Vehicle state and tire force estimation: Performance analysis of pre and post sensor additions

This work presents a Virtual Sensor for vehicle planar velocities and tire forces. The Virtual Sensor is based on an Extended Kalman Filter utilizing a vehicle model and onboard sensors. The Virtual Sensor is evaluated at different stages, starting from a model with lower complexity, i.e. a 3 degrees of freedom bicycle model, and evolving to higher complexity, i.e. a 10 degrees of freedom vehicle model with wheel suspension. The estimation results from the different stages are compared qualitatively as well as with experimental data. The evaluation brings forth the gains, limitations and drawbacks when increasing model complexity accompanied by sensor additions. This gives insight when a trade-off is required between accuracy and model complexity, e.g. in Advanced Driver Assistance Systems. The conducted analysis also clarifies performance dependencies on the sensors as well as sensor redundancies, which is of added value, e.g. for robustifying against sensor failures.