Vertical and horizontal alignment within organizations are seen as prerequisites for meeting strategic objectives and indications of effective management. In the area of safety management, the concept of vertical alignment has been followed through the introduction of hierarchical structures and bidirectional communication, but horizontal alignment has been given little attention. The principal goal of this study was the assessment of horizontal alignment within an aviation organization with the use of data from safety investigations, audits and meetings in order to explore the extent to which (1) causal factors recorded in safety investigation reports comprised topics discussed by safety committees and focus areas of internal safety auditors, and (2) the agendas of safety committees include weak points revealed during safety audits. The study employed qualitative and quantitative analysis of data collected over a 6 years’ period at three organizational levels. The results suggested a low horizontal alignment across the three pairs of the corresponding safety management activities within each organizational level. The findings were attributed to the inadequacy of procedures and lack of a safety information database for consistently sharing safety information, cultural factors and lack of planning for the coordination of safety management activities. The current research comprises a contribution to the literature and practice and introduces a technique to assess the intra-alignment of safety management initiatives within various organizational levels. Future research is needed in order to investigate the association between horizontal alignment of safety management practices and safety performance.
Vertical and horizontal alignment within organizations are seen as prerequisites for meeting strategic objectives and indications of effective management. In the area of safety management, the concept of vertical alignment has been followed through the introduction of hierarchical structures and bidirectional communication, but horizontal alignment has been given little attention. The principal goal of this study was the assessment of horizontal alignment within an aviation organization with the use of data from safety investigations, audits and meetings in order to explore the extent to which (1) causal factors recorded in safety investigation reports comprised topics discussed by safety committees and focus areas of internal safety auditors, and (2) the agendas of safety committees include weak points revealed during safety audits. The study employed qualitative and quantitative analysis of data collected over a 6 years’ period at three organizational levels. The results suggested a low horizontal alignment across the three pairs of the corresponding safety management activities within each organizational level. The findings were attributed to the inadequacy of procedures and lack of a safety information database for consistently sharing safety information, cultural factors and lack of planning for the coordination of safety management activities. The current research comprises a contribution to the literature and practice and introduces a technique to assess the intra-alignment of safety management initiatives within various organizational levels. Future research is needed in order to investigate the association between horizontal alignment of safety management practices and safety performance.
Literature and industry standards do not mention inclusive guidelines to generate safety recommendations. Following a literature review, we suggest nine design criteria as well as the classification of safety recommendations according to their scope (i.e. organisational context, stakeholders addressed and degree of change) and their focus, the latter corresponding to the type of risk barrier introduced. The design and classification criteria were applied to 625 recommendations published by four aviation investigation agencies. The analysis results suggested sufficient implementation of most of the design criteria. Concerning their scope, the findings showed an emphasis on processes and structures (i.e. lower organisational contexts), adaptations that correspond to medium degree of changes, and local stakeholders. Regarding the focus of the recommendations, non-technical barriers that rely mostly on employees’ interpretation were introduced by the vast majority of safety recommendations. Also, statistically significant differences were detected across investigation authorities and time periods. This study demonstrated how the application of the suggested design and classification frameworks could reveal valuable information about the quality, scope and focus of recommendations. Especially the design criteria could function as a starting point towards the introduction of a common standard to be used at local, national and international levels.
In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
