Rustam Tagiew

Rustam Tagiew

This paper argues for the introduction of a mainline rail-oriented performance metric for driver-replacing on-board perception systems. Perception at the head of a train is divided into several subfunctions. This article presents a preliminary submetric for the obstacle detection subfunction. To the best of the author's knowledge, no other such proposal for obstacle detection exists. A set of submetrics for the subfunctions should facilitate the comparison of perception systems among each other and guide the measurement of human driver performance. It should also be useful for a standardized prediction of the number of accidents for a given perception system in a given operational design domain. In particular, for the proposal of the obstacle detection submetric, the professional readership is invited to provide their feedback and quantitative information to the author. The analysis results of the feedback will be published separately later.

Rustam Tagiew, Ilkay Wunderlich, Mark Sastuba et al.

Driverless train operation for open tracks on urban guided transport and mainline railways requires, among other things automatic detection of actual and potential obstacles, especially humans, in the danger zone of the train's path. Machine learning algorithms have proven to be powerful state-of-the-art tools for this task. However, these algorithms require large amounts of high-quality annotated data containing human beings in railway-specific environments as training data. Unfortunately, the amount of publicly available datasets is not yet sufficient and is significantly inferior to the datasets in the road domain. Therefore, this paper presents RailGoerl24, an on-board visual light Full HD camera dataset of 12205 frames recorded in a railway test center of TÜV SÜD Rail, in Görlitz, Germany. Its main purpose is to support the development of driverless train operation for guided transport. RailGoerl24 also includes a terrestrial LiDAR scan covering parts of the area used to acquire the RGB data. In addition to the raw data, the dataset contains 33556 boxwise annotations in total for the object class 'person'. The faces of recorded actors are not blurred or altered in any other way. RailGoerl24, available at data.fid-move.de/dataset/railgoerl24, can also be used for tasks beyond collision prediction.

Rustam Tagiew, Prasannavenkatesh Balaji

Points 2.1.4(b), 2.4.2(b) and 2.4.3(b) in Annex I of Implementing Regulation (EU) No. 402/2013 allow a simplified approach for the safety approval of computer vision systems for driverless trains, if they have 'similar' functions and interfaces as the replaced human driver. The human driver is not replaced one-to-one by a technical system - only a limited set of cognitive functions are replaced. However, performance in the most challenging function, obstacle detection, is difficult to quantify due to the deficiency of published measurement results. This article summarizes the data published so far. This article also goes a long way to remedy this situation by providing a new public and anonymized dataset of 711 train driver performance measurements from controlled experiments. The measurements are made for different speeds, obstacle sizes, train protection systems and obstacle color contrasts respectively. The measured values are reaction time and distance to the obstacle. The goal of this paper is an unbiased and exhaustive description of the presented dataset for research, standardization and regulation. The dataset with supplementing information and literature is published on https://data.fid-move.de/de/dataset/atosensedata

Rustam Tagiew, Martin Köppel, Karsten Schwalbe et al.

To achieve a driverless train operation on mainline railways, actual and potential obstacles for the train's driveway must be detected automatically by appropriate sensor systems. Machine learning algorithms have proven to be powerful tools for this task during the last years. However, these algorithms require large amounts of high-quality annotated data containing railway-specific objects as training data. Unfortunately, all of the publicly available datasets that tackle this requirement are restricted in some way. Therefore, this paper presents OSDaR23, a multi-sensor dataset of 45 subsequences acquired in Hamburg, Germany, in September 2021, that was created to foster driverless train operation on mainline railways. The sensor setup consists of multiple calibrated and synchronized infrared (IR) and visual (RGB) cameras, lidars, a radar, and position and acceleration sensors mounted on the front of a rail vehicle. In addition to the raw data, the dataset contains 204091 polyline, polygonal, rectangle, and cuboid annotations in total for 20 different object classes. It is the first publicly available multi-sensor dataset annotated with a variety of object classes that are relevant for the railway context. OSDaR23, available at data.fid-move.de/dataset/osdar23, can also be used for tasks beyond collision prediction, which are listed in this paper.

Rustam Tagiew

The target of this paper is to present an industry-ready prototype software for general game playing. This software can also be used as the central element for experimental economics research, interfacing of game-theoretic libraries, AI-driven software testing, algorithmic trade, human behavior mining and simulation of (strategic) interactions. The software is based on a domain-specific language for electronic business to business negotiations -- SIDL3.0. The paper also contains many examples to prove the power of this language.

Rustam Tagiew

The number of scientific papers grows exponentially in many disciplines. The share of online available papers grows as well. At the same time, the period of time for a paper to loose at chance to be cited anymore shortens. The decay of the citing rate shows similarity to ultradiffusional processes as for other online contents in social networks. The distribution of papers per author shows similarity to the distribution of posts per user in social networks. The rate of uncited papers for online available papers grows while some papers 'go viral' in terms of being cited. Summarized, the practice of scientific publishing moves towards the domain of social networks. The goal of this project is to create a text engineering tool, which can semi-automatically categorize a paper according to its type of contribution and extract relationships between them into an ontological database. Semi-automatic categorization means that the mistakes made by automatic pre-categorization and relationship-extraction will be corrected through a wikipedia-like front-end by volunteers from general public. This tool should not only help researchers and the general public to find relevant supplementary material and peers faster, but also provide more information for research funding agencies.

Rustam Tagiew, Dmitry I. Ignatov

This paper presents an analysis of data from a gift-exchange-game experiment. The experiment was described in `The Impact of Social Comparisons on Reciprocity' by Gächter et al. 2012. Since this paper uses state-of-art data science techniques, the results provide a different point of view on the problem. As already shown in relevant literature from experimental economics, human decisions deviate from rational payoff maximization. The average gift rate was $31$%. Gift rate was under no conditions zero. Further, we derive some special findings and calculate their significance.