Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

Abstract

High-integrity information about the vehicle’s dynamic state, including position and heading (yaw angle), is required in order to implement automated driving functions. In this work, a comparison of three integrity algorithms for the vehicle dynamic state estimation of a research vehicle for an application in automated driving is presented. Requirements for this application are derived from the literature. All implemented integrity algorithms output a protection level for the position and heading solution. In the comparison, four measurement data sets obtained for the vehicle dynamic state estimation, which is based on a Global Navigation Satellite Signal receiver, inertial measurement units and odometry information (wheel speeds and steering angles), are used. The data sets represent four driving scenarios with different environmental conditions, especially regarding the satellite signal reception. All in all, the Kalman Integrated Protection Level demonstrated the best performance out of the three implemented integrity algorithms. Its protection level bounds the position error within the specified integrity risk in all four chosen scenarios. For the heading error, this also holds true, with a slight exception in the very challenging urban scenario.