busy urban scene.
The system is far more accurate than GPS and works in places where GPS does not, such as indoors, in tunnels, or in cities where a reliable GPS signal is not available.
The localisation system uses the geometry of a scene to learn its precise location, and is able to determine, for example, whether it is looking at the east or west side of a building, even if the two sides appear identical.
"In the short term, we're more likely to see this sort of system on a domestic robot -- such as a robotic vacuum cleaner, for instance," Cipolla added.
The researchers presented details of the two technologies at the International Conference on Computer Vision in Santiago, Chile, recently.
The system is far more accurate than GPS and works in places where GPS does not, such as indoors, in tunnels, or in cities where a reliable GPS signal is not available.
The localisation system uses the geometry of a scene to learn its precise location, and is able to determine, for example, whether it is looking at the east or west side of a building, even if the two sides appear identical.
"In the short term, we're more likely to see this sort of system on a domestic robot -- such as a robotic vacuum cleaner, for instance," Cipolla added.
The researchers presented details of the two technologies at the International Conference on Computer Vision in Santiago, Chile, recently.
தென் ஆப்பிரிக்க விளையாட்டுத் துறையில் கருப்பின வீரர்களுக்குப் போதிய இடமளிப்பது தொடர்பாக தென் ஆப்பிரிக்க அரசு அதிரடி நடவடிக்கை எடுத்துள்ளது.
தென் ஆப்பிரிக்க கிரிக்கெட் அணியில் தற்போது 9% கருப்பின வீரர்கள் மட்டுமே விளையாடி வருகிறார்கள். தென் ஆப்பிரிக்க விளையாட்டுத் துறையில் கருப்பின வீரர்களுக்குப் போதிய இடமளிக்கவேண்டும், இந்த விவகாரத்தில் மாற்றங்களை அமல்படுத்தவேண்டும் என்று விளையாட்டுச் சங்கங்களுக்குத் தென் ஆப்பிரிக்க அரசு கட்டளையிட்டது.
ஆனால் இதனை அமல்படுத்தாத ரக்பி, கிரிக்கெட், நெட்பால், தடகள சங்கங்ளுக்கு தென் ஆப்பிரிக்க அரசு தற்போது புதிய கட்டுப்பாடு விதித்துள்ளது. அதன்படி, அந்த விளையாட்டுச் சங்கங்களுக்கு, தென் ஆப்பிரிக்காவில் முக்கியமான விளையாட்டுப் போட்டிகளை நடத்த அனுமதி கிடைக்காது. இந்த விதிமுறை 2023 வரை தொடரும் என்றும் அரசின் பரிந்துரைகளை ஏற்றுக்கொண்டால் மட்டுமே தடை விலக்கப்படும் என்றும் அறிவிக்கப்பட்டுள்ளது.