The need of a self for self-driving cars a theoretical model applying homeostasis to self driving

TL;DR

This paper proposes a homeostatic architecture for self-driving cars that monitors internal states and environmental impact to enhance autonomy and safety, using virtual training and blockchain-based shared learning.

Contribution

It introduces a novel homeostatic model for self-driving cars that integrates internal state monitoring, environmental impact assessment, and blockchain communication for improved autonomy.

Findings

Homeostatic architecture effectively monitors internal vehicle states.

Blockchain enables shared learning among self-driving cars.

Virtual environments accelerate training processes.

Abstract

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.