Skip to main content

UK start-up receives funding for artificial intelligence that could end traffic jams

UK start-up Vivacity Labs, creators of a sensor with in-built machine-learning that can identify individual road users and manage traffic accordingly has secured a total of US$3.8 million (£3 million) in funding, that could pave the way for driverless cars and truly smart cities that can recognise different vehicles and regulate traffic in real-time. The company has secured a US$2.2 million (£1.7 million) project grant from Innovate UK to roll out a city-wide sensor network for the VivaMK project and a str
May 17, 2017 Read time: 2 mins
UK start-up Vivacity Labs, creators of a sensor with in-built machine-learning that can identify individual road users and manage traffic accordingly has secured a total of US$3.8 million (£3 million) in funding, that could pave the way for driverless cars and truly smart cities that can recognise different vehicles and regulate traffic in real-time.

The company has secured a US$2.2 million (£1.7 million) project grant from Innovate UK to roll out a city-wide sensor network for the VivaMK project and a strategic investment of US$2 million (£1.6 million) from Tracsis, Downing Ventures and the London Co-Investment Fund.

The VivaMK project, part of Innovate UK’s Smart Cities initiative, will see Vivacity Labs deploy 2,500 of its sensors across 50 square miles of Milton Keynes, monitoring all major junction points and car parking spaces. This is the first step in creating an intelligent traffic management system that avoids bottlenecks and improves safety by influencing traffic movement as it happens, based on the type of traffic and monitoring the areas where it becomes congested. The first 12 months of the project will involve installation of the sensors and subsequent data gathering (expected to start in September), followed by integration into traffic management systems.

The cameras will also allow future traffic lights to give priority at signalled intersections to cyclists, buses or ambulances. Vehicle dashboards that communicate with traffic lights could also flag the presence of cyclists to lorry drivers. The technology could also improve safety for pedestrians by enabling traffic signals to communicate with driverless cars and inform them if pedestrians are crossing the road.

Vivacity Labs’ systems will also allow Tracsis to create a much more efficient traffic management system by replacing manual image processing with cameras that have built-in AI technology.

Related Content

  • March 14, 2012
    Automatic signal control to prevent emergency vehicle collisions?
    Field trials under way in Arizona promise eradication of accidents between emergency vehicles at intersections – as part of a national focus on ‘intelligent signal’ infrastructure. Collisions between police cars, ambulances and fire crews as they reach intersections at the same time, with equal priority given by all signals set on red, are as serious as they sound absurd. For emergency teams and those in need of their help, the consequences are dire. The solution could come from application of connected veh
  • January 30, 2025
    Funding secured for TRL’s Data Sustains Life project
    Research body will collaborate on collision data to improve road safety
  • October 24, 2016
    Ford trials technology to help drivers ‘ride the green wave’
    Ford is currently trialling technology which aims to reduce time spent waiting at a red light. Green Light Optimal Speed Advisory uses information on traffic light timings from a roadside unit to display to the driver the best speed to travel at to get a green light. The tests are part of the UK Autodrive self-driving and connected car trial, a 16-member, publicly funded US$24 million (£20 million) project which is developing and trialling vehicle-to-vehicle and vehicle to infrastructure technologies tha
  • May 10, 2023
    Austrian Bike2CAV V2X project could mark turning point in cyclist safety
    Research in Salzburg into C-ITS equips bikes with V2X tech to allow detection via ITS-G5