Self-trained automated parking system
Richard J. Oentaryo, Michel Pasquier · 2005
This paper presents part of the research work carried out at the Centre for Computational Intelligence at NTU to develop novel technologies for the routing, navigation, and control of intelligent cars. One objective is to endow the cars with the ability to autonomously drive on various types of roads and realize manoeuvres such as reverse and parallel parking, three-point turns, etc. Our approach is to design a self-training system that makes use of human expertise to automatically derive a working car control system. A new neuro-fuzzy architecture known as the GenSoYager fuzzy neural network has been realized and integrated with our car-driving simulator for training and testing purposes. The GenSoYagerFNN has proven so far superior to other trained networks in detecting parking slots and accomplishing reverse parking manoeuvres. The approach described has also been validated using a microprocessor controlled model car.