• Home
  • About Us
  • Contact Us
  • Privacy Policy
  • Special Offers
Business Intelligence Info
  • Business Intelligence
    • BI News and Info
    • Big Data
    • Mobile and Cloud
    • Self-Service BI
  • CRM
    • CRM News and Info
    • InfusionSoft
    • Microsoft Dynamics CRM
    • NetSuite
    • OnContact
    • Salesforce
    • Workbooks
  • Data Mining
    • Pentaho
    • Sisense
    • Tableau
    • TIBCO Spotfire
  • Data Warehousing
    • DWH News and Info
    • IBM DB2
    • Microsoft SQL Server
    • Oracle
    • Teradata
  • Predictive Analytics
    • FICO
    • KNIME
    • Mathematica
    • Matlab
    • Minitab
    • RapidMiner
    • Revolution
    • SAP
    • SAS/SPSS
  • Humor

WaveSense’s ground-penetrating radars could make self-driving cars safer

August 20, 2018   Big Data
 WaveSense’s ground penetrating radars could make self driving cars safer

Radar. Lidar. Cameras. They’re the components that help give autonomous vehicles from Uber, GM’s Cruise Automation, Google spinoff Waymo, and countless others a sense of their surroundings. But WaveSense CEO Tarik Bolat thinks they have a blind spot.

“A massive transformation in transportation and mobility is underway around the world as autonomous systems advance at an unprecedented pace,” Bolat said. “But before broad adoption of self-driving vehicles can occur, navigation safety and reliability must improve significantly, particularly in adverse weather conditions like snow, rain, and fog.”

Enter WaveSense’s ground-penetrating radars (GPR), which leverage a 12-element antenna array to send very high frequency (VHF) electromagnetic pulses up to 10 feet below the ground. Those waves reflect off of underground features like pipes, roots, rocks, and dirt, which helps build a basemap that an onboard computer correlates into a three-dimensional, GPS-tagged subterranean database.

The radars can penetrate rain, fog, dust, and snow, Bolat said, making them ideally suited to inclement weather. And with the help of an algorithm and WaveSense’s underground maps, they’re able to iteratively narrow in on the car’s location as it moves.

They have other uses, too. GPR might one day be used to alert municipalities when roads are in need of maintenance or for underground navigation.

The technology has its origins at the Massachusetts Institute of Technology’s Lincoln Laboratory for the United States Department of Defense, where it was developed for military vehicles deployed in regions with poor or nonexistent road markings. (The first systems underwent testing in Afghanistan in 2013.)

Lincoln Laboratory researchers took a step toward commercialization in 2016, when they demonstrated that a sports utility vehicle equipped with the system could stay within centimeters of its lane on a road freshly coated with snow.

“We’ve achieved 4cm lateral side-to-side accuracy at highway speeds and 6cm lateral accuracy in snowstorms in the middle of the night,” Bolat said. “I don’t believe any of the autonomous vehicle companies can lay claim to this.”

To be clear, WaveSense isn’t advocating the replacement of lidar, radar, or cameras with GPR — Bolat acknowledges that they perform mapping and object detection tasks quite well in most scenarios. Instead, it’s positioning its solution as a complement to existing sensors and as a fallback for when those sensors fail — in heavy rain and fog, for instance, or in sand and dust storms.

“The ground-penetrating radar technology that successfully protected our troops in Afghanistan from dangerous situations will accelerate the commercialization of self-driving vehicles, and will significantly reduce civilian autonomous vehicle fatalities,” said Byron Stanley, WaveSense cofounder and chief technology officer and a lead researcher on Lincoln Laboratory’s GPR program, in a statement. “That mission has driven our work and our passion for a decade and is what propels us forward now.”

Despite GPR’s merits, it won’t necessarily be an easy sell. Getting the sort of localized tracking Bolat describes set up in major cities would be a massive undertaking — each road would need to be scanned individually. And competing solutions exist in the form of monitoring systems like NIRA Dynamics’ Road Surface Information (RSI), which uses machine learning algorithms to aggregate data from vehicle sensors, controllers, and camera feeds as map layers.

But Bolat remains confident about GPR’s efficacy. He’s not the only one — WaveSense has several pilots underway with automotive partners.

“[Our] technology radically improves the safety of self-driving vehicles in all conditions and provides the confidence and reliability our sector must demonstrate in order to earn the public’s trust,” he said.

WaveSense is currently raising a $ 3 million seed round led by Rhapsody Ventures Partners.

Let’s block ads! (Why?)

Big Data – VentureBeat

cars, Could, groundpenetrating, radars, safer, selfdriving, WaveSense’s
  • Recent Posts

    • Someone’s having surgery
    • C’mon hooman
    • Build and Release Pipelines for Azure Resources (Logic Apps and Azure Functions)
    • Database version control: Getting started with Flyway
    • Support CRM with New Dynamics 365 Field Service Mobile App
  • Categories

  • Archives

    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
© 2021 Business Intelligence Info
Power BI Training | G Com Solutions Limited