Automation of the rail—removing the human factor?

Stene, Trine Marie

doi:10.1201/9781351174664-244

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…We therefore set an assumption that neglecting these tramway scenes and training only with an in-domain data would improve performance. This assumption is treated in the next scenario (3).…”

Section: Scenarios On Rail Classmentioning

confidence: 99%

“…The concept of developing self-driving trains is not new; indeed, the "Victoria" metro line (inaugurated in 1968 in London-UK) is considered to be the first large-scale automatic rail system [1] [4]. This train is currently classified as GoA2 [3]: the driver is still present in the cab but only deals with closing doors and starting orders, and the train moves automatically to the next stop. As of July 2016, 789 km of automated metro in operation consisting of 53 lines and 822 stations in 36 cities around the world according to the International Union of Public Transport (UITP) [4].…”

Section: Introductionmentioning

confidence: 99%

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Application of Rail Segmentation in the Monitoring of Autonomous Train’s Frontal Environment

Hadded

Mahtani

Ambellouis

et al. 2022

Lecture Notes in Computer Science

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One of the key factors in achieving an autonomous vehicle is understanding and modeling the driving environment. This step requires a considerable amount of data acquired from a wide range of sensors. To bridge the gap between the Roadway and Railway fields in terms of datasets and experimentation, we provide a new dataset called RailSet as the second large dataset after Railsem19, specialized in Rail segmentation. In this paper we present a multiple semantic segmentation using two deep networks UNET and FRNN trained on different data configuration involving RailSet and Railsem19 datasets. We show comparable results and promising performance to be applicable in monitoring autonomous train's ego perspective view.

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Section: Scenarios On Rail Classmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Rail Segmentation in the Monitoring of Autonomous Train’s Frontal Environment

Hadded

Mahtani

Ambellouis

et al. 2022

Lecture Notes in Computer Science

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show abstract

“…There are five Grades of Automation (GOA) for rail services: GOA 0: manual operation with no automatic train protection; GOA 1: manual operation with automatic train protection; GOA 2: semi-automatic train operation; GOA 3: driverless train operation; GOA 4: unattended train operation. Yet, automation has not been fully realized due to the inherent difficulties and risks to handle unexpected situations or incidents (84). Due to rail network complexities and legal requirements, it is not anticipated that full automation with no train driver or attendant is envisaged universally in the near future.…”

Section: Environmental and Resource Implications Of Automated Rail Transportmentioning

confidence: 99%

The Environmental and Resource Dimensions of Automated Transport: A Nexus for Enabling Vehicle Automation to Support Sustainable Urban Mobility

Nikitas

Thomopoulos

Milakis

2021

Annu. Rev. Environ. Resour.

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Automation carries paradigm-shifting potential for urban transport and has critical sustainability dimensions for the future of our cities. This article examines the diverse environmental and energy-related dimensions of automated mobility at the city level by reviewing an emerging and increasingly diversified volume of literature for road, rail, water, and air passenger transport. The multimodal nature of this investigation provides the opportunity for a novel contribution that adds value to the literature in four distinctive ways. It reviews from a sustainability angle the state of the art underpinning the transition to a paradigm of automated mobility, identifies current knowledge gaps highlighting the scarcity of non-technicalresearch outside the autonomous car's realm, articulates future directions for research and policy development, and proposes a conceptual model that contextualizes the automation-connectivity-electrification-sharing-multimodality nexus as the only way forward for vehicle automation to reach its pro-environmental and resource-saving potential.

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Automated and Autonomous Driving in Freight Transport - Opportunities and Limitations

Daduna

2020

Lecture Notes in Computer Science

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Automation of the rail—removing the human factor?

Cited by 5 publications

References 10 publications

Application of Rail Segmentation in the Monitoring of Autonomous Train’s Frontal Environment

Application of Rail Segmentation in the Monitoring of Autonomous Train’s Frontal Environment

The Environmental and Resource Dimensions of Automated Transport: A Nexus for Enabling Vehicle Automation to Support Sustainable Urban Mobility

Automated and Autonomous Driving in Freight Transport - Opportunities and Limitations

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