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Autonomous Truck Data Analysis

The goal of efficiency drives the mining industry in Western Australia. Principal among this intention is the want to reduce costs without compromising the health and safety of workers. Recently, a leading example of this can be seen in the introduction of autonomous (driverless) trucks into operating mine sites across the Pilbara.

As with any new technology, considerable investment is put behind ensuring that it is the implementation process is undertaken to yield the best possible outcome for the organisation. This usually involves a staged approach to implementation, coupled with detailed performance analysis and a model of continuous improvement.

Talis was engaged by the Client to provide these data analysis services, with a specific spatial focus, for a project which was investigating the performance of autonomous (driverless) dump trucks within an active iron ore mine. The trucks themselves were fitted with dozens of onboard telemetry sensors, measuring every aspect of the vehicle’s operational performance. The analysis of the data, coupled with high-precision GPS data, resulted in a spatial dataset which described numerous aspects of the truck’s behaviour during its operating cycle.

Our study sought to assess the specific driving patterns that the autonomous trucks adopted, including driving paths along haul roads, adherence to safe driving conditions, reversing patterns and behaviours during loading and dumping, and any instances of “queueing” or other non-productive occurrences along the route.

The data was then analysed across the various phases of haulage to determine key performance metrics throughout the cycle and identify any significant differences in behaviour between autonomous trucks and those with drivers. This information then formed the basis of a series of recommendations to adjust certain aspects of the control algorithms used by the vehicles, to improve the overall efficiency, and therefore profitability, of each truck in the fleet.