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This paper illustrates a method to design dynamic neural network controllers to allow a robot to autonomously navigate roads based on color perception. The neuro-controller moves the robot by setting the speed of the wheels and adjusts the robot visual systems by setting the value of three parameters that determine how much of the red, green and blue components of the RGB camera images contribute in generating the network input vector. Results show that the best evolved controller can successfully drive a real robot in environments with color characteristics never encountered during evolution. Moreover, we show that the dynamic color perception abilities are based on complex patterns of activation of the three color parameters. These patterns are generated by evolved neural mechanisms that successfully adapt the robot perceptual system to the color characteristics of the different visual scenes.