A Study of Design and Verification of Mini 4WD AI System Based on Automotive Engineering Theory

Abstract

The Mini 4WD AI competition executive committee of Japan Society for Fuzzy Theory and Intelligent Informatics has been organizing competitions for Mini 4WD AI vehicles equipped with microcomputers, sensors, motor drivers, etc., since around 2014. Maintaining a high average speed is crucial for cars, including Mini 4WD, to run fast on the course. Additionally, to further advance Mini 4WD AI research, it is important to present the design principles for vehicles that can achieve the fastest speed using engineering theory, calculation formulas, and numerical values. The purpose of this research is to clarify the theory of the driving performance of Mini 4WD based on automotive engineering theory and to design an AI system using the Profit Sharing method, a sequential experience reinforcement learning algorithm, and study the learning results. The results showed that changing the vehicle speed in stages can produce a learning effect that maintains a high speed. Furthermore, from the perspective of maneuverability, it was shown that improving the ability to re-accelerate after decelerating on a slope is necessary to enhance the average speed.