Timeline
August 2021 - September 2021

The Problem
Autonomous vehicles are a cutting edge and world changing technology. However, with this new technology comes new and unique challenges. The client noticed the overall performance of their autonomous haul system was not optmized at their mine, so they decided to do something about it.

Mission
To run an analysis on the IoT data from the autonomous trucks to determine the bottle necks in the operation. This analysis data needed to be delivered in a dyanmic, queryable, and widely distributed map visualization in Power BI.

Methodology
IoT data from the autonomous trucks was stored in tables on a PostgreSQL database. Using SQL, the raw data underwent analysis and was saved to materialized views. These materialized views contained metrics and geospatial data that identified bottle necks in the autonomous haul system throughout the mine and the precise positions where they were taking place. The materialized views were then set as the data source for a Custom Power BI Map Integration (another project I’ve worked on in my career). This map displayed the autonomous haul system analysis in a geospatial format through the GIS (geographic information system) map visualization. The data displayed was dynamic and queryable and interacted with other data visualizations in the Power BI dashboard to allow users to drill down on specific metrics and autonomous trucks. These visualizations were widely distributed throughout the company and used to opmitize the autonomous haul system strategy at the mine.

Project Results
The Autonomous Haul System Analysis POC gave the client the ability to see their autonomous haul systems performance in greater detail. With this new analytics data in a GIS map format the client was able to optimize their autonomous haul system strategy in the boardroom and on the ground at the mine.

Tech
This project utilized PostgreSQL and the Custom Power BI Map Integration.