Reference number : GC-2017-Q3-00442, amendment number : , agreement type : G, recipient legal name : Adler, Andy (Carleton University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Autonomous Vehicles (AVs) are poised to cause major transformations in transportation, and will enable efficiencies in vehicle_x000D_
design, energy consumption, vehicle ownership, and road utilization (as well as large business opportunities)._x000D_
A key challenge for developing fully autonomous transport solutions is navigation through locations that are currently "blind spots"_x000D_
for GPS-based positioning systems, such parking garages and underground roads._x000D_
Our corporate partner (VitalAlert) has technology which could address this issue. Their current product allows communication with_x000D_
underground mine workers using Very Low Frequency (VLF) magnetic induction, which can penetrate 100s of meters of rock and_x000D_
soil. VitalAlert is pursuing using this technology for AVs with large automotive companies._x000D_
One key challenge is that the VLF signals are attenuated when passing through concrete, but this problem can be solved by_x000D_
accurate modelling and calibration to obtain an accurate position estimates. To do this calibration, VitalAlert has developed a_x000D_
Finite-Difference Time-Domain (FDTD) software to estimate the propagation of the VLF signal. While this method can provide_x000D_
acceptable results, it has long execution times (hours to days for a 50m x 50m x 20m parking garage). It is important to improve_x000D_
this computation time, as it limits the commercial potential of the product._x000D_
In this project, we will develop an Finite-Element Frequency Domain (FEMFD) modelling technique to allow faster (i.e. minutes)_x000D_
accurate modelling. This, in turn, will improve the commercial potential of the VLF positioning system. Our approach is based on 10 years of experience in this technology._x000D_, program purpose french : Autonomous Vehicles (AVs) are poised to cause major transformations in transportation, and will enable efficiencies in vehicle_x000D_
design, energy consumption, vehicle ownership, and road utilization (as well as large business opportunities)._x000D_
A key challenge for developing fully autonomous transport solutions is navigation through locations that are currently "blind spots"_x000D_
for GPS-based positioning systems, such parking garages and underground roads._x000D_
Our corporate partner (VitalAlert) has technology which could address this issue. Their current product allows communication with_x000D_
underground mine workers using Very Low Frequency (VLF) magnetic induction, which can penetrate 100s of meters of rock and_x000D_
soil. VitalAlert is pursuing using this technology for AVs with large automotive companies._x000D_
One key challenge is that the VLF signals are attenuated when passing through concrete, but this problem can be solved by_x000D_
accurate modelling and calibration to obtain an accurate position estimates. To do this calibration, VitalAlert has developed a_x000D_
Finite-Difference Time-Domain (FDTD) software to estimate the propagation of the VLF signal. While this method can provide_x000D_
acceptable results, it has long execution times (hours to days for a 50m x 50m x 20m parking garage). It is important to improve_x000D_
this computation time, as it limits the commercial potential of the product._x000D_
In this project, we will develop an Finite-Element Frequency Domain (FEMFD) modelling technique to allow faster (i.e. minutes)_x000D_
accurate modelling. This, in turn, will improve the commercial potential of the VLF positioning system. Our approach is based on 10 years of experience in this technology._x000D_, agreement title english : Modelling to Improve Positioning Accuracy in Urban Environments for Autonomous Vehicles, agreement title french : Modelling to Improve Positioning Accuracy in Urban Environments for Autonomous Vehicles, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-12-13, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada