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Retro-fitting Vehicles for Semi-Autonomous Safety

Mystic Pants wins finalist spot in IoT Awards.

The intriguingly named Mystic Pants, an IoT software development company based in Windsor, Victoria, has developed and deployed the equally intriguingly named Mystic Motion, a generic control system for retro-fitting for vehicle fleets that allows high-precision vehicle location and control.

Mystic Motion uses a combination of differential GPS and multiple wireless technologies to implement safety control systems in environments where multiple vehicles are operating together in close proximity and at high speeds.

Mystic Pants is aiming the system largely at the mining and agriculture sectors, although its first implementation is actually on the Wild Buggy ride at the Wet’n’Wild theme park on the Gold Coast, where it is used to keep buggies on a dirt track — which can be driven by children as young as five — a safe distance from each other.

The system, says Mystic Pants, “enables high-precision GEO fencing and vehicle performance information gathering in real time, allowing the system to automatically execute safety overrides and trigger emergency stops in dangerous situations.”

All system performance data is visible on a screen that show the location and performance information of each vehicle.

Mystic Pants says the system has been implemented using low-cost edge computing processes and off the shelf vehicle electronic control units.

This means the complete units can be produced at low cost and large scale on a single board, which Mystic Pants says, promises to dramatically lower the cost of fitting precision GPS and control equipment for mining and agriculture applications.

Mystic Pants says its system is an order of magnitude lower cost that existing systems that are attempting to achieve the same results.

Mystic Pants’ Mystic Motion semi-autonomous vehicle control system is a finalist in the Best Primary Industry Project category in the 2018 Australian IoT Awards being held at the IoT Festival on 4 June in Melbourne.

Article first published at IoTHub

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