Self-Trained Automated Parking System R.J. Oentaryo and M. Pasquier Centre for Computational Intelligence, Nanyang Technological University, School of Computer Engineering, Blk N4 #02a-32, Nanyang Avenue, Singapore 639798. Email: pasquier@pmail.ntu.edu.sg Abstract 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. 1. Introduction The dramatic growth in automotive technology during the past century has culminated in today’s society of motorization, the safety of which is more than ever an imperative concern. Because human errors remain the primary cause in most traffic accidents, the development of in-car technologies for monitoring, prevention, and guidance, has become a major research area, with the objective of reducing the burden of the human driver, increasing traffic capacity, and providing both safe and smooth vehicle operations. Our group at the Centre for Computational Intelligence (formerly Intelligent Systems Laboratory) at NTU has long been working on this topic and particularly the realization of autonomous driving systems for road vehicles [2][3][8]. This paper describes our latest system, the Generic Self-Organising Yager Fuzzy Neural Network (GenSoYagerFNN) and its application to autonomously drive a car on a road track and realize manoeuvres such as reverse parking and three-point turns. Our approach consists of designing a self-training system that can make use of human expertise to automatically extract objective rules for controlling the car. The assumption is that driving is a continuous decision-making process that can be articulated as a set of rules relating sensory input to control output. A fuzzy control system is selected to model the intrinsic vagueness of the information available (speed, distance, dynamic environment variables). This fuzzy rule-based system is then implemented atop a neural network architecture, which provides the ability to learn, recall, generalize and adapt from training data. The resulting fuzzy neural network, or neuro-fuzzy system, possesses the merits of both techniques, that is, in the aspect of learning and optimization capabilities as well as connectionist structure, the human reasoning ability and ease of incorporating expert knowledge. At the same time, the shortcomings of each approach are alleviated, namely the design issue for the fuzzy rule-based system (selection of membership functions, fuzzy rules identification) and the network’s black-box nature (opacity of the intermediate layers). Inference in the GenSoYagerFNN is modelled after the Yager reasoning scheme [10], which computes the dissimilarity of the inputs with the rule antecedents to derive the degree of dissimilarity with the rule consequent, thus arriving at the output. The main benefit over the classic Compositional Rule of Inference (CRI) [11], which we used previously [3][4][9], is that when the input matches the antecedent exactly, the resultant output matches the consequent exactly. Intuitively, the Yager inference rule is closer to human reasoning, and it emerges superior to existing techniques. Indeed the GenSoYagerFNN has proven so far superior to other trained networks in detecting parking slots and accomplishing reverse parking manoeuvres. Lastly, it must be noted that, while the work reported is done in simulation, the approach has also been validated recently using a microprocessor controlled model car. 2. GenSoYager Fuzzy Neural Network The proposed GenSoYagerFNN is based on another in- house connectionist structure, namely the Generic Self- Organizing Fuzzy Neural Network (GenSoFNN) [7] [9]. It is able to automatically formulate fuzzy rules from given numerical training data and to maintain a consistent rule set by ensuring that each fuzzy label in the input/output dimensions is uniquely represented by only one cluster (fuzzy set). Each input fuzzy set can still contribute to the antecedent of more than one fuzzy rule. The GenSoYagerFNN has strong noise tolerance