This paper presents a lane information based vehicle localization algorithm for application to automated driving on complex urban roads. Vehicle position estimation of centimeter-level with low-priced commercial sensor setup is one of the emphasis in automated driving. The fusion approach of localization algorithm utilizes LiDAR, Around View Monitoring (AVM) camera and vehicle chassis sensor. The proposed localization algorithm consists of several sections: a lane detection and fusion, a map-matching based position correction, an extended Kalman filter (EKF) based localization filter and validation gate. A lane information is detected and combined from LiDAR reflectivity signals and AVM image around the vehicle. This integrated lane information is possible to correct the ego vehicle position by the iterative closest point (ICP) algorithm which calculates the rigid transformation between the digital lane map and lanes obtained by LiDAR and AVM. The corrected vehicle position by transformation is fused with the information of vehicle chassis sensors based on EKF. The performance of proposed localization is verified through vehicle experiments on a driver’s license test course in Korea.