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The Self-Driving Cars unit builds on the computing and AI knowledge and skills developed during Sensor Immersion and uses the same micro:bit and MakeCode platforms. Students learn about the phenomenon of the self-driving car, and how artificial intelligence systems power its decision-making processes so that it can perform safe driving behaviors. 

Assume that in one instance a car encounters a piece of trash on the road ahead and in another it encounters a dog as an obstacle: how does it know to stop for the dog but not for the trash bag? Students engage with the decisions that self-driving cars make to keep us safe and get us where we need to go. 

The unit aligns with several AI4K12 learning standards around data collection, training classifiers, neural networks, bias and training data, and human-robot interaction. Students learn about self-driving cars and then program their own cars (using the BitCar shown on the left) to navigate the track and avoid crashing into obstacles. Then students learn about AI features to improve the functionality of their cars, including how to make them fair, unbiased, accurate and reliable in their classification of obstacles.