The ability for autonomous vehicles to accurately and precisely determine their location is a problem that is crucial to the advancement of that technology. The Ackermann Steering Model can use the wheel encoders and the steering angle of the front wheels to provide another data point that can be combined with accelerometer and GPS data. The goal of introducing more data sources is to help overcome the limitations of each individual data source. An extended kalman filter is one method for combining these three data methods.
  For this project we drove Northeastern's autonomous vehicle around Boston to collect real data. We intentionally drove the vehicle amongst tall buildings to provide inaccurate GPS data and drove the vehicle in multiple laps around a rotary to try and provide inaccurate accelerometer and steering angle data. This data collection methodology was done to test the worst case scenarios and see how and if an Extended Kalman Filter with the Ackermann Steering Model can overcome these flaws.