Rfpro develops platform to test AVs in simulated environment

UK-based Rfpro has launched a commercially available platform to train and develop autonomous vehicles (AVs) in simulation. The innovation is said to reduce the costs and time involved in developing these vehicles as well as provide a safe testing environment. The solution intends to replicate the real word to allow the various sensors of AVs to react naturally. In addition, Rfpro is producing a library of real roads created through precise scanning technology, to help form the basis of the simulation.
April 5, 2018 Read time: 2 mins

UK-based Rfpro has launched a commercially available platform to train and develop autonomous vehicles (AVs) in simulation. The innovation is said to reduce the costs and time involved in developing these vehicles as well as provide a safe testing environment.

The solution intends to replicate the real word to allow the various sensors of AVs to react naturally. In addition, Rfpro is producing a library of real roads created through precise scanning technology, to help form the basis of the simulation. The digital product is said to enable users to control all variables such as traffic, pedestrians, weather and location.

Chris Hoyle, Rfpro’s technical director said: “By using multiple computers 24/7, manufacturers can undertake millions of miles of testing every month using our platform. Humans can also be introduced into the simulation, controlling surrounding cars or pedestrians, so we can assess an autonomous vehicle’s decision making and also the interaction between the vehicle and the driver, but most importantly it is carried out in a safe environment.”

Urban Traffic Control

Related Content

  • US university launches program to study safe integration of semi-autonomous trucks
    May 26, 2017
    The Western Transportation Institute (WTI) at Montana State University is launching a program to study how to safely integrate driverless technology into the US trucking fleet. Similar to the driverless cars being developed by Google and others, self-driving trucks would use sophisticated computers and GPS technology to navigate roadways. Within a decade, the technology is likely to be applied in semi-autonomous truck convoys, or ‘platoons’, in which trucks equipped with self-driving technology would be pro
    Read More
  • More openness - the simple answer to transport's data issues
    October 22, 2018
    Public transit agencies create a lot of data – but using it constructively to solve transportation issues has been a problem. Ben Winokur and Luke Segars think they have the answer: greater openness. Today, more people are connected through smartphones than ever before - and they’re using them for more than texting and calling. People are searching for jobs on their devices, dating, shopping and even managing their finances. But Forbes reports that only a select few companies leverage all the technology at
    Read More
  • Digital Light Processing transforms travel information
    July 19, 2012
    David Crawford investigates the potential of new projection technology. Fifty years on from its invention of the microchip, US company Texas Instruments (TI) has compressed the technology into a surface area of just 4.3mm. As such, it forms the heart of a new Pico Digital Light Processing (DLP) system that is set to transform travel information delivery for millions of users on the move - by making it projectable.
    Read More
  • Debating the future development of ANPR
    July 31, 2012
    What future is there for automatic number plate recognition? Will it be supplanted by electronic vehicle identification, or will continuing development maintain the technology's relevance? In recent years, digitisation and IP-based communication networks have allowed Automatic Number Plate Recognition (ANPR) to achieve ever-greater utility and a commensurate increase in deployments. But where does the technology go next - indeed, does it have a future in the face of the increasing use of, for instance, Dedi
    Read More

Highlighted Content