Fig 1.
Region of interest and light segmentation in the image.
Fig 2.
Detailed view of Fig 1(c).
Table 1.
Image values by distance.
Fig 3.
Longitudinal dynamics model of the vehicle.
Table 2.
Vehicle dynamics model parameters.
Fig 4.
Vehicle dynamics model implemented in Simulink.
Fig 5.
Output velocity of the vehicle dynamics model.
Fig 6.
Overall system block diagram.
Fig 7.
Interval type-2 fuzzy system block diagram.
Fig 8.
Interval type-2 membership function for speed.
Fig 9.
Interval type-2 membership function for distance.
Table 3.
Fuzzy rules of interval type-2.
Fig 10.
Output of the vehicle system: a) speed measurement, b) distance measurement.
Fig 11.
Type-1 membership function for speed.
Fig 12.
Type-1 membership function for distance.
Fig 13.
Vehicle system with disturbances.
Fig 14.
Vehicle speed and tracking error of the type-1 fuzzy controller in the presence of disturbances.
Fig 15.
Vehicle speed and tracking error of the interval type-2 fuzzy controller in the presence of disturbances.
Fig 16.
Membership functions for speed and distance in the 41–43 m section.
Fig 17.
Output of the vehicle system for the 41–43 m section: a) speed measurement, b) distance measurement.
Fig 18.
Membership functions for speed and distance in the 47–49 m section.
Fig 19.
Output of the vehicle system for the 47–49 m section: a) speed measurement, b) distance measurement.
Fig 20.
The five layers of the adaptive neuro–fuzzy inference system.
Fig 21.
Membership functions for speed generated by the adaptive neuro–fuzzy inference system.
Fig 22.
Membership functions for distance generated by the adaptive neuro–fuzzy inference system.
Fig 23.
Input and output areas of the adaptive neuro–fuzzy inference system controller.
Table 4.
Fuzzy rules generated by the adaptive neuro–fuzzy inference system.
Fig 24.
Output speed of the vehicle system with an ANFIS controller.
Fig 25.
Distance variation of the vehicle system with an ANFIS controller.