Influence of Blind Spot Assistance Systems in Heavy Commercial Vehicles on Accident Reconstruction.

Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRUs) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system's performance. The lateral distance between the right side of the commercial vehicle and the VRU, as well as obstacles between them, along with the speed difference can have great influence on the reliability of the assistance system. Depending on the concrete time of the system's warning signal, the accident can be avoided or not by the driver when reacting to this signal.

Testing the reliability of accident analysis methods: a comparison of AcciMap, STAMP-CAST and AcciNet.

Accident analysis methods are used to model the multifactorial cause of adverse incidents. Methods such as AcciMap, STAMP-CAST and recently AcciNet, are systemic approaches that support the identification of safety interventions across sociotechnical system levels. Despite their growing popularity, little is known about how reliable systems-based methods are when used to describe, model and classify contributory factors and relationships. Here, we conducted an intra-rater and inter-rater reliability assessment of AcciMap, STAMP-CAST and AcciNet using the Signal Detection Theory (SDT) paradigm. A total of 180 hours' worth of analyses across 360 comparisons were performed by 30 expert analysts. Findings revealed that all three methods produced a weak to moderate positive correlation coefficient, however the inter-rater reliability of STAMP-CAST was significantly higher compared to AcciMap and AcciNet. No statistically significant or practically meaningful differences were found between methods in the overall intra-rater reliability analyses. Implications and future research directions are discussed.

Practitioners who undertake accident analysis within their organisations should consider the use of STAMP-CAST due to the significantly higher inter-rater reliability findings obtained in this study compared to AcciMap and AcciNet, particularly if they tend to work alone and/or part of relatively small teams.

Burden of injury due to occupational accidents and its spatiotemporal trend at the national and subnational levels in Iran, 2011-2018.

PURPOSE: The spatiotemporal trend of the burden of injury due to occupational accidents in Iran, 2011-2018 were assessed at the national and subnational levels. METHODS: The burden of occupational injury was estimated using three datasets of occupational injury data, the employed population, and duration and disability weight of injuries. RESULTS: The disability-adjusted life years (DALYs), deaths, DALY rate, and death rate (per 100,000 workers) of occupational injury in Iran drastically decreased from 169,523, 2,280, 827, and 11 in 2011 to 86,235, 1,151, 362, and 5 in 2018, respectively. The DALY rates of occupational injury were significantly different by gender and age in a manner that the DALY rate of men was much higher than that of women and the DALY rates by age group in 2018 ranged from 98 for 50 y and over to 901 for 15-19 y. The shares of injury outcomes in the total DALYs in 2018 were as follows: 63.6% for fatal injuries, 17.4% for fracture, 7.9% for open wound, 7.3% for amputation, and 3.8% for other injuries. Over 83% of the DALYs was observed in three economic activity groups of construction, manufacturing, and community, social, and personal service activities. The three provinces with the highest DALY rates in 2018 were Markazi, West Azarbaijan, and East Azarbaijan, respectively. CONCLUSION: Despite the decreasing temporal trend, the burden of occupational injury in Iran in 2018 was high. The high-risk groups and hot spot provinces should be taken into more consideration for further reduction of the injury burden.

Impact of Partially Automated Driving Functions on Forensic Accident Reconstruction: A Simulator Study on Driver Reaction Behavior in the Event of a Malfunctioning System Behavior.

Partially automated driving functions (SAE Level 2) can control a vehicle's longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver's ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The results show that none of the subjects could avoid leaving their lane and moving into the oncoming lane and, therefore, could not control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 s. The second scenario examined driver responses to phantom braking caused by AEBS. We found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered.

Factors That Affect Drivers' Perception of Closing and an Immediate Hazard.

OBJECTIVE: To measure the looming threshold for when drivers perceive closing and an immediate hazard and determine what factors affect these thresholds. BACKGROUND: Rear-end collisions are a common type of crash. One key issue is determining when drivers first perceive they need to react. The looming threshold for closing and an immediate hazard are critical perceptual thresholds that reflect when drivers perceive they need to react. METHOD: Two driving simulator experiments examined whether engaging in a cell phone conversation and whether the complexity of the roadway environment affect these thresholds for the perception of closing and immediate hazard. Half of the participants engaged in a cognitive task, the last letter task, to emulate a cell phone conversation, and all participants experienced both simple and complex roadway environments. RESULTS: Drivers perceived an immediate hazard later when engaged in a cell phone conversation than when not engaged in a conversation but only when the driving task was relatively less demanding (e.g., simple roadway, slow closing velocity). Compared to simple scenes, drivers perceived closing and an immediate hazard later for complex scenes but only when closing velocity was 30 mph (48.28 km/h) or greater. CONCLUSION: Cell phone conversation can affect when drivers perceive an immediate hazard when the roadway is less demanding. Roadway complexity can affect when drivers perceive closing and an immediate hazard when closing velocity is high. APPLICATION: Results can aid accident analysis cases and the design of driving automation systems by suggesting when a typical driver would respond.

John Senders, Human Error, and System Safety.

OBJECTIVE: I examine John Senders' work and discuss his influence on the study of error causation,error mitigation, and sociotechnical system safety. BACKGROUND: John Senders' passing calls for an evaluation of the impact of his work. METHOD: I review literature and accident investigation findings to discuss themes in Senders' work and potential associations between that work and error causation and system safety. RESULTS: Senders consistently emphasized empirical rigor and theoretical exploration in his research, with the desire to apply that work to enhance human performance. He has contributed to changing the way error has been viewed, and to developing and implementing programs and techniques to mitigate error. While a causal relationship between Senders' work and safety cannot be established, an association can be drawn between his research and efforts to mitigate error. CONCLUSION: Because of Senders' work, we have a better understanding of error causation and enhanced ways of mitigating system errors. However, new sources of error, involving advanced systems and operators' knowledge and understanding of their functionalities can, if not addressed, degrade system safety. APPLICATION: Modifications to advanced automation and operator training are suggested, and research to improve operator expertise in interacting with automated systems proposed.

Towards a unified model of accident causation: refining and validating the systems thinking safety tenets.

The systems thinking tenets were developed based on a synthesis of contemporary accident causation theory, models and approaches and encapsulate 15 features of complex systems that interact to create both safety and adverse events. Whilst initial testing provided supportive evidence, the tenets have not yet been subject to formal validation. This article presents the findings from a three-round Delphi study undertaken to refine and validate the tenets and assess their suitability for inclusion in a unified model of accident causation. Participants with expertise in accident causation and systems thinking provided feedback on the tenets and associated definitions until an acceptable level of consensus was achieved. The results reduced the original 15 tenets to 14 and 10 were identified as important to include in unified model of accident causation. The refined systems thinking tenets are presented along with future research directions designed to facilitate their use in safety practice.Practitioner summary: This article presents a refined and validated set of systems thinking tenets which describe features of complex systems that interact to create adverse events. The tenets can be used by practitioners to proactively identify safety leading indicators and contributory factors during adverse event analysis.

C-ITS Relevant Critical Vehicle-to-Vehicle Accident Scenarios for Accident Analysis.

The relevance of scientific investigations, whether simulative or empirical, is strongly related to the environment used and the scenarios associated with it. Within the field of cooperative intelligent transport systems, use-cases are defined to describe the benefits of applications. This has already been conducted in the available safety-relevant Day 1 applications longitudinal and intersection collision risk warning through the respective technical specifications. However, the relevance of traffic scenarios is always a function of accident severity and frequency of a retrospective consideration of accident databases. In this study, vehicle-to-vehicle scenarios with high frequency and/or severe personal injuries are therefore determined with the help of the CISS database and linked to the use-cases of the safety-relevant Day 1 applications. The relevance of the scenarios thus results on the one hand from the classical parameters of retrospective accident analysis and on the other hand from the coverage by the named vehicle-to-x applications. As a result, accident scenarios with oncoming vehicles are the most relevant scenarios for investigations with cooperative intelligent transport systems. In addition, high coverage of the most critical scenarios within the use-cases of longitudinal and intersection collision risk warning is already apparent.

Does Vehicle-2-X Radio Transmission Technology Need to Be Considered within Accident Analysis in the Future?

In this analysis, Cooperative Intelligent Transportation System relevant scenarios are created to investigate the need to differentiate Vehicle-to-X transmission technologies on behalf of accident analysis. For each scenario, the distances between the vehicles are calculated 5 s before the crash. Studies on the difference between Dedicated Short-Range Communication (IEEE 802.11p) and Cellular Vehicle-to-X communication (LTE-V2C PC5 Mode 4) are then used to assess whether both technologies have a reliable connection over the relevant distance. If this is the case, the transmission technology is of secondary importance for future investigations on Vehicle-to-X communication in combination with accident analysis. The results show that studies on freeways and rural roads can be carried out independently of the transmission technology and other boundary conditions (speed, traffic density, non-line of sight/line of sight). The situation is different for studies in urban areas, where both technologies may not have a sufficiently reliable connection range depending on the traffic density.

Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer.

OBJECTIVE: We propose a method for recognizing driver distraction in real time using a wrist-worn inertial measurement unit (IMU). BACKGROUND: Distracted driving results in thousands of fatal vehicle accidents every year. Recognizing distraction using body-worn sensors may help mitigate driver distraction and consequently improve road safety. METHODS: Twenty participants performed common behaviors associated with distracted driving while operating a driving simulator. Acceleration data collected from an IMU secured to each driver's right wrist were used to detect potential manual distractions based on 2-s long streaming data. Three deep neural network-based classifiers were compared for their ability to recognize the type of distractive behavior using F1-scores, a measure of accuracy considering both recall and precision. RESULTS: The results indicated that a convolutional long short-term memory (ConvLSTM) deep neural network outperformed a convolutional neural network (CNN) and recursive neural network with long short-term memory (LSTM) for recognizing distracted driving behaviors. The within-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.82, and 0.82, respectively. The between-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.76, and 0.85, respectively. CONCLUSION: The results of this pilot study indicate that the proposed driving distraction mitigation system that uses a wrist-worn IMU and ConvLSTM deep neural network classifier may have potential for improving transportation safety.

Adopting systems models for multiple incident analysis: utility and usability.

BACKGROUND: This study aims to present two system models widely used in Human Factors and Ergonomics (HF/E) and evaluate whether the models are adoptable to England's national patient safety team in improving the exploration and understanding of multiple incident reports of an active patient safety issue and the development of the remedial actions for a potential National Patient Safety Alert. The existing process of examining multiple incidents is based on inductive thematic analysis and forming the remedial actions is based on barrier analysis of intelligence on potential solutions. However, no formal systems models evaluated in this study have been used. METHODS: AcciMap and Systems Engineering Initiative for Patient Safety (SEIPS) were selected, applied and evaluated to the analysis of two different sets of patient safety incidents: (i) incidents concerning ingestion of superabsorbent polymer granules and (ii) incidents concerning the interruption in use of High Nasal Flow Oxygen. The first set was analysed by the first author and the utility and usability were reflected. The second set was analysed collectively by a purposeful sample of patient safety team members, who create the National Patient Safety Alerts from incident-level data and information. All of them attended a 30-min video-based training and a 1.5 h case-based online workshop. Post-workshop individual interviews were conducted to evaluate their perceived utility and usability of each model. RESULTS: The patient safety team showed overwhelming support for the utility of the system models as a 'framework' that provides a systematic, structured way of looking at an issue and examining the causes, whilst also sharing concerns regarding their usability. AcciMap was viewed useful particularly in providing a visual comprehensive overview of the issue but considered chaotic by some participants due to many arrows between factors. SEIPS was perceived easier to understand due to the familiarity of the structure (Donbedian's model), but the non-hierarchical format of SEIPS was considered less useful. CONCLUSIONS: The participants of the study agreed with the high level of utility of both models for their unique strengths, but shared some concern for the usability of them in terms of complexity and further training/coaching time would be required to adopt these models in their daily practices. It is recommended that the gap between HF/E practitioners and patient safety practitioners can be narrowed by strengthening education, and coaching and mentoring relationships between the two groups, led by the increasing number of healthcare practitioners who embrace their membership to HF/E practice.

Complexity theory in accident causation: using AcciMap to identify the systems thinking tenets in 11 catastrophes.

The quest to explain and understand the cause of accidents is both ever-present and ongoing amongst the safety science community. In an attempt to advance the theory and science of accident causation, researchers have recently formalised a set of '15 systems thinking tenets' that cover the conditions and characteristics of work systems that are believed to contribute to the cause of accidents. The purpose of this study was to attempt to identify the systems thinking tenets across a range of different systems and accidents using the Accident Mapping (AcciMap) method. The findings suggest that the tenets can be attributed to play a role in accident causation, however as a result of this process, the capability of AcciMap has been brought into question. Implications and directions for future research are described. Practitioner Summary: This study is an extension of previous work that suggested there was a need to test for the 'systems thinking tenets of accident causation' in a multi-incident dataset. We used AcciMap to evaluate whether it has the capability to support ongoing accident analysis activities in ergonomics research.

State of science: evolving perspectives on 'human error'.

This paper reviews the key perspectives on human error and analyses the core theories and methods developed and applied over the last 60 years. These theories and methods have sought to improve our understanding of what human error is, and how and why it occurs, to facilitate the prediction of errors and use these insights to support safer work and societal systems. Yet, while this area of Ergonomics and Human Factors (EHF) has been influential and long-standing, the benefits of the 'human error approach' to understanding accidents and optimising system performance have been questioned. This state of science review analyses the construct of human error within EHF. It then discusses the key conceptual difficulties the construct faces in an era of systems EHF. Finally, a way forward is proposed to prompt further discussion within the EHF community. Practitioner statement This state-of-science review discusses the evolution of perspectives on human error as well as trends in the theories and methods applied to understand, prevent and mitigate error. It concludes that, although a useful contribution has been made, we must move beyond a focus on an individual error to systems failure to understand and optimise whole systems.

Comparative analysis of two almost identical traffic accidents.

In the Czech Republic, 1 500 suicides are committed in average per year approximately. Only in a small percentage has been used vehicle as a tool. Prove suicidal intent in a fatal road accident has been difficult, but mostly these accidents have similar characteristics - impact with tree or similar fixed obstacle, non - use of seat belt, absence of brake traces, etc. The aim of this paper is to present and analyse two almost identical traffic accidents - identical location, identical vehicle type. While one of these was fatal (but not proven suicide), the second accident was not fatal, but with suicidal intent. Data were obtained as a part of the Czech In-depth Study conducted by Transport Research Centre. Data from In-depth Accident Analysis provide a comprehensive view of all the factors related to a particular accident and serve to identify the characteristics leading to the crash occurrence.

Analyzing Collision, Grounding, and Sinking Accidents Occurring in the Black Sea Utilizing HFACS and Bayesian Networks.

This study examines and analyzes marine accidents that have occurred over the past 20 years in the Black Sea. Geographic information system, human factor analysis and classification system (HFACS), and Bayesian network models are used to analyze the marine accidents. The most important feature distinguishing this study from other studies is that this is the first study to analyze accidents that have occurred across the whole Black Sea. Another important feature is the application of a new HFACS structure to reveal accident formation patterns. The results of this study indicate that accidents occurred in high concentrations in coastal regions of the Black Sea, especially in the Kerch Strait, Novorossiysk, Kilyos, Constanta, Riva, and Batumi regions. The formation of grounding and sinking accidents has been found to be similar in nature; the use of inland and old vessels has been highlighted as important factors in sinking and grounding incidents. However, the sequence of events leading to collision-contact accidents differs from the sequence of events resulting in grounding and sinking accidents. This study aims to provide information to the maritime industry regarding the occurrence of maritime incidents in the Black Sea, in order to assist with reduction and prevention of the marine accidents.

Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts.

Human Factors Analysis and Classification System (HFACS) is based upon Reason's organizational model of human error which suggests that there is a 'one to many' mapping of condition tokens (HFACS level 2 psychological precursors) to unsafe act tokens (HFACS level 1 error and violations). Using accident data derived from 523 military aircraft accidents, the relationship between HFACS level 2 preconditions and level 1 unsafe acts was modelled using an artificial neural network (NN). This allowed an empirical model to be developed congruent with the underlying theory of HFACS. The NN solution produced an average overall classification rate of ca. 74% for all unsafe acts from information derived from their level 2 preconditions. However, the correct classification rate was superior for decision- and skill-based errors, than for perceptual errors and violations. Practitioner Summary: A model to predict unsafe acts (HFACS level 1) from their preconditions (HFACS level 2) was developed from the analysis of 523 military aircraft accidents using an artificial NN. The results could correctly predict approximately 74% of errors.

What went right? An analysis of the protective factors in aviation near misses.

Learning from successful safety outcomes, or what went right, is an important emerging component of maintaining safe systems. Accordingly, there are increasing calls to study normal performance in near misses as a part of safety management activities. Despite this, there is limited guidance on how to accomplish this in practice. This article presents a study in which using Rasmussen's risk management framework to analyse 16 serious incidents from the aviation domain. The findings show that a network of protective factors prevents accidents with factors identified across the sociotechnical system. These protective networks share many properties with those identified in accidents. The article demonstrates that is possible to identify these networks of protective factors from incident investigation reports. The theoretical implications of these results and future research opportunities are discussed. Practitioner Statement: The analysis of near misses is an important part of safety management activities. This article demonstrates that Rasmussen?s risk management framework can be used to identify networks of protective factors which prevent accidents. Safety practitioners can use the framework described to discover and support the system-wide networks of protective factors.

Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel.

OBJECTIVE: The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation. BACKGROUND: Securing driver engagement-by mitigating irony of automation (i.e., the better the automation, the less attention drivers will pay to traffic and the system, and the less capable they will be to resume control) and by communicating system limitations to avoid mental model misconceptions-is a major challenge in the human factors literature. METHOD: One hundred six drivers participated in three test-track experiments in which we studied driver intervention response to conflicts after driving highly reliable but supervised automation. After 30 min, a conflict occurred wherein the lead vehicle cut out of lane to reveal a conflict object in the form of either a stationary car or a garbage bag. RESULTS: Supervision reminders effectively maintained drivers' eyes on path and hands on wheel. However, neither these reminders nor explicit instructions on system limitations and supervision responsibilities prevented 28% (21/76) of drivers from crashing with their eyes on the conflict object (car or bag). CONCLUSION: The results uncover the important role of expectation mismatches, showing that a key component of driver engagement is cognitive (understanding the need for action), rather than purely visual (looking at the threat), or having hands on wheel. APPLICATION: Automation needs to be designed either so that it does not rely on the driver or so that the driver unmistakably understands that it is an assistance system that needs an active driver to lead and share control.

Falls From Agricultural Machinery: Risk Factors Related to Work Experience, Worked Hours, and Operators' Behavior.

Objective We investigated the risk factors for falls when egressing from agricultural tractors, analyzing the role played by worked hours, work experience, operators' behavior, and near misses. Background Many accidents occur within the agricultural sector each year. Among them, falls while dismounting the tractor represent a major source of injuries. Previous studies pointed out frequent hazardous movements and incorrect behaviors adopted by operators to exit the tractor cab. However, less is known about the determinants of such behaviors. In addition, near misses are known to be important predictors of accidents, but they have been under-investigated in the agricultural sector in general and as concerns falls in particular. Method A questionnaire assessing dismounting behaviors, previous accidents and near misses, and participants' relation with work was administered to a sample of Italian tractor operators ( n = 286). Results A mediated model showed that worked hours increase unsafe behaviors, whereas work experience decreases them. Unsafe behaviors in turn show a positive association with accidents, via the mediation of near misses. Conclusions We gave a novel contribution to the knowledge of the chain of events leading to fall accidents in the agricultural sector, which is one of the most hazardous industries. Applications Besides tractor design improvements, preventive training interventions may focus on the redesign of the actual working strategies and the adoption of engaging training methods in the use of machinery to optimize the learning of safety practices and safe behaviors.

The Automation-by-Expertise-by-Training Interaction.

OBJECTIVE: I introduce the automation-by-expertise-by-training interaction in automated systems and discuss its influence on operator performance. BACKGROUND: Transportation accidents that, across a 30-year interval demonstrated identical automation-related operator errors, suggest a need to reexamine traditional views of automation. METHOD: I review accident investigation reports, regulator studies, and literature on human computer interaction, expertise, and training and discuss how failing to attend to the interaction of automation, expertise level, and training has enabled operators to commit identical automation-related errors. RESULTS: Automated systems continue to provide capabilities exceeding operators' need for effective system operation and provide interfaces that can hinder, rather than enhance, operator automation-related situation awareness. Because of limitations in time and resources, training programs do not provide operators the expertise needed to effectively operate these automated systems, requiring them to obtain the expertise ad hoc during system operations. As a result, many do not acquire necessary automation-related system expertise. CONCLUSION: Integrating automation with expected operator expertise levels, and within training programs that provide operators the necessary automation expertise, can reduce opportunities for automation-related operator errors. APPLICATION: Research to address the automation-by-expertise-by-training interaction is needed. However, such research must meet challenges inherent to examining realistic sociotechnical system automation features with representative samples of operators, perhaps by using observational and ethnographic research. Research in this domain should improve the integration of design and training and, it is hoped, enhance operator performance.