This paper focuses on the development of a multi-sensor data-fusion algorithm, which plays a vital role in vehicle localization using continuous landmarks in an automated Bus Rapid Transport (BRT) System. The model vehicle is instrumented with odometry-based wheel encoders and Infra Red (IR) distance sensors. Vehicle localization is done using an Extended Kalman Filter (EKF), which fuses data from these sensors. The path error is corrected discretely by using hall-effect sensors and magnets. The system has been modeled and simulated in MATLAB for straight line and curved paths to test the efficacy of the algorithm. Finally, the implementation of the sensor system has been done on a laboratory scale model integrated with the sensors to verify the integrity of the system. Tests were performed to calculate the error in detection of the actual co-ordinates, as well as to test out the control system being developed. © 2016 IEEE.