Hansjörg Manz

Implementation of the normative safety case structure for satellite based railway applications

Enabling safe, green and intelligent railway transportation is a major target of transportation research. This is supported by satellite based localisation systems (GNSS) like GPS which already provide large economical and operational benefits by supporting applications in transport like passenger information, energy saving driving or tracking and tracing. The application of satellite based localisation systems for information purposes is a valuated contribution for a more efficient operation of trains. If satellite based localisation can be used as mean of safe localisation in train control systems, higher benefits according a safer and more reliable operation can be expected. To enable the usage in this application field, the certification of the localisation unit according the valid normative background of railways is necessary. Traditionally, a development of a product in railways starts with requirements which are the base for the product development and the following certification process. For the approval of an application of satellite based localisation for safety relevant purposes in railways, an already existing system like GPS shall be used as localisation device and therefore as important data source and needs therefore to be included into the certification process because of no generic certification of GLASS being already available. This paper can be the base for an approval process enabling the certification of satellite based localisation in railways.

Accuracy Evaluation of GNSS for a Precise Vehicle Control

Abstract This paper describes a methodology for accuracy evaluation of Global Positioning System (GPS) data, on the basis of a reference measurement system (RMS). The use of Global Navigation Satellite System (GNSS) for positioning in precise vehicle control needs previous evaluation and validation of the generated position information. The methodology, based on deviation analysis, is described using actual experimental data that allows a characterization of the quality of the positioning system through statistical distribution fittings, also validating the expected sensors responses. A system quality characterization method focused on accuracy is proposed, relating these findings with potential requirements for precise vehicle control systems. Actual results obtained by using the developed method of accuracy evaluation are shown.

Particle Filter technique for position estimation in GNSS-based localisation systems

The usage of filter techniques for position estimation for safety-relevant purposes is an extensive field of research. Depending on the needed application for the Global Navigation Satellite System (GNSS) the state estimation can be achieved by several techniques. State estimation by means of Kalman Filter (KF), as well as Extended Kalman Filter (EKF) and Particle Filter (PF) have been developed and tested. Map-matching algorithms integrate the localisation data provided by GNSS with spatial road network data (also called “digital map”) to identify the correct line (or track) on which a vehicle is traveling and to determine the location of a vehicle within the line (or track). The goal of map-matching techniques is to exploit prior information contained in road or railway networks. However, incorporating digital map information within the conventional KF framework is not easy, because this constraint leads to highly non-Gaussian posterior densities that are difficult to represent accurately using conventional techniques. Since the PF approach presents no restrictions regarding non-linearity of models and noise distribution the velocity and heading measurement errors can be accurately modelled. The most significant advantages of the PF approach for map-matching application are: 1) PF approach provides a natural way for road map information to be incorporated into vehicle position estimation. 2) PF approach is capable of capturing multi-modal distributions. The selected PF-based location estimators presented here is oriented to work within an intelligent GNSS-based localisation system. The PF-based map matching techniques are presented in a mathematical ground and tests are performed, as part of a developed satellite localisation system based on artificial intelligence (AI) tools. In the railway domain the same integration can be performed by means of a “digital trap map” to identify the location within the tracks. A map-matching algorithm can be the key component of the data fusion to improve the performance of localisation systems that support the navigation function of intelligent transport systems (ITS).

Bridging the gap between railway safety and the specification of satellite based localisation systems

In the future all vehicles in public transport will be equipped with tracking systems that use positioning information provided by satellites in order to make efficient use of the infrastructure and to meet the increased quality standards in public transport. The specification of the future standard of Global Navigation Satellite Systems (GNSS) is a task which can only be solved by a tremendous collaborative and communicative effort of experts from all modes of transportation. Effective communication in the specification process calls for mutual understanding and a terminology well-suited for multidisciplinary discourse. This paper introduces a methodical framework for the disambiguation and harmonization of the terminology currently used in the specification phase. The method outlined in this paper will be exemplified with terms currently used in the draft specification. Using this methodical framework the currently existing problems of ambiguity, terminological vagueness, inconsistency as well as domain-specificity can be overcome.

Enabling certification of satellite based localization in railways by combination of redundant sensors and map matching

Satellite based localization systems are currently applied in various domains in transportation for information purposes. For example in railways for passenger information, in automotive for navigation or in aviation to precisely follow flight paths. These applications are mainly non-safety relevant because of a missing certification. In this paper a development strategy towards the certification of safety relevant applications in transportation with the example of railways is shown. For the certification of an application for transportation, several steps of a domain specific process based on the normative background to be analyzed have to be followed. Though this paper focuses on railways, the basic certification process in other domains is similar.

Simulink based prototype for real-time intelligent GNSS-based localisation system

In the frame of the development of artificial intelligent (AI) based validation tool for Global Navigation Satellite Systems (GNSS) localisation systems MATLAB Simulink models for all the developed AI-based methodologies have been created. The combination of Artificial Neural Network (ANN) based validation tools with the Mahalanobis Ellipses Filter (MEF) methodology for accuracy-based data evaluation for quantitative and qualitative analysis of trueness and precision, as well as Particle Filter (PF) techniques for position estimation to aid the reference system result in significant improvements for GNSS-aided localisation system, allowing the construction of a GNSS-dependent reference system for when no independent reference is available. These additional elements are the bases for future intelligent GNSS-based localisation systems with both quantitative and qualitative validation tools. The proposed prototype constructed within System functions (S-Functions) for extension of MATLAB Simulink capabilities presents total interconnectivity in real-time, implemented using commodity microcomputers and providing an interface between the MATLAB Simulink implemented algorithms and the real world. In the presented prototype a Raspberry Pi Model B microcomputer running a modified Debian Linux distribution provides General Purpose and UART ports that enable the implementation of the low-level communication driver for the GNSS receiver. The prototype enables real-time application of the algorithms. The prototype includes a GNSS receiver, with a sampling position rate and constellation data at 1Hz. NMEA format provides the necessary data to be processed with the MATLAB Simulink model of the ANN-based validation tools and the PF-based estimator. All algorithms are executed in a Windows PC and the communication between the microcomputer and the MATLAB Simulink model is achieved via TCP Sockets, with interface S-Functions to handle the communication. These methodologies will enable future safety-relevant applications, such as on-board uncertainty evaluation; advanced driver assistance systems; and GNSS-based vehicle localisation with intelligent maps for track selective.