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

Related Content

  • July 16, 2012
    Semi-autonomous hybrid vehicle trials show fuel, emission savings
    The Transport Research Laboratory has unveiled an innovative semi-autonomous vehicle prototype. It offers improves in environmental performance and safety but also displays some shortcomings. Mike Woof reports. The UK's Transport Research Laboratory (TRL) has been working on an innovative project to develop a prototype vehicle intended to reduce fuel consumption. Based on a Ford Escape hybrid model, TRL's Sentience vehicle uses a combination of mobile communications and mapping technologies to reduce fuel c
    Read More
  • February 18, 2013
    Oxford University develops self-driving car
    Oxford University scientists have developed a self-driving car system that can be installed in existing cars and can cope with snow, rain and other weather conditions. Developed by a team led by Professor Paul Newman at Oxford University, the new system has been installed in a Nissan Leaf electric car and tested on private roads around the university. The car will halt for pedestrians, and could take over the tedious parts of driving such as negotiating traffic jams or regular commutes. The car alerts the
    Read More
  • February 23, 2017
    LiDAR sets its sights on future problems
    AAdvances in LiDAR are helping transport authorities improve services and identify potential problem areas, as geospatial technology expert Dr Neil Slatcher explains. The effects of climate change on the transport infrastructure have long been a cause of concern within the transportation sector - and not only on the structures themselves but also on the surrounding areas. This year, those concerns have become reality with landslides, structural collapses and surfacing issues impacting services across the wo
    Read More
  • April 1, 2015
    MIT study combines traffic data for smarter signal timings
    Researchers at Massachusetts Institute of Technology (MIT) have found a method of combining vehicle-level data with less precise, but more comprehensive, city-level data on traffic patterns to produce better information than current systems provide. They claim this reduce delays, improve efficiency, and reduce emissions. The new findings are reported in a pair of papers by assistant professor of civil and environmental engineering Carolina Osorio and alumna Kanchana Nanduri, published in the journals Tra
    Read More

Highlighted Content