Skip to main content

Cognitive Technologies to develop autonomous tram in Russia

Cognitive Technologies has joined forces with Russian manufacturer PC Transport Systems to deploy an autonomous tram on the streets of Moscow by 2022. Cognitive says that its simplified system means autonomous trams will appear on public roads much earlier than self-driving cars. The company claims its system will detect vehicle and other trams, traffic lights, pedestrians, tram and bus stops, railway and switches and obstacles. Also, the technology will allow the tram to stop in front of obstacles a
February 14, 2019 Read time: 2 mins

Cognitive Technologies has joined forces with Russian manufacturer PC Transport Systems to deploy an autonomous tram on the streets of Moscow by 2022.

Cognitive says that its simplified system means autonomous trams will appear on public roads much earlier than self-driving cars.
 
The company claims its system will detect vehicle and other trams, traffic lights, pedestrians, tram and bus stops, railway and switches and obstacles. Also, the technology will allow the tram to stop in front of obstacles and maintain a safe distance to the cars ahead, accelerate and stop.

The trams will feature a combination of sensors which include 20 video cameras and up to ten radars to help detect road scene objects at night as well as in rain, fog and snowy conditions.
 
Olga Uskova, president of Cognitive, says the company’s low-level data fusion technology allows the computer vision model to use the combined raw data coming from cameras and radars to provide a better understanding of the road scene.

“Cameras, for example, correctly recognise objects in 80% of cases, additional data from radar raises the detection accuracy to 99% and higher,” Uskova adds.

The trams will use GPS sensors and will use high-precision cartography along its route.

Initially, an intelligent control system will serve as an active driving assistant in dangerous situations. A second stage test will follow in which an operator will remain in the cabin as a backup driver.

During the next two months, autonomous tram tests with the operator in the cabin will take place in closed facilities which will then be followed by a trail in Moscow.

Related Content

  • CCTV brings transit safety into view
    September 15, 2014
    David Crawford looks at camera-based vulnerable road users protection systems.Safe and efficient operation of road-based transit depends on minimising the risks of incidents involving other vehicles or vulnerable road users such as pedestrians, cyclists and passengers boarding or alighting from buses or trams. The extent and quality of the visibility available to drivers is crucial in preventing and avoiding incidents. Conventionally, they have had to rely on fairly basic equipment - essentially the human
  • Vision technology lifts blinkers from tunnel vision
    December 6, 2017
    Sony’s Jerome Avenel looks at how advances in imaging technology are helping improve safety. On the 24th March 1999, a Belgian truck transporting flour and margarine through the 11.6km Mont Blanc tunnel caught alight when a cigarette stub entered the engine induction snorkel, lighting the paper air filter. The fire left over 30 dead and many more injured. At the time, the Mont Blanc tunnel disaster was the world’s worst tunnel fire.
  • Autonomous vehicles, the pros and cons
    November 21, 2013
    Driver interface and human factors could provide the biggest obstacles to autonomous vehicles as Jon Masters discovers.
  • Daimler’s double take sees machine vision move in-vehicle
    December 13, 2013
    Jason Barnes looks at Daimler’s Intelligent Drive programme to consider how machine vision has advanced the state of the art of vision-based in-vehicle systems. Traditionally, radar was the in-vehicle Driver Assistance System (DAS) technology of choice, particularly for applications such as adaptive cruise control and pre-crash warning generation. Although vision-based technology has made greater inroads more recently, it is not a case of ‘one sensor wins’. Radar and vision are complementary and redundancy