  This paper explores the concept of creating a "self" for self-driving cars
through a homeostatic architecture designed to enhance their autonomy, safety,
and efficiency. The proposed system integrates inward focused sensors to
monitor the car's internal state, such as the condition of its metal bodywork,
wheels, engine, and battery, establishing a baseline homeostatic state
representing optimal functionality. Outward facing sensors, like cameras and
LIDAR, are then interpreted via their impact on the car's homeostatic state by
quantifying deviations from homeostasis. This contrasts with the approach of
trying to make cars "see" reality in a similar way to humans and identify
elements in their reality in the same way humans. Virtual environments would be
leveraged to accelerate training. Additionally, cars are programmed to
communicate and share experiences via blockchain technology, learning from each
other's mistakes while maintaining individualized training models. A dedicated
language for self-driving cars is proposed to enable nuanced interpretation and
response to environmental data. This architecture allows self-driving cars to
dynamically adjust their behavior based on internal and external feedback,
promoting cooperation and continuous improvement. The study concludes by
discussing the broader implications for AI development, potential real-world
applications, and future research directions.
