I’m building a physical cab dashboard to control Train Simulator, from master controller to an AWS sunflower to an air gauge and speedometer, and everything in between. In a previous blog post, I detailed the CAN controller I have been designing to power every instrument and sensor in the system. In my Train Simulator setup, as in a real vehicle, using a CAN bus connecting semi-autonomous controllers provides fault tolerance and distributed computing that makes the system more robust.

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In my ongoing quest to build a physical cab dashboard to control Train Simulator, and important set of components are the gauges that tell me what’s going on with the train. I already showed off the internals of an AWS sunflower, which will become CAN bus-connected in a future blog post, early experiments with a speedometer, and next up is an air pressure gauge or air gauge. In a self-powered train or locomotive, one or more air gauges are vital instruments to tell the engineer (or driver, for my UK readers) information about the air-driven braking system.

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I’m building a physical cab dashboard to control Train Simulator, from master controller to an AWS sunflower to a speedometer, and everything in between. The engineer or hobbyist interested in getting this project ready expediently would probably find their favorite microcontroller, figure out how to interface enough GPIO pins to it (maybe an Arduino Mega?), and connect all their hardware to that microcontroller. Not me. Ever dedicated to over diligently engineering my projects, I decided to build each component of my Train Simulator setup with a controller capable of speaking connecting to a CAN bus.

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Train Simulator has for many years given me an easy, accessible outlet for my love of trains, including operating trains. Its realism leaves something to be desired, and I’m now building my own physical controller for Train Simulator, resembling the dashboard of a Class 80x passenger EMU from the UK. The preceding sections of this blog introduced experimenting with a speedometer and AWS acknowledgment pluinger, experimenting with the starwheel at the heart of a DIY master controller, and delving into what makes a real AWS “sunflower” tick.

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In my first post about the Train Simulator Controller project, I introduced the Automated Warning System (AWS) on most of the UK trains that I drive in Train Simulator. This safety system is designed to warn drivers of restricting (non-green) signals, often because there’s a train ahead, a switch that is aligned against the train, or a speed restriction. The system includes magnets between the rails, coils underneath each train, and in the cab, a warning buzzer/horn (and sometimes a bell), a button to acknowledge AWS warnings, and a visual indicator colloquially called a “sunflower”.

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I have long played Train Simulator, and have wanted something a bit more realistic to control the game. The first part of this log discussed my plans and motivations, and detailed the first steps along the journey: experimenting with connecting a speedometer and AWS acknowledgement plunger to the game. The core of controlling any train is making it go and stop, so naturally that was the next component to which I turned my attention.

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Can AI run on a calculator? Machine learning and computer vision algorithms can certainly be run on a calculator albeit slowly: I ported a convolutional neural network (CNN) to a TI-84 Plus CE, making it capable of using “AI” to identify handwritten digits. As an added challenge, I implemented this in a single three-day train ride, including solving several interesting systems problems and making the code equally useable on a computer.

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For quite a few years now, I have been an avid Train Simulator player, operating virtual trains from the United States, the UK, and beyond. I’ve played Dovetail Games’ Train Simulator 2015 to 2022 and now “Train Simulator Classic”, FIFA-style annual upgrades that have incrementally improved the core game - and I’ve spent a decent amount of money on the much-memed Train Simulator DLC. The game is pretty immersive, but since part of my life also includes running real-life rail vehicles, I naturally want to make it feel even more real.

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