Vehicle Localization Using Wheel Speed Sensor (WSS) and Inertial Measurement Unit (IMU)

W.M.H. Wan Azree, M.A. Abdul Rahman, H. Zamzuri


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.


Autonomous vehicle; vehicle localization; global positioning system; wheel speed sensor; inertial measurement unit; bicycle model; Kinematic modelling; Kalman filter

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References (2014). ECU (engine control unit) cars, ECM, parts, functioning. Retrieved from

Axelrad, P., Comp, C.J., & Macdoran, P.F. (1996). SNR-based multipath error correction for GPS differential phase. IEEE Transactions on Aerospace and Electronic Systems, 32(2), 650-660.

Belta, C., & Kumar, V. (2001). Motion generation for formation of robots: A geometric approach. Proceedings of 2001 ICRA. IEEE International Conference on Robotics and Automation, Seoul, South Korea.

DEWESoft (n.d.). MINITAURs – Compact all-in-one DAQ instrument. Trbovlje, Slovenia: DEWESoft d.o.o. Retrieved from

Kuebler Technology News (2016). Incremental encoders feature robust design for use outdoors. Charlotte, NC: Kuebler Group. Retrieved from

Marino, R., Scalzi, S., & Netto, M. (2012). Integrated driver and active steering control for vision-based lane keeping. European Journal of Control, 18(5), 473-484. (2015). 6 ways you could cause a traffic jam without even trying. Selangor, Malaysia: REV Social Malaysia Sdn. Bhd. Retrieved from

Taheri, S. (1990). An investigation and design of slip control braking systems integrated with four wheel steering (PhD thesis). Clemson University, Clemson, South Carolina, US.

Tash, A. (2016). Gridlock comes to Kuala Lumpur. Retrieved from

The World Bank (2015). Annual report 2015. Washington, DC: The World Bank Group. Retrieved from

Welch, G., & Bishop, G. (2006). An introduction to the Kalman filter. Department of Computer Science, University of North Carolina.

Xsens (n.d.). MTi 100-series. Enschede, Netherlands: Xsens Technologies B.V. Retrieved from


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Publisher: Society of Automotive Engineers Malaysia.
eISSN: 2550-2239
ISSN: 2600-8092