GSSI partners with MIT Lincoln Laboratory to develop LGPR for autonomous vehicles

US-based Geophysical Survey Systems (GSSI), manufacturer of ground penetrating radar (GPR) equipment, has entered into a licensing agreement with Massachusetts Institute of technology (MIT) Lincoln Laboratory to build and sell commercial prototypes of their localised ground penetrating radar (LGPR) system, which helps autonomous vehicles navigate by using subsurface geology. The partnership will make prototype systems available to the self-driving vehicle industry.
September 11, 2017 Read time: 2 mins
US-based Geophysical Survey Systems (GSSI), manufacturer of ground penetrating radar (GPR) equipment, has entered into a licensing agreement with 2024 Massachusetts Institute of technology (MIT) Lincoln Laboratory to build and sell commercial prototypes of their localised ground penetrating radar (LGPR) system, which helps autonomous vehicles navigate by using subsurface geology. The partnership will make prototype systems available to the self-driving vehicle industry.

 
The agreement builds on GSSI’s new engineering initiative, which focuses on using GPR to solve difficult problems that cannot be solved with any other technologies. Led by newly appointed Vice President of Research and Development, David Cist, an expert engineering team is focusing on commercialising the new technology.
 
Engineers at MIT Lincoln Laboratory, who developed LGPR, have demonstrated that features in soil layers, rocks, and road bedding can be used to localize vehicles to centimetre-level accuracy. The LGPR technology has been tested for lane keeping even when snow, fog, or dust obscures above-ground features.
 
The LGPR sensor uses high-frequency radar reflections of underground features to generate a baseline map of a road's subsurface. Whenever an LGPR vehicle drives along a road, the data can be used as a reference map. On subsequent passes the LGPR equipped vehicle compares its current map against the reference map to create an estimate of the vehicle's location. This localisation has been demonstrated to be accurate to within a few centimetres, in real-time and at highway speeds, even at night in snow-storms.

Location Based Systems Detection, Monitoring & Machine Vision Classification & Data Collection

For more information on companies in this article

Related Content

  • V2X: The design challenges
    May 2, 2018
    The connected future throws up a number of enticing possibilities for us all. But, says Houman Zarrinkoub of MathWorks, issues around visualisation, prototyping and model evolution need to be examined carefully. We are all aware of the huge amount of investment going into driverless car technologies. With the likes of Volvo, Tesla and BMW getting in on the act, soon they will be a common sight on our roads. However, for this to occur, the vehicles must be able to connect with each other and ensure driver
    Read More
  • Seoul sensors aid autonomous mobility
    January 18, 2022
    Seoul Robotics' LV5 CTRL TWR product can automate vehicles from around corners
    Read More
  • Toyota, BMW, Allianz to partner with autonomous vehicle startup Nauto
    October 11, 2016
    Autonomous vehicle technology company Nauto has entered into strategic agreements with BMW i Ventures and Toyota Research Institute, as well as with Allianz Ventures, part of financial service provider and insurance company Allianz Group. These companies have invested in Nauto and are working with the company on autonomous vehicle development using the Nauto cloud-based data learning platform. Nauto has developed deep learning capabilities that run both in the cloud and on retrofit devices that can be mo
    Read More
  • Xerox counts on machine vision for high occupancy enforcement
    October 29, 2014
    Machine vision techniques can provide solutions to some of the traffic planners most enduring problems With a high proportion of cars being occupied by the driver alone, one of the easiest, most environmentally friendly and cheapest methods of reducing congestion is to encourage more people to travel in each vehicle. So to persuade people to share rides, high occupancy lanes were devised to prioritise vehicles with (typically) three of more people on board and in some areas these vehicles are exempt from
    Read More

Highlighted Content