Towards Nucleation of GoA3+ Approval Process

Tagiew, Rustam; Buder, Thomas; Hofmann, Kai; Klotz, Christian; Tilly, Roman

doi:10.1145/3497737.3497742

Cited by 2 publications

(6 citation statements)

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“…1. Rough categorization of system's functions for driverless mainline rail traffic, which in comparison to driverless metros, require additional technological effort [25].…”

Section: Overrun Event Detectionmentioning

confidence: 99%

“…2. Data required for two currently available approaches of safety argumentation for European mainline railway systems [26]. The grey frame denotes the explicit risk assessment with resulting hourly fatality rates and the maximal values of harmonized design goals.…”

Section: Human Collision Prediction and Collision Detection For All R...mentioning

confidence: 99%

“…The distribution of distances, at which human drivers detect objects on railway, has an irregular bell shape [26]. Tab.I shows median distances for human performance at detecting Fig.…”

Section: Requirements For Obstacle Detectionmentioning

confidence: 99%

“…1 R. Tagiew, P. Klasek and R. Tilly are scientific desk officers at the German Centre for Rail Traffic Research at the Federal Railway Authority (DZSF), Dresden, Germany info@dzsf.bund.de 2 M. Köppel, P. Denzler, P. Neumaier, T. Klockau and M. Boekhoff work at DB Netz AG (all Digitale Schiene Deutschland), Berlin, Germany {philipp.neumaier, patrick.denzler, martin.koeppel}@deutschebahn.com 3 K. Schwalbe is project leader at FusionSystems GmbH, Chemnitz, Germany karsten.schwalbe@fusionsystems.de (CV) systems are not required to replace human vision for driveway monitoring [3]. In contrast, open rail systems are subject to environmental interferences, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…It requires a development of a vehicle-side AI system performing a multisensory perception. However, a literature review has shown an order of magnitude lower research activity for rail traffic in comparison to road traffic [25]. It also showed insufficient progress -the current Technology Readiness Level (TRL) for rail traffic is 5 and remains unsurpassed for the last two decades.…”

Section: Introductionmentioning

confidence: 99%

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Onboard Sensor Systems for Automatic Train Operation

Tagiew

Leinhos

Haar

et al. 2022

Communications in Computer and Information Science

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For driverless train operation on mainline railways, several tasks need to be implemented by technical systems. One of the most challenging tasks is to monitor the train's driveway and its surroundings for potential obstacles due to long braking distances. Machine learning algorithms can be used to analyze data from vision sensors such as infrared (IR) and visual (RGB) cameras, lidars, and radars to detect objects. Such algorithms require large amounts of annotated data from objects in the rail environment that may pose potential obstacles, as well as rail-specific objects such as tracks or catenary poles, as training data. However, only very few datasets are publicly available and these available datasets typically involve only a limited number of sensors. Datasets and trained models from other domains, such as automotive, are useful but insufficient for object detection in the railway context. Therefore, this publication presents OSDaR23, a multi-sensor dataset of 21 sequences captured in Hamburg, Germany, in September 2021. The sensor setup consisted of multiple calibrated and synchronized IR/RGB cameras, lidars, a radar, and position and acceleration sensors front-mounted on a railway vehicle. In addition to raw data, the dataset contains 204 091 polyline, polygonal, rectangle and cuboid annotations for 20 different object classes. This dataset can also be used for tasks going beyond collision prediction, which are listed in this paper.

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“…1. Rough categorization of system's functions for driverless mainline rail traffic, which in comparison to driverless metros, require additional technological effort [25].…”

Section: Overrun Event Detectionmentioning

confidence: 99%

Section: Human Collision Prediction and Collision Detection For All R...mentioning

confidence: 99%

“…The distribution of distances, at which human drivers detect objects on railway, has an irregular bell shape [26]. Tab.I shows median distances for human performance at detecting Fig.…”

Section: Requirements For Obstacle Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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