Abstract
The design of high–performance state estimators for future autonomous vehicles constitutes a challenging task, because of the rising complexity and demand for operational safety. In this application, a vehicle state observer with a focus on the estimation of the quantities position, yaw angle, velocity, and yaw rate, which are necessary for a path following control for an autonomous vehicle, is discussed. The synthesis of the vehicle’s observer model is a trade-off between modelling complexity and performance. To cope with the vehicle still stand situations, the framework provides an automatic event handling functionality. Moreover, by means of an efficient root search algorithm, map-based information on the current road boundaries can be determined. An extended moving horizon state estimation algorithm enables the incorporation of delayed low bandwidth Global Navigation Satellite System (GNSS) measurements—including out of sequence measurements—as well as the possibility to limit the vehicle position change through the knowledge of the road boundaries. Finally, different moving horizon observer configurations are assessed in a comprehensive case study, which are compared to a conventional extended Kalman filter. These rely on real-world experiment data from vehicle testdrive experiments, which show very promising results for the proposed approach.
Highlights
Many demanding mechatronic systems, like the German Aerospace Center’s (DLR’s) ROMO [1], employ state dependent nonlinear optimization-based control (e.g., [2]), which needs an accurate knowledge of the system states.Often, these states cannot be gathered directly through sensors, as an appropriate measurement principle for the searched quantity is not available, or the sensor is expensive and it is desirable to be economized
A novel nonlinear position estimator, relying on a moving horizon estimator approach, which fuses inertial navigation system (INS) and Global Navigation Satellite System (GNSS) sensor data based on a model-based observer framework [2] is proposed
This framework relies on multiphysical prediction model design in Modelica [5], and an automated tool chain to incorporate these by means of the Functional Mockup Interface (FMI) [6] technology, with well proven nonlinear Kalman filter-based algorithms
Summary
Like the German Aerospace Center’s (DLR’s) ROMO (short for ROboMObil—DLR’s robotic electric vehicle) [1], employ state dependent nonlinear optimization-based control (e.g., [2]), which needs an accurate knowledge of the system states. A novel nonlinear position estimator, relying on a moving horizon estimator approach, which fuses inertial navigation system (INS) and Global Navigation Satellite System (GNSS) sensor data based on a model-based observer framework [2] is proposed This framework relies on multiphysical prediction model design in Modelica [5], and an automated tool chain to incorporate these by means of the Functional Mockup Interface (FMI) [6] technology, with well proven nonlinear Kalman filter-based algorithms (cf e.g., [2,7]). Dead reckoning integrated navigation system, by linearizing the optimization problem along the previous state estimation in each iteration Another method to compensate the missing data of the slow sampled measurements in a (linear) MHE formulation with bounded measurement noise is proposed by the authors of [15], by means of the introduction of prediction values in the missing data gaps.
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