| 1. |
- Cabrall, Christopher D., et al.
(författare)
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Validity and reliability of naturalistic driving scene categorization judgments from crowdsourcing
- 2016
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Konferensbidrag (refereegranskat)abstract
- A common challenge with processing naturalistic driving data for many different possible driving research interests or applications is that humans may need to categorize great volumes of recorded visual information until automated algorithms might be trained to do so alone.This study, by means of the online platform CrowdFlower, investigated the potential of crowdsourcing to provide content identification categorizations of driving scene features (e.g., presence of another vehicle, straight road segments, etc.) at greater scale than a single person or a small team of researchers would be capable of. The validity and reliability of CrowdFlower results were examined, both with and without employing a set of randomly embedded controlled questions (Gold Test Questions) intermixed with experimental questions (Work Mode). In total, 200 workers from 46 countries participated in this study, and the collection of data lasted one and a half days.By employing Gold Test Questions, we found significantly more accurate and consistent responses from external workers at both a smaller and larger scale of video segment categorizations for the identification of common driving scene elements (e.g., position and behavior of other vehicles, road and signage characteristics, etc.). In terms of validity and at the small scale, an average accuracy of 91% on paired items was found with the controlled questions compared to 78% without. A difference in bias was found where without Gold Test Questions external workers returned more false positives than false negatives whereas the opposite was found true of the condition with Gold Test Questions. At the large scale (making use of the controlled questions), a random subset of categorizations returned similar levels of accuracy (95%) and a similar pattern of error bias. In terms of reliability and at the small scale, where segments were rated in triplicate redundancy, the percentage of unanimous agreement was found significantly higher when using controlled questions (90%) than without them (65%). Across the small scale of internally validated answers, more than two-thirds of any correct categorization were unanimously returned and 86% or more of any correct categorization was returned by a majority vote. Where it would be infeasible to validate every response for accuracy, similar voting reliability results were found to exist across the responses of the large scale.Overall results support compelling evidence for CrowdFlower as being able to yield valid and reliable crowdsourced categorizations of naturalistic driving scene contents in a short period of time and thus a potentially powerful and as-of-yet under-utilized resource in the toolbox of driving research and driving automation development.
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| 2. |
- Kyriakidis, Miltos, et al.
(författare)
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A human factors perspective on automated driving
- 2019
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Ingår i: Theoretical Issues in Ergonomics Science. - : Taylor & Francis. - 1463-922X .- 1464-536X. ; 20:3, s. 223-249
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Tidskriftsartikel (refereegranskat)abstract
- Automated driving can fundamentally change road transportation and improve quality of life. However, at present, the role of humans in automated vehicles (AVs) is not clearly established. Interviews were conducted in April and May 2015 with 12 expert researchers in the field of human factors (HFs) of automated driving to identify commonalities and distinctive perspectives regarding HF challenges in the development of AVs. The experts indicated that an AV up to SAE Level 4 should inform its driver about the AV's capabilities and operational status, and ensure safety while changing between automated and manual modes. HF research should particularly address interactions between AVs, human drivers and vulnerable road users. Additionally, driver-training programmes may have to be modified to ensure that humans are capable of using AVs. Finally, a reflection on the interviews is provided, showing discordance between the interviewees’ statements – which appear to be in line with a long history of HFs research – and the rapid development of automation technology. We expect our perspective to be instrumental for stakeholders involved in AV development and instructive to other parties.
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| 3. |
- Kyriakidis, Miltos, et al.
(författare)
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The indirect costs assessment of railway incidents and their relationship to human error : The case of Signals Passed at Danger
- 2019
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Ingår i: Journal of Rail Transport Planning & Management. - : Elsevier. - 2210-9706 .- 2210-9714. ; 9, s. 34-45
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Tidskriftsartikel (refereegranskat)abstract
- The majority of railway incidents result neither in passenger nor operators harm, nor they lead to any severe damage on the rolling stock or the infrastructure. Nevertheless, such incidents result in financial loses, broadly known as indirect costs, which are difficult to identify, isolate, evaluate, and quantify. This paper introduces a framework to quantify the indirect costs in railway operations. Furthermore, as degraded human performance remains a major contributor to operational errors and railway incidents, this study explores for associations between the indirect costs and the factors that affect and contribute to degraded human performance. The framework was implemented in the calculation of the Category A1 Signals Passed at Danger (SPADs) indirect costs. Data was obtained from two UK train operators, while the associated human performance was analysed using the Railway-Performance Shaping Factors (R-PSFs) taxonomy. Employing Spearman's rank order correlation and Fisher's exact statistical tests the associations between R-PSFs and indirect costs were reviewed. Results show significant correlations between the R-PSFs and indirect costs, but only if the importance and severity of every individual R-PSFs is considered. We expect our findings to aid the relevant stakeholders on their efforts to make better decisions on improving safety performance of railway operations.
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| 4. |
- Singh, Sarbjeet, et al.
(författare)
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Incorporating Human Reliability Analysis to enhance Maintenance Audits : The Case of Rail Bogie Maintenance
- 2017
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Ingår i: International Journal of Prognostics and Health Management. - USA : Prognostics and Health Management (PHM) Society. - 2153-2648. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Human errors occurring during railway maintenance activities can significantly reduce the availability of equipment. Identification of potential human errors, their causes and prediction of the associated probabilities are important stages in order to manage such errors. This paper investigates the probability of human error during the maintenance of railway bogies. A case study examines technicians performing maintenance on the disc brake assembly unit, wheel set, and bogie frame under various error producing conditions in a railway maintenance workshop in Luleå, Sweden. The Human Error Assessment and Reduction Technique (HEART) is employed to determine the probability of human error occurring during each of the maintenance tasks, while fault tree analysis is used to define the potential errors throughout the maintenance process. The probability of a technician committing an error during the maintenance of the disc brake assembly, wheel set, and bogie frame is found to be 0.20, 0.039 and 0.021 respectively, with the human error probability (HEP) for the entire bogie 0.24. Time pressure, ability to detect and perceive problems, over-riding information, the need to make decisions and mismatches between the operator and designer’s model turn out to be major contributors to human error. These findings can help maintenance management personnel to better understand the error producing conditions that may lead to errors and in turn serve as an input to modify policies and guidelines for railway maintenance tasks.
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