Intelligent Speed Adaptation Using a Self-Organizing Neuro-Fuzzy Controller

Abstract : The need to increase road safety is a major concern, with millions of road users and pedestrians being killed in traffic accidents each year. The Centre for Computational Intelligence (C2i) at NTU has developed an intelligent driving system based on hybrid fuzzy neural networks, which is able to park autonomously, drive on highways, and take some decisions such as lane changing, car following, and overtaking. This paper presents a new approach to autonomously adapt the speed of a vehicle by learning from a human driver and using anticipation. The architecture of the system is a specific fuzzy neural network realized at C2i: the Generic Self Organizing Fuzzy Neural Network using the Yager inference scheme (GenSoFNN(Yager)). Experiments have been conducted in simulation to test the longitudinal control and the ability of the system to anticipate curves. Results found are very promising.
Complete list of metadatas

https://hal.inria.fr/inria-00171488
Contributor : David Partouche <>
Submitted on : Wednesday, September 12, 2007 - 3:02:02 PM
Last modification on : Friday, April 12, 2019 - 1:30:50 AM

Identifiers

  • HAL Id : inria-00171488, version 1

Collections

LIG | UGA

Citation

David Partouche, Anne Spalanzani, Michel Pasquier. Intelligent Speed Adaptation Using a Self-Organizing Neuro-Fuzzy Controller. IEEE Intelligent Vehicles Symposium, Jun 2007, Istanbul, Turkey. ⟨inria-00171488⟩

Share

Metrics

Record views

197