Abstract

This is a critique of ESARR4 and its main supporting documents. ESARR4 is the Eurocontrol Safety Regulatory Requirement Number 4 [Eurocontrol safety regulatory requirement ESARR4, Edition 1.0., Eurocontrol, Safety Regulation Commission, Brussels, 2001]: ‘Risk assessment and mitigation in ATM’, ATM standing for Air Traffic Management. It is demonstrated that ESARR4 and its supporting documents are defective. There is a lack of clarity about responsibilities for ATM safety. The claims ESARR4 etc. make for its proposed methodologies are overstated—not supported by sound evidence from real world hazard analysis. Serious negative effects from this defective document include mis-allocation of scarce safety resources and the diversion of attention away from real safety improvements—wasteful of regulators’ and managers’ time. Suggestions are made for repairing these deficiencies. The most important underlying change would be a refocusing on practical safety assessment based on methods that have already demonstrated their merits.

Abstract

The prime goal of the Air Traffic Management (ATM) system is to control accident risk. Some key questions are posed, including: What do design safety targets really mean and imply for risk modelling? In what circumstances can future accident risk really be modelled with sufficient precision? If risk cannot be estimated with precision, then how is safety to be assured with traffic growth and operational/technical changes? This paper endeavours to answer these questions by an analysis of the nature of accidents, causal factors and practical collision risk modelling. The main theme is how best to combine sound safety evidence and real world hazard analysis in a coherent and systematic framework.

Abstract

This paper describes the conceptual design and validation of an air traffic management (ATM) concept and the role the safety and human factors played in this design and validation process. The free flight (FF) concept is characterised by being a direct route concept where the pilots, instead of the air traffic controller, are responsible for the separation assurance. Moving this task to the cockpit has consequences for the man machine interface in the cockpit, which needs to be modified to accommodate this new task (micro level design). On top of that, a set of rules and procedures are required to ensure an efficient and safe traffic flow (macro level design). Both the micro and macro aspect of this design are intertwined and require an accurate tuning to arrive at an overall acceptable solution. Both micro-level (flight simulator) experiments and macro-level (traffic simulations) experiments have been conducted to investigate the feasibility of this concept after optimising the initial conceptual design.

Abstract

The consequence assessment of a major accident involving a release of toxic gas by a pipeline is usually performed through the evaluation of the associated hazard area, which is an area where the concentration of the toxic substance exceeds a fi xed threshold level and induces harmful effects in people and the environment; its extension represents a signifi cant source of information required for the development of both safety and security strategies associated with dangerous pipelines. Since the threshold level adopted in the calculation strongly affects the extent of this hazard area, the purpose of this paper is to analyse such infl uence and the potential implications on the decisional process concerning prevention, preparedness and response actions in the case of major accidents. The paper describes the methodological approach adopted for this purpose, as well as the main results obtained using the threshold levels most commonly applied in the industrialized countries. Although the estimate of the hazard areas involves a high level of uncertainty, this study aims at supporting the development of pipeline safety and security strategies, thus increasing the overall safety level in this vital sector.

Abstract

In this paper, we present a deep neural network based real-time integrated framework to detect objects, lane markings, and drivable space using a monocular camera for advanced driver assistance systems. The object detection framework detects and tracks objects on the road such as cars, trucks, pedestrians, bicycles, motorcycles, and traffic signs. The lane detection framework identifies the different lane markings on the road and also distinguishes between the ego lane and adjacent lane boundaries. The free space detection framework estimates the drivable space in front of the vehicle. In our integrated framework, we propose a pipeline combining the three deep neural networks into a single framework, for object detection, lane detection, and free space detection simultaneously. The integrated framework is implemented in C++ and runs real-time on the Nvidia's Drive PX 2 platform.

Abstract

fault classification method is proposed which has been applied to an electric vehicle. Potential faults in the different subsystems that can affect the vehicle directional stability were collected in a failure mode and effect analysis. Similar driveline faults were grouped together if they resembled each other with respect to their influence on the vehicle dynamic behaviour. The faults were physically modelled in a simulation environment before they were induced in a detailed vehicle model under normal driving conditions. A special focus was placed on faults in the driveline of electric vehicles employing in-wheel motors of the permanent magnet type. Several failures caused by mechanical and other faults were analysed as well. The fault classification method consists of a controllability ranking developed according to the functional safety standard ISO 26262. The controllability of a fault was determined with three parameters covering the influence of the longitudinal, lateral and yaw motion of the vehicle. The simulation results were analysed and the faults were classified according to their controllability using the proposed method. It was shown that the controllability decreased specifically with increasing lateral acceleration and increasing speed. The results for the electric driveline faults show that this trend cannot be generalised for all the faults, as the controllability deteriorated for some faults during manoeuvres with low lateral acceleration and low speed. The proposed method is generic and can be applied to various other types of road vehicles and faults. "p"QC 20140509. Updated from manuscript to article in journal"/p""p""/p""p"Funder: Swedish Hybrid vehicle Center

Abstract

practical technique is investigated for the determination of dynamic stresses in pipelines through the use of Finite Element Method (FEM) and field measurement vibrations at selected points. Numerical simulation of a harmonically loaded pipeline structure is used to establish the validity of the technique in the frequency domain. The analysis is carried out for a fixed-hinged pipe model. The results show that lack of coincidence between the vibration measurement points (VMPs) and the exciting force, or the use of only translational vibration measurements (TVMs) produce an approximate stress picture. The extent of the “error” in these cases is found to depend on the density of the VMPs and the proximity between these points and the exciting force location. A safety-related risk assessment is applied to find the minimum distance between measuring points that is needed to meet design codes reliability specifications.Copyright © 1998 by ASME

Abstract

This article discusses the results of supporting transition towards fully automated driving with remote operator support via the novel V2X channels. Automated passenger cars are equipped with multiple sensors (radars, cameras, LiDARs, inertia, GNSS, etc.), the operation of which is limited by weather, detection range, processing power and resolution. The study explores the use of a dedicated network for supporting automated driving needs. The MEC server latencies and bandwidths are compared between the Tampere, Finland test network and studies conducted in China to support remote passenger car operation. In China the main aim is to evaluate the network latencies in different communication planes, whereas the European focus is more on associated driving applications, thus making the two studies mutually complementary.5G revolutionizes connected driving, providing new avenues due to having lower and less latency variation and higher bandwidths. However, due to higher operating frequencies, network coverage is a challenge and one base station is limited to a few hundred meters and thus they deployed mainly to cities with a high population density. Therefore, the transport solutions are lacking so-called C-V2X (one form of 5G RAT) to enable data exchanges between vehicles (V2V) and also between vehicles and the digital infrastructure (V2I). The results of this study indicate that new edge-computing services do not cause a significant increase in latencies $(\\lt 100$ ms), but that latency variation (11 - 192 ms) remains a problem in the first new network configurations.

Abstract

This work provides an unsupervised learning approach based on a single-valued performance indicator to monitor the global behavior of critical components in a viaduct, such as bearings. We propose an outlier detection method for longitudinal displacements to assess the behavior of a singular asymmetric prestressed concrete structure with a 120 m high central pier acting as a fixed point. We first show that the available long-term horizontal displacement measurements recorded during the undamaged state exhibit strong correlations at the different locations of the bearings. Thus, we combine measurements from four sensors to design a robust performance indicator that is only weakly affected by temperature variations after the application of principal component analysis. We validate the method and show its efficiency against false positives and negatives using several metrics: accuracy, precision, recall, and F1 score. Due to its unsupervised learning scope, the proposed technique is intended to serve as a real-time supervision tool that complements maintenance inspections. It aims to provide support for the prioritization and postponement of maintenance actions in bridge management. Authors would like to acknowledge the discussions with Marcos Pantaleón from APIA XXI, Ambher Monitoring Systems and Banobras S.N.C. This work has received funding from the European’s Union Horizon 2020 research and innovation program under the grant agreement No 690660 (RAGTIME Project) and No 769373 (FORESEE Project). This paper reflects only the author’s views. The European Commission and INEA are not responsible for any use that may be made of the information contained therein. David Pardo has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 777778 (MATHROCKS), the European POCTEFA 2014-2020 Project PIXIL (EFA362/19) by the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra program, the Project of the Spanish Ministry of Science and Innovation with reference PID2019-108111RBI00 (FEDER/AEI), the BCAM “Severo Ochoa” accreditation of excellence (SEV-2017-0718), and the Basque Government through the BERC 2018-2021 program, the two Elkartek projects 3KIA (KK-2020/00049) and MATHEO (KK-2019-00085), the grant "Artificial Intelligence in BCAM number EXP. 2019/00432", and the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education. Document type: Article

Abstract

Switches and crossings (S&C) or turnouts are one of the important systems in the Swedish railway traffic maintenance planning. For immediate diverting of the trains, they need to be predict the working condition for short time duration, also known as nowcasting and for long time duration, also known as forecasting. The prediction of the condition of turnout is useful for traffic planning without disrupting to the traffic. Hence, the main purpose of this paper is to predict the condition of S&Cs for shorter and longer duration. In order to achieve it, at first, statistical analysis is carried out to find the root causes of failures. Secondly, non-homogenous Poisson process is applied to nowcast and forecast the working condition. The results of this study will guide the train dispatchers to plan the train timetable according the present traffic. Document type: Article