Thomas Wohlgemuth

Dual-Bayes Localization Filter Extension for Safeguarding in the Case of Uncertain Direction Signals

In order to run a localization filter for parking systems in real time, the directional information must be directly available when a distance measurement of the wheel speed sensor is detected. When the vehicle is launching, the wheel speed sensors may already detect distance measurement in the form of Delta-Wheel-Pulse-Counts (DWPCs) without having defined a rolling direction. This phenomenon is particularly problematic during parking maneuvers, where many small correction strokes are made. If a localization filter is used for positioning, the restrained DWPCs cannot process in real time. Without directional information in the form of a rolling direction signal, the filter has to ignore the DWPCs or artificially stop until a rolling direction signal is present. For this reason, methods for earlier estimation of the rolling direction based on the pattern of the incoming DWPCs and based on the force equilibrium have been presented. Since the new methods still have their weaknesses and a wrong estimation of the rolling direction can occur, an extension of a so-called Dual-Localization filter approach is presented. The Dual-Localization filter uses two localization filters and an intelligent initialization logic that ensures that both filters move in opposite directions at launching. The primary localization filter uses the estimated and the secondary one the opposite direction. As soon as a valid rolling direction signal is present, an initialization logic is used to decide which localization filter has previously moved in the true direction. The localization filter that has moved in the wrong direction is initialized with the states and covariances of the other localization filter. This extension allows for a fast and real-time capability to be achieved, and the accumulated velocity error can be dramatically reduced.

GNSS-shortages-resistant and self-adaptive rear axle kinematic parameter estimator (SA-RAKPE)

This paper investigates the improvements from an intelligent self-adaptive modification to a Global Navigatior Satellite System (GNSS)-Based Rear Axle Kinematic Parametei Estimator (SA-RAKPE) for an automatic-driving-system in a passenger vehicle. The required highly accurate dead-reckoning localization can be achieved by a well-calibrated kinematic odometry model. For this purpose, the presented Extended Kalman filter approach combines a Differential-Velocity system model and a GNSS measurement model. Subsequently the intelligent self-adaptive modifications are introduced to allow the SA-RAKPE to work even under difficult conditions The self-adaptive modifications include a GNSS-Delay-Finder Module that calculates variable delays of the signals used in complex vehicle architectures. The newly developed SA-RAPKE deals with changes in the system and measurement mode accuracies and even works during interruptions caused by GNSS-shortages. To do this, it changes the update equation and fills the interruptions with virtual parameter measurement to avoid estimation inaccuracies from observability loss and even to store the level of learned parameters. After passing the GNSS-shortages, the filter compensates the error in the system model depending on the length of the GNSS-shortage This makes it possible to continue the parameter learning while passing a great number of bad condition passages. This newly developed self-adaptive filter learns the true axle parameter faster than a restartable filter. The results show that despite numerous high-rise zones, tunnels and bridges, outstanding performance and a short learning phase ensue, especially in urban areas.