Abstract
An automatic steering control algorithm which resembles the behavior of an efficient human driver is presented. The controller consists of central part which uses the position plus Orientation Preview Acceleration (POPA) driver model and a vehicle built-in controller which resembles the neuromuscular actuation and proprioceptive feedback elements of the human driver. This ensures smooth transition between the automatic and manual steering operating modes. A neural network vehicle model is developed and used to test the efficiency of the control algorithm. A simulation example of a standard lane change maneuver demonstrated the efficiency of the control algorithm.