Autonomous vehicles, and self-driving cars in particular, are probably one of the most enticing technologies of the 21st century. But despite a great deal of R&D and even more speculation, we have yet to see a self-driving car that can actually operate on real public roads without any human oversight at all. If, however, we remove that “real public roads” constraint, the challenge becomes a lot more approachable. All you need is a few Arduino boards and a webcam, as proven by Austin Blake’s self-driving go-kart.

Blake previously attempted a miniature self-driving Tesla project, which was supposed to drive around a park walking path. That was only a partial success, because the vehicle struggled to put its “behavioral cloning” machine learning algorithms into practice. Blake took those lessons and applied them here, with much better results.

Behavioral cloning, in this context, means that the machine learning algorithm watches what Blake does as he drives around the track, then attempts to replicate that while driving on its own. During training, it looks ahead of the kart through a webcam while monitoring the steering angle. Then, while driving on its own, it looks through the webcam at the track and tries to match the steering angle to what it saw during training.

The machine learning model runs on a laptop, but Blake needed a way for it to control the kart’s steering and throttle. He used three Arduino Nano boards to pull that off. The first just listens to the machine learning model’s serial output for a PWM signal representing the steering angle. It then sends that to the second, which uses that information and the real-time steering angle to control a Cytron motor driver for the steering. The third controls the throttle using an RC car-style circuit.

This proved to work quite well and the go-kart can navigate around a small track in Blake’s workshop. In theory, it could also handle new tracks — so long as they have similar clearly marked edges.

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