Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Teaching machines to see

21.12.2015

New smartphone-based system could accelerate development of driverless cars

Two newly-developed systems for driverless cars can identify a user's location and orientation in places where GPS does not function, and identify the various components of a road scene in real time on a regular camera or smartphone, performing the same job as sensors costing tens of thousands of pounds.


This is an example of SegNet in action: the separate components of the road scene are all labelled in real time.

Credit: Alex Kendall

The separate but complementary systems have been designed by researchers from the University of Cambridge and demonstrations are freely available online. 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.

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. Previous systems using expensive laser or radar based sensors have not been able to reach this level of accuracy while operating in real time.

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.

For the driverless cars currently in development, radar and base sensors are expensive - in fact, they often cost more than the car itself. In contrast with expensive sensors, which recognise objects through a mixture of radar and LIDAR (a remote sensing technology), SegNet learns by example -- it was 'trained' by an industrious group of Cambridge undergraduate students, who manually labelled every pixel in each of 5000 images, with each image taking about 30 minutes to complete. Once the labelling was finished, the researchers then took two days to 'train' the system before it was put into action.

"It's remarkably good at recognising things in an image, because it's had so much practice," said Alex Kendall, a PhD student in the Department of Engineering. "However, there are a million knobs that we can turn to fine-tune the system so that it keeps getting better."

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.

The system is not yet at the point where it can be used to control a car or truck, but it could be used as a warning system, similar to the anti-collision technologies currently available on some passenger cars.

"Vision is our most powerful sense and driverless cars will also need to see," said Professor Roberto Cipolla, who led the research. "But teaching a machine to see is far more difficult than it sounds."

As children, we learn to recognise objects through example -- if we're shown a toy car several times, we learn to recognise both that specific car and other similar cars as the same type of object. But with a machine, it's not as simple as showing it a single car and then having it be able to recognise all different types of cars. Machines today learn under supervision: sometimes through thousands of labelled examples.

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 localisation system designed by Kendall and Cipolla runs on a similar architecture to SegNet, and is able to localise 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.

It has been tested along a kilometre-long stretch of King's Parade in central Cambridge, and it is able to determine both location and orientation within a few metres and a few degrees, which is far more accurate than GPS -- a vital consideration for driverless cars. Users can try out the system for themselves here.

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.

"Work in the field of artificial intelligence and robotics has really taken off in the past few years," said Kendall. "But what's cool about our group is that we've developed technology that uses deep learning to determine where you are and what's around you - this is the first time this has been done using deep learning."

"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," said Cipolla. "It will take time before drivers can fully trust an autonomous car, but the more effective and accurate we can make these technologies, the closer we are to the widespread adoption of driverless cars and other types of autonomous robotics."

The researchers are presenting details of the two technologies at the International Conference on Computer Vision in Santiago, Chile.

Media Contact

Sarah Collins
sarah.collins@admin.cam.ac.uk
44-012-237-65542

 @Cambridge_Uni

http://www.cam.ac.uk 

Sarah Collins | EurekAlert!

Further reports about: GPs autonomous vehicles deep learning orientation vehicles

More articles from Information Technology:

nachricht Putting food-safety detection in the hands of consumers
15.11.2018 | Massachusetts Institute of Technology

nachricht Next stop Morocco: EU partners test innovative space robotics technologies in the Sahara desert
09.11.2018 | Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, DFKI

All articles from Information Technology >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: UNH scientists help provide first-ever views of elusive energy explosion

Researchers at the University of New Hampshire have captured a difficult-to-view singular event involving "magnetic reconnection"--the process by which sparse particles and energy around Earth collide producing a quick but mighty explosion--in the Earth's magnetotail, the magnetic environment that trails behind the planet.

Magnetic reconnection has remained a bit of a mystery to scientists. They know it exists and have documented the effects that the energy explosions can...

Im Focus: A Chip with Blood Vessels

Biochips have been developed at TU Wien (Vienna), on which tissue can be produced and examined. This allows supplying the tissue with different substances in a very controlled way.

Cultivating human cells in the Petri dish is not a big challenge today. Producing artificial tissue, however, permeated by fine blood vessels, is a much more...

Im Focus: A Leap Into Quantum Technology

Faster and secure data communication: This is the goal of a new joint project involving physicists from the University of Würzburg. The German Federal Ministry of Education and Research funds the project with 14.8 million euro.

In our digital world data security and secure communication are becoming more and more important. Quantum communication is a promising approach to achieve...

Im Focus: Research icebreaker Polarstern begins the Antarctic season

What does it look like below the ice shelf of the calved massive iceberg A68?

On Saturday, 10 November 2018, the research icebreaker Polarstern will leave its homeport of Bremerhaven, bound for Cape Town, South Africa.

Im Focus: Penn engineers develop ultrathin, ultralight 'nanocardboard'

When choosing materials to make something, trade-offs need to be made between a host of properties, such as thickness, stiffness and weight. Depending on the application in question, finding just the right balance is the difference between success and failure

Now, a team of Penn Engineers has demonstrated a new material they call "nanocardboard," an ultrathin equivalent of corrugated paper cardboard. A square...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

“3rd Conference on Laser Polishing – LaP 2018” Attracts International Experts and Users

09.11.2018 | Event News

On the brain’s ability to find the right direction

06.11.2018 | Event News

European Space Talks: Weltraumschrott – eine Gefahr für die Gesellschaft?

23.10.2018 | Event News

 
Latest News

Purdue cancer identity technology makes it easier to find a tumor's 'address'

16.11.2018 | Health and Medicine

Good preparation is half the digestion

16.11.2018 | Life Sciences

Microscope measures muscle weakness

16.11.2018 | Life Sciences

VideoLinks
Science & Research
Overview of more VideoLinks >>>