Simultaneous ego-vehicle state estimation and vehicle trajectory tracking using a multistage high gain observer

Accurate vehicle tracking is an important requirement on autonomous and semi-autonomous vehicles for safe operation. This paper presents a multi-stage high-gain observer formulation for vehicle tracking using various motion models. Vehicle tracking has previously relied on inaccurate relative motion models due to which tracking errors are obtained, especially during turning maneuvers of the ego vehicle. In this paper, the limitations of the previous motion models are delineated along with the development of new motion models. An important aspect of the new models is that they inherently need ego-vehicle state estimates to track the other vehicles on the road. The previously developed relative models neglect the motion of the ego vehicle. The first newly developed model is an inertial motion model in which the ego state estimates are accounted for in the measurement equation. The second model is a new relative motion model in which ego states are accounted in the process dynamics. Furthermore, in this paper the new motion models are transformed into companion form, and a high-gain observer formulation is then provided for each model along with ego state estimation. Such a multi-stage high gain observer performs simultaneous ego state estimation and vehicle tracking for both inertial and relative motion models. The performance of this multi-stage high-gain observer is validated using experimental data from a full-scale Chrysler Pacifica autonomous vehicle at the University of Minnesota. The experimental results clearly demonstrate superior performance of the newly developed motion models compared to the traditional relative motion model especially in terms of velocity estimation during turning motion of the ego vehicle. The velocity estimation is shown to improve by more than 30%.