Denis Stein

A Train Localization Algorithm for Train Protection Systems of the Future

This paper describes an algorithm that enables a railway vehicle to determine its position in a track network. The system is based solely on onboard sensors such as a velocity sensor and a Global Navigation Satellite System (GNSS) sensor and does not require trackside infrastructure such as axle counters or balises. The paper derives a probabilistic modeling of the localization task and develops a sensor fusion approach to fuse the inputs of the GNSS sensor and the velocity sensor with the digital track map. We describe how we can treat ambiguities and stochastic uncertainty adequately. Moreover, we introduce the concept of virtual balises that can be used to replace balises on the track and evaluate the approach experimentally. This paper focuses on an accurate modeling of sensor and estimation uncertainties, which is relevant for safety critical applications.

Algorithms and concepts for an onboard train localization system for safety-relevant services

This paper describes a system that enables a railway vehicle to determine its position in a track network accurately. The system does not rely on trackside hardware like balises or axle counters but is based solely on onboard sensors. It is composed out of drift-free velocity estimates of an eddy current sensor, a GNSS sensor, and a geodetic and topological track map. The paper develops an algorithm based on a probabilistic modeling that fuses the data of those sensors and determines position estimates robustly. We describe how we can treat ambiguities and stochastic uncertainty adequately and we introduce the concept of virtual balises that replace in software what is implemented by trackside balises in present train protection systems. The technique of onboard train localization is one important contribution to future train protection systems that are based on onboard sensors rather than trackside infrastructure and that are more flexible and less expensive than todays systems without loss of safety.

Track detection in 3D laser scanning data of railway infrastructure

Novel safety systems are needed to meet the growing demand of railway operation. In this paper we introduce general techniques for the detection of tracks and their components in 3D laser scanning data. These techniques make use of feature based methods, such as support vector machines, as well as model based methods, such as template matching. The focus of this work are robust and precise detectors for infrastructure elements, such as rails, tracks, closure rails, and frogs. These parts can be used for both, track maintenance and train-borne localization. The approach is evaluated experimentally on 3D laser scanning data and compared with a reference system. Furthermore, the approach is generic such that it can be used for data of any suitable laser scanning system.

An Analysis of Different Sensors for Turnout Detection for Train-borne Localization Systems

Safe railway operation requires a reliable localization of trains in the railway network. Hence, this paper aims to improve the accuracy and reliability of train-borne localization systems proposed recently. Most of these approaches are based on a global navigation satellite system (GNSS) and odometers. However, these systems turned out to have severe shortcomings concerning accuracy and availability. The authors believe that the ability to detect turnouts and the branching direction thereon is the most valuable clue for improvement. Knowing the branching direction provides topological information about the train position. Thus, it complements the geographical information of GNSS and the longitudinal position information of odometers in an ideal way. With such a sensor setup a track-selective localization would be possible even if GNSS is unavailable or disturbed. Therefore, this paper compares the individual benefits of different sensor principles for turnout detection such as inertial measurement units (IMUs), cameras, and lidar (light detection and ranging) sensors. As a consequence, the authors focus on lidar sensors. For those the authors define requirements, review the market, and report the results of a case study in a tramway scenario. The authors proved that it is possible to detect rails, turnouts, and platforms. Finally the authors discuss the findings intensively and give an outlook on further research.

Sensors, models and platform for ambient control

The future of ambient intelligence (AmI) brings new challenges in designing adequate control systems able to handle a diversity of sensors, and actuators. The paper analyzes on the example of a personalized climate control the classification of sensor, actuator and control approaches and derives a system architecture for an AmI-based control system.

Control of processes with an integrator and long dead-time: The smith predictor vs. virtual sensor

In this paper, a smith predictor control scheme is compared with a solution on the basis of a simple PI controller with a virtual sensor. Virtual sensor eliminates the technology-related dead-time and makes the control of dead-time processes possible without high development costs. The effectiveness of the proposed scheme will be demonstrated by simulations of real process with an integrator and long dead-time.

Model-based rail detection in mobile laser scanning data

Similar to autonomous vehicles, future train applications require an accurate on-board self-localization for railway vehicles. Therefore, a reliable and real-time capable environment perception is required. In particular, the knowledge of the track taken at a turnout overcomes ambiguities in self-localization. As the most important groundwork for this, the paper introduces a new approach for the detection of rails and tracks solely from 2d lidar measurements. The technique is based on a new feature point method for lidar data, a template matching approach, and a spatial clustering technique to extract rails and tracks from the detected rail elements. The new approach is evaluated on six different datasets taken outdoors at a demanding test ground. It provides reliable and accurate detection results with centimeter accuracy, a recall of about 90 %, and a precision of about 95 %. The approach is able to detect rails even in complex real-world topologies such as at turnouts and even on tracks with more than two rails.

RAIL DETECTION USING LIDAR SENSORS

This article investigates in which way a lidar sensor can be used in a train-borne localization system. The idea is to sense infrastructure elements like rails and turnouts with the lidar sensor and to recognize those objects with a template-matching approach. A requirement analysis for the lidar sensor is presented and a market review based on these requirements is performed. Furthermore, an approach for template matching on lidar scans to recognize infrastructure objects is introduced and its empirical performance is demonstrated based on measurements taken in a light rail environment. The overall goal of the integration of lidar sensors is to fill the sensory gap of existing train localization approaches, which are able to determine the exact, track-selective train position only if highly accurate position measurements from satellite navigation systems are available, which is often not the case. By integrating a lidar sensor, the localization system becomes more diverse, more robust, and can tolerate missing or faulty measurements from the satellite navigation system.

Low power and low cost sensor for train velocity estimation

Nowadays railway vehicle speed sensors suffer from insufficient measurement accuracy. E. g. the Doppler radar is prone to adverse weather conditions while wheel speed sensors are not sufficiently robust against wheel slip and wheel wear. However, since velocity sensors are safety relevant components, it becomes clear that conventional sensors are not able to cover all the requirements for an everyday use. To overcome these deficiencies, we introduce a new train speed sensor based on an electromagnetic measurement principle. It does not require physical contact to the surface. Therefore, it does not suffer from slip or wheel wear. Furthermore, it works reliably under various weather conditions so that it offers high availability. We present the technical basis of the sensor and the algorithm for speed estimation based on the raw sensory data. We show results from tests on a test bench and tests on a railway vehicle which was operated on a test track. We compare the sensor results quantitatively with a high accuracy GNSS receiver and a laser tachymeter.