Modelling to Improve Positioning Accuracy in Urban Environments for Autonomous Vehicles

点击翻译按钮获取中文摘要

Autonomous Vehicles (AVs) are poised to cause major transformations in transportation, and will enable efficiencies in vehicledesign, energy consumption, vehicle ownership, and road utilization (as well as large business opportunities).A key challenge for developing fully autonomous transport solutions is navigation through locations that are currently "blind spots"for GPS-based positioning systems, such parking garages and underground roads.Our corporate partner (VitalAlert) has technology which could address this issue. Their current product allows communication withunderground mine workers using Very Low Frequency (VLF) magnetic induction, which can penetrate 100s of meters of rock andsoil. VitalAlert is pursuing using this technology for AVs with large automotive companies.One key challenge is that the VLF signals are attenuated when passing through concrete, but this problem can be solved byaccurate modelling and calibration to obtain an accurate position estimates. To do this calibration, VitalAlert has developed aFinite-Difference Time-Domain (FDTD) software to estimate the propagation of the VLF signal. While this method can provideacceptable results, it has long execution times (hours to days for a 50m x 50m x 20m parking garage). It is important to improvethis computation time, as it limits the commercial potential of the product.In this project, we will develop an Finite-Element Frequency Domain (FEMFD) modelling technique to allow faster (i.e. minutes)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.