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This paper describes an automated path following system using vision sensor. Lateral control law for path following is especially underlined which is developed by using the backstepping control design methodology. To establish the proposed control system, the lateral offset to the reference path, the heading angle of vehicle relative to tangent line to the path, and path curvature are required. Those inputs to the controller have been calculated through Kalman filter which is frequently adopted for the purpose. The lane mark detection has been achieved in an ECU (Electric Control Unit) platform with vision sensor. The yaw rate and side-slip angle also needed in the controller are estimated by Kalman estimator. To show the performance of the proposed controller under different speeds, experiment has been conducted on a proving ground having straight and curve sections with the curvature of about 260m.

This paper presents a vehicle to road tracking algorithm based on vision sensor by using Extended Kalman Filter (EKF) from which outputs [i.e. lateral offset, heading angle relative to lane, road width, and road curvature, so called, VRTP (Vehicle to Road Tracking Parameter)] might be used as inputs to steering controller of lane keeping assist system or for smart warning decision logic of lane departure warning system among automotive driver assistance systems. The proposed approach makes use of lane marking pixel coordinates on image plane extracted from a kind of lane detection algorithm, together with yaw rate, steering angle and velocity measurements.