Grants and Contributions:

Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019).

Autonomous Vehicles (AVs) are poised to cause major transformations in transportation, and will enable efficiencies in vehiclex000D
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 withx000D
underground mine workers using Very Low Frequency (VLF) magnetic induction, which can penetrate 100s of meters of rock andx000D
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 byx000D
accurate modelling and calibration to obtain an accurate position estimates. To do this calibration, VitalAlert has developed ax000D
Finite-Difference Time-Domain (FDTD) software to estimate the propagation of the VLF signal. While this method can providex000D
acceptable results, it has long execution times (hours to days for a 50m x 50m x 20m parking garage). It is important to improvex000D
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