An autonomous vehicle following fuzzy-neural algorithm - Ramoso, John Paolo A.
Macroscopic and microscopic algorithms are the base approaches for traffic and vehicular studies. However, modern models accept the ambiguity of the human perception and qualify them then quantify. The hypothesis of this thesis is that a hybrid Fuzzy-Neural Framework can mimic local behavioral driving pattern best from determining input parameters and developing the rule-based set, neural classification, and the link that would fust the two models into one. The developed methodology utilized four algorithms: Fuzzy Logic only, Fuzzy-Neural with one Hidden Layer, Fuzzy-Neural with two Hidden Layers, and Neural Network only frameworks. The parameters of these algorithms are then varied to ascertain the best design for car following. The parameters being tested are batch learning, learning rate, and randomized weight range. The algorithms with neural parts are then trained using measured input-output pairing values collected by observing E-Trike drivers in the university. Seven University licensed E-Trike drivers with at least six months of experience with electric vehicles were considered as experts. The data gathered will be used for the training, validation, and test sets of the learning process. A back propagation technique and Dombi T-norm was implemented for the weights optimization. Arbitrary lead vehicle velocity profiles are then used to simulate autonomous following vehicle response. Hardware implementation and verification were also done to observe performance in real road conditions. The fuzzy logic only algorithm was observed to jerk after stopping. The neural network only algorithm came out with multiple failed runs. The fuzzy-neural with one hidden layer had trouble maintaining the desired headway clearance. Six out of the eight tests for the algorithm with two hidden layers came out successful.