Autonomous Car Fuzzy Control Modeled by Iterative Genetic Algorithms

Enrique Onieva (1) , Javier Alonso (1) , Joshué Pérez Rastelli (1) , Vicente Milanés (1) , Teresa de Pedro (1)

1 CAR - Centre for Automation and Robotics

The techniques of Soft Computing are recognized as having a strong learning and cognition capability as well as good tolerance to uncertainty and imprecision. These properties allow them to be applied successfully to Intelligent Transportation Systems (ITS), a broad range of diverse technologies that designed to answer many transportation problems. The unmanned control of the steering wheel is one of the most important challenges faced by researchers in this area. This paper presents a method of automatically adjusting a fuzzy controller to manage the steering wheel of a mass-produced vehicle. Information about the state of the car while a human driver is handling it is captured and used to search, via genetic algorithms, for the best fit of an appropriate fuzzy controller. Evaluation of the fuzzy controller will take into account its adjustment to the human driver's actions and the absence of abrupt changes in its control surface, so that not only is the route tracking good, but the drive is smooth and comfortable for the vehicle's occupants.

Domaines: Intelligence artificielle [cs.AI]

Enrique Onieva, Javier Alonso, Joshué Pérez Rastelli, Vicente Milanés, Teresa de Pedro. Autonomous Car Fuzzy Control Modeled by Iterative Genetic Algorithms. . IEEE International Conference on Fuzzy Systems, Aug 2009, Jeju, South Korea. ⟨hal-00738079⟩