‘How (not) to get hit by a self-driving car’ is a street-based game that challenges anyone to avoid being detected by an AI-powered camera. In the game, players see themselves augmented on a large screen at the end of a playing field. Marking each player is a percentage signifying the level at which the AI sees them as a pedestrian. If this exceeds a certain limit, the player will lose, and so to win, players must figure out how to cleverly dodge the AI and reach the goal without detection.

This game utilises a widely used object detection algorithm known as the Single Shot Detector (SSD), trained on a large-scale dataset. Every player’s win generates vital data that exposes the inability of the system to detect pedestrians and highlights the flaws of these algorithms, which could potentially be used to improve self-driving cars in the future. As a result, players are presented with a final dilemma upon victory: either train the AI or not. They can opt-in their anonymised gameplay footage to improve the AI models, or immediately delete the image. This poses the question of whether people are willing to trade their data for a potentially safer system, or if they would rather remain invisible, despite the implied risks of inaccurate systems in the future.

Through technology like surveillance cameras and self-driving cars, these advanced image recognition systems are being increasingly deployed in the public space, often without the awareness of the citizens they affect. By inviting the public to play, the game aims to become a platform to understand how these systems work, fostering dialogue about the challenges and potential dangers they present.