The advent of autonomous driving has led researchers toward a whole new technological age where vehicle positioning and localization system form the back bone of an autonomous electric vehicle. However, localization becomes poor as a vehicle enters GPS-denied areas due to multi path errors. Autonomous vehicle, in addition, needs to be localized from time to time and be guided on the right path along its destination. The purpose of this study is to overcome the problem of adopting an alternative method by using the vehicle’s Wheel Speed Sensor (WSS) for localization. WSS as an auxiliary sensor is attached to the vehicle’s wheel to track its position upon considering its travelling speed in a period of time. This is done in such a way that the existence of obscured portion along the guideway will be neglected. The data obtained from WSS are combined with yaw rate from an Inertial Measurement Unit (IMU) through Kinematic Modelling algorithm and then be converted to get the local position coordinates. In order to analyse whether the yaw rate produced by IMU is acceptable or not, comparison with simulation is needed. A Bicycle Model is used to generate simulated yaw rate from the steering angle of the vehicle and Kalman Filter estimates the simulated yaw rate to be close with the raw yaw rate. Therefore, this will clarify that the yaw rate obtained from IMU is acceptable and that true localization path is generated.