Journal of Safety Research最新文献

{"title":"The ROADS project: Road observational assessment of driving distractions","authors":"Marko Gjorgjievski , Bradley Petrisor , Sheila Sprague , Silvia Li , Herman Johal , Bill Ristevski","doi":"10.1016/j.jsr.2024.11.016","DOIUrl":"10.1016/j.jsr.2024.11.016","url":null,"abstract":"<div><div><em>Background</em>: Globally, motor-vehicle collisions cause 1.35 million deaths and more than 78 million injuries every year, with distracted driving contributing to many of these tragedies. Our main objective was to covertly determine the proportion of distracted drivers in live traffic. <em>Methods:</em> ROADS was a covert observational study conducted from November 2020–June 2021. We observed drivers on the highways and urban streets between Hamilton and Toronto, Ontario. The research team observed drivers of moving vehicles and collected data covertly while driving beside them in live traffic. Moving passenger vehicles ahead of the research team were randomly screened for inclusion. Stopped/parked vehicles, buses, and semi-trucks were excluded. Demographic and safety variables included estimated age and sex, seatbelt usage, and two-handed driving. Driving distractions were categorized as in-vehicle, outer-vehicle, and mobile phones. Driving errors, such as lane drift, evasive maneuvers, and near-crash/crash, were recorded. We analyzed associations between demographic and situational variables (weekday/weekend, urban/highway, presence/absence of passenger) and distracted driving, as well as associations between driving errors and distracted driving. <em>Results:</em> Of the observed 1,105 drivers, 609 (55.1%) were distracted. In-vehicle distractions (42.3%, 467/1105) were most prevalent, while 151 (13.7%) drivers were using mobile phones. Hands-free usage was observed in 92 (8.3%) drivers, while 63 (5.7%) drivers used a handheld device, visibly manipulating (3.4%, 38/1105), or actively talking (2.3%, 25/1105). Of the 24 (2.2%) drivers observed exhibiting driving errors, 23 (95.8%) drivers were visibly distracted. Younger estimated age (under 30 years old: OR 2.0, CI 1.320–3.105; 30–50 years old: OR 1.5, CI 1.090–1.925), and driver errors were significantly associated with distracted driving (p < 0.005). Sex, urban vs highways, and weekday vs weekend did not demonstrate a statistically significant association with distracted driving. <em>Conclusion:</em> By covertly observing moving vehicles while actively participating in live traffic, we identified that 55.1% of drivers were distracted, and approximately one in seven drivers used their mobile phones. Of the 24 drivers who were recorded making driving errors, an astounding 95.8% (23) were distracted, with two-thirds of these drivers illegally engaging with their phones. Also, driving on city streets versus highways (>60 km/hr) did not play a role in distracted driving. All this indicates that distracted driving is not only prevalent but also pervasive. Future research should focus on targeted driver education and behavioral modification. <em>Practical Applications:</em> This data can be applied towards driver education programs counseling drivers on dangerous distracting behaviors, as well as influencing legislature, informing, and providing law enforcement insight into worri","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 91-97"},"PeriodicalIF":3.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perception of distraction and risk among drivers engaging in non-driving tasks: Findings from a German study","authors":"Maria Kreusslein ,&nbsp;Katja Schleinitz ,&nbsp;Markus Schumacher","doi":"10.1016/j.jsr.2024.11.019","DOIUrl":"10.1016/j.jsr.2024.11.019","url":null,"abstract":"<div><div><em>Introduction:</em> One of the leading causes of traffic crashes is the distraction of drivers caused by performing secondary tasks. <em>Method:</em> We conducted a nationwide interview study with car drivers in Germany. A total sample of 1,072 drivers participated in a face-to-face semi-standardized interview based on quota sampling. Almost 90% of all drivers performed a secondary task. <em>Results:</em> On average, drivers reported two non-driving-related activities in the last 30 min of driving. The most frequent activities were interactions with passengers, internal distractions (e.g., intense thinking, singing), and operating vehicle instruments. Mobile phone operations were mentioned less frequently, and texting, reading, and browsing were reported in 6% of the activities. Texting, reading, browsing, and hand-held phoning were rated as the most distracting and risky. Drivers perceive secondary tasks like passenger interaction and hygiene as less risky. The risk ratings when performing a secondary task were significantly lower than the overall risk rating of the respective task. Demographic analysis showed that women rated some tasks as riskier than men, while older drivers perceived higher risks than younger ones. <em>Conclusion:</em> The findings on the frequency of performed secondary tasks illustrate that drivers underestimate the risks of seemingly trivial secondary activities. Age and gender influence risk perception and distraction. <em>Practical implication</em>: Raising awareness of the negative consequences of engaging in activities that appear insignificant is advisable. Information about the crash risk of all secondary tasks should be provided more often (e.g., in commercials, especially for tasks that appear trivial and safe to perform while driving). Additionally, interventions aimed at promoting safer driving practices should consider demographic factors, such as age and gender, to enhance their effectiveness.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 109-120"},"PeriodicalIF":3.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Driving a car under the influence of alcohol in Germany: Results from a trip-based self-report measurement","authors":"Bernhard Schrauth","doi":"10.1016/j.jsr.2024.10.001","DOIUrl":"10.1016/j.jsr.2024.10.001","url":null,"abstract":"<div><div><em>Introduction:</em> Driving under the influence of alcohol comprises a serious road safety issue. A comprehensive investigation is challenging and a high number of unreported cases of driving under the influence of alcohol is suspected. Existing methods, including roadside surveys or period-based self-reports, are either difficult to implement or may lack informative value. <em>Method:</em> This paper describes a newly developed questionnaire-based survey conducted in a nationwide online survey in Germany that measures the prevalence of driving under the influence of alcohol via self-reports concerning randomly selected trips from 7 days prior. The trip-based data collection includes details about the reported car ride. Expected low case numbers are addressed by additionally recording the last trip driven under the influence of alcohol from the previous week. <em>Results:</em> Within the previous 7 days, 6.3% of the surveyed drivers had driven under the influence of alcohol. Further analyses aligned with familiar patterns from prior research: Age, sex, daytime, and days of the week significantly predict driving under the influence of alcohol. However, attitudes toward stricter rules are also identified as a factor. <em>Conclusions:</em> The proposed survey design enables the current findings to surpass results of previous surveys and yields data comparable to roadside survey results. The questionnaire proved feasible in conducting the survey and gathered valid findings that correspond to international research and traffic crash data. For Germany, in particular, and in alignment with familiar patterns related to times and days, the findings point to the likelihood that particularly males and younger drivers will drive under the influence of alcohol. <em>Practical Applications:</em> The proposed survey concept adds a new variant to the set of instruments for recording driving under the influence of alcohol by determining a trip-based prevalence, thus offering new insights into driving under the influence in alcohol of Germany.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"91 ","pages":"Pages 447-464"},"PeriodicalIF":3.9,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics of media-reported road traffic crashes related to new energy vehicles in China","authors":"Shuying Zhao ,&nbsp;Peixia Cheng ,&nbsp;David C. Schwebel ,&nbsp;Min Zhao ,&nbsp;Lei Yang ,&nbsp;Wangxin Xiao ,&nbsp;Guoqing Hu","doi":"10.1016/j.jsr.2024.11.012","DOIUrl":"10.1016/j.jsr.2024.11.012","url":null,"abstract":"<div><div><em>Introduction</em>: New energy vehicles (NEVs) refer to vehicles entirely or primarily powered by energy sources outside of conventional fuels. As the number of NEVs increases, road traffic crashes related to NEVs have emerged as a new challenge for road traffic injury prevention. However, basic epidemiological data are scarce concerning NEV-related crashes. <em>Methods</em>: Using media-reported crash data from the Automated Road Traffic Crash Data Platform (ARTCDP), a data platform developed and validated by our research group to gather eligible reports automatically and systematically from online Chinese media concerning road traffic crashes, we examined the characteristics of new energy vehicles between 2015 and 2022 in China. <em>Results:</em> The ARTCDP captured 2,927 crashes related to NEVs from 2015 to 2022, accounting for 1.1% of total number of motor vehicle-related crashes indexed by the ARTCDP during the same time period. Of them, 2,262 (77.3%) crashes occurred in east and central China. NEV-related traffic crashes occurred most often on urban roads (68.8%), well-lit roads (72.2%), roads without adequate safety infrastructure facilities (63.2%), and at intersections (78.7%). 1,864 media reports described the reason for the crash, with 44.1% listing two or more factors to explain the NEV-related crashes. Brake system failure and dangerous or improper driving operations were more frequently reported in NEV-related crashes than in other motorvehicle crashes (55.6% vs. 18.3% and 37.5% vs. 20.8%, <em>P</em> &lt; 0.01). NEV-related crashes occurred more often on rainy days and on foggy or smoggy days than other motor-vehicle crashes (83.6% vs. 72.2% and 4.1% vs. 0.7%, <em>P</em> &lt; 0.01). <em>Conclusions</em>: Media-reported news elucidate distinct characteristics of road traffic crashes involving NEVs versus other motor vehicles in China. <em>Practical applications:</em> NEV-related crashes represent an emerging road traffic safety challenge in China and worldwide. Characteristics revealed by media-reported NEV-related crashes merit the attention of policymakers, automobile industry, researchers, and law enforcement.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 48-54"},"PeriodicalIF":3.9,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk of crashes among self-employed truck drivers: Prevalence evaluation using fatigue data and machine learning prediction models","authors":"Rodrigo Duarte Soliani ,&nbsp;Alisson Vinicius Brito Lopes ,&nbsp;Fábio Santiago ,&nbsp;Luiz Bueno da Silva ,&nbsp;Nwabueze Emekwuru ,&nbsp;Ana Carolina Lorena","doi":"10.1016/j.jsr.2024.11.002","DOIUrl":"10.1016/j.jsr.2024.11.002","url":null,"abstract":"<div><div><em>Introduction</em>: Transportation companies have increasingly shifted their workforce from permanent to outsourced roles, a trend that has consequences for self-employed truck drivers. This transition leads to extended working hours, resulting in fatigue and an increased risk of crashes. The present study investigates the factors contributing to fatigue and impairment in truck driving performance while developing a machine learning-based model for predicting the risk of traffic crashes. <em>Method:</em> To achieve this, a comprehensive questionnaire was designed, covering various aspects of the participants’ sociodemographic characteristics, health, sleep, and working conditions. The questionnaire was administered to 363 self-employed truck drivers operating in the State of São Paulo, Brazil. Approximately 63% of the participants were smokers, while 17.56% reported drinking alcohol more than four times a week, and also admitted to being involved in at least one crash in the last three years. Fifty percent of the respondents reported consuming drugs (such as amphetamines, marijuana, or cocaine). <em>Results:</em> The surveyed individuals declared driving for approximately 14.62 h (SD = 1.97) before they felt fatigued, with an average of approximately 5.92 h of sleep in the last 24 h (SD = 0.96). Truck drivers unanimously agreed that waiting times for truck loading/unloading significantly impact the duration of their working day and rest time. The study employed eight machine learning algorithms to estimate the likelihood of truck drivers being involved in crashes, achieving accuracy rates ranging between 78% and 85%. <em>Conclusions:</em> These results validated the construction of accurate machine learning-derived models. <em>Practical Applications</em>: These findings can inform policies and practices aimed at enhancing the safety and well-being of self-employed truck drivers and the broader public.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 68-80"},"PeriodicalIF":3.9,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the time to death of pedestrian fatalities: A copula approach","authors":"Nafis Anwari ,&nbsp;Tanmoy Bhowmik ,&nbsp;Mohamed Abdel-Aty ,&nbsp;Naveen Eluru ,&nbsp;Juneyoung Park","doi":"10.1016/j.jsr.2024.11.007","DOIUrl":"10.1016/j.jsr.2024.11.007","url":null,"abstract":"<div><div><em>Introduction:</em> The study aims to investigate the instant fatality likelihood and time to death (lag time) of pedestrian fatalities using a copula-based joint modeling framework. The upper level model investigates whether or not the pedestrian died instantly, while the lower level model investigates time to death for pedestrians who did not die instantly. <em>Method:</em> The joint model was run on a dataset of 33,615 observations obtained from the Fatality Accident Reporting System for the 2015–2019 period. The effect of roadway and traffic characteristics were investigated on time to death using six copula structures along with their parameterized versions. <em>Results:</em> Gaussian parameterized copula was found to have the best fit. Weather, Driver age groups, Drunk/ distracted/ drowsy drivers, Hit and Run, Involvement of Large Truck, VRU age group, VRU Gender, Presence of Sidewalk, Presence of Intersection, Light Condition, and Speeding were significant common factors for both sub-models. The factors found to be significant exclusively to one of the sub-models include: Area type for the Binary Logit model, and Presence of Crosswalk and Fire station nearby for the Ordered Logit model. <em>Conclusions:</em> Instant fatality likelihood increased and lag time for non-instant fatalities decreased for 16–24 year old drivers, drunk drivers, during hit and run situations, when large trucks were involved, for the elderly pedestrians, for female pedestrians, during dark conditions, and when vehicles were speeding. On the other hand, instant fatality likelihood decreased and lag time for non-instant fatalities increased in adverse weather conditions, for elderly drivers, on sidewalks, at intersections, and during daylight hours. <em>Practical applications:</em> Results can be useful to transportation policymakers and practitioners in implementing countermeasures to improve road safety. These include placing sidewalks, various types of crosswalks, traffic calming measures, and adequate artificial lighting in areas frequented by pedestrians. Alcohol and drug testing need to be enforced.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 55-67"},"PeriodicalIF":3.9,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent class analysis of autonomous vehicle crashes","authors":"Jianfeng Qiao,&nbsp;Yanan Wang,&nbsp;Zixiu Zhao,&nbsp;Dawei Chen,&nbsp;Yanping Fu,&nbsp;Jie Hou","doi":"10.1016/j.jsr.2024.11.014","DOIUrl":"10.1016/j.jsr.2024.11.014","url":null,"abstract":"<div><div><em>Introduction:</em> Since September 2014, the California Department of Motor Vehicles has requested autonomous vehicle (AV) manufacturers to report their accidents if they take field tests on public roadways in California. These collision reports are heterogeneous containing a variety of accident factors. <em>Method:</em> To describe the accident more elaborately, we add three new category variables: ‘traffic control and status,’ ‘speed/speed change,’ and ‘type of accident location,’ extracted from crash narratives. Combining with the existing variables as model inputs, we use Latent Class Analysis (LCA) to investigate the mixture types of traffic accidents. After using ‘Mplus’ (LCA tool), the data set with 308 cases has been segmented into three clusters, including ‘rear-end collisions after the speed change of AV,’ ‘sideswipe collisions at parking places,’ and ‘hit-object collisions in normal traffic road.’ <em>Results:</em> These three clusters are not highlighted in previous literature and Cluster 1 shows AV should not be designed too ethically. To follow the driving habits of traditional drivers, AVs should accelerate vehicles quickly when they start to move and delay stopping in front of stop lines, traffic lights, and yielding. The cluster-based analyses show that applying LCA as a preliminary analysis can reveal the interesting hierarchical patterns hidden in the dataset and help traffic safety researchers improve AV safety performances.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 81-90"},"PeriodicalIF":3.9,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictors of driving errors contributing to crashes in older adults across age groups, 2010 to 2020","authors":"Gilsu Pae ,&nbsp;Jonathan Davis ,&nbsp;Joseph Cavanaugh ,&nbsp;Motao Zhu ,&nbsp;Cara Hamann","doi":"10.1016/j.jsr.2024.11.010","DOIUrl":"10.1016/j.jsr.2024.11.010","url":null,"abstract":"<div><div><em>Introduction:</em> Given the largely autocentric nature of the United States, drivers continue to operate vehicles with varying levels of driving ability and self-restriction as they advance into older age. This study explores the associations of vehicle actions and traffic control devices with older drivers’ driving errors contributing to crashes, incorporating age group as effect modifiers of these relationships. <em>Method:</em> This study includes crashes reported to the Iowa Department of Transportation from 2010 to 2020. Analysis was completed for drivers involved in a crash who were aged 45 years and older (n = 254,912). Driving errors were identified based on driver contributing factors reported in the Iowa crash data. A multivariable logistic regression model was built to model predictors of driving errors, focusing on crash-related vehicle actions and traffic control devices. Additionally, interaction terms were incorporated to examine the moderating effect of age groups (45–64; 65–74; 75–84; 85+). <em>Results:</em> Driving errors increased with age, especially in the middle-old age group (75–84). A higher probability of driving errors was observed in changing lanes, merging, and turning, with right turns showing the most substantive increase in the middle-old age group compared to the other age groups. Stop and yield signs were associated with a higher probability of driving errors, increasing monotonically with age. The middle-old age group exhibited a notable increase in driving errors at uncontrolled or traffic signaled locations compared to the other age groups. <em>Conclusions:</em> The significant increase in driving errors at and beyond the middle-old age group may demonstrate higher age-related declines in safe driving compared to younger age groups. <em>Practical Applications:</em> Careful evaluations for older drivers’ fitness to drive during license renewal periods are needed once drivers reach the middle-old age. Additionally, effective combinations of advanced technologies, traffic systems, and policies are necessary to reduce the burdens associated with aging.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 40-47"},"PeriodicalIF":3.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning approach to understanding the road and traffic environments of crashes involving driver distraction and inattention (DDI) on rural multilane highways","authors":"Chenxuan Yang ,&nbsp;Jun Liu ,&nbsp;Zihe Zhang ,&nbsp;Emmanuel Kofi Adanu ,&nbsp;Praveena Penmetsa ,&nbsp;Steven Jones","doi":"10.1016/j.jsr.2024.11.011","DOIUrl":"10.1016/j.jsr.2024.11.011","url":null,"abstract":"<div><div><em>Introduction</em>: Driver distraction and inattention (DDI) are major causes of road crashes, especially on rural highways. However, not all instances of distracted or inattentive driving lead to crashes. Previous studies indicate that DDI-related driving behavior is closely associated with low-traffic and less complex driving environments. Nevertheless, it is unclear if these traffic or road environments also increase the likelihood of crashes involving DDI. <em>Method</em>: This study employed machine learning algorithms to identify the factors contributing to DDI-involved crashes on rural highways. This study applied multiple machine learning models including the Light Gradient Boosting Model (LGBM), Random Forest (RF), and Neural Network (NN) to quantify the correlations of DDI-involved crashes related to road and traffic environments. The study leveraged a statewide crash database with unique roadway data that contains variables for median type (e.g., 4-ft flush medians) and roadside access point density. To deal with the extreme imbalance of data, two sampling methods (over and under-sampling) were used to balance the data for machine learning<em>. Results</em>: Modeling results indicated that the road and traffic environments that are strongly linked to DDI-involved crashes in general overlap with the environments that lead to DDI-related driving behavior, except for the truck volumes in traffic. Crashes that involved DDI were more likely to occur in environments with non-traversable medians (compared to 4-ft flush medians), lower-volume traffic, and greater access spacing on roadsides. With regard to truck volumes, a non-linear relationship with the occurrence of DDI-involved crashes was uncovered. Traffic with about 8 to 10% of trucks is associated with the highest likelihood of DDI-involved crashes. <em>Practical Applications:</em> This study provides valuable information for drivers who need to be careful while driving in certain environments with a risk of DDI-involved crashes and for agencies who need to take actions to address the issue of DDI under such environments.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 14-26"},"PeriodicalIF":3.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safety assurance for automated systems in transport: A collective case study of real-world fatal crashes","authors":"Stuart Ballingall,&nbsp;Majid Sarvi,&nbsp;Peter Sweatman","doi":"10.1016/j.jsr.2024.11.008","DOIUrl":"10.1016/j.jsr.2024.11.008","url":null,"abstract":"<div><div><em>Introduction</em>: Traditional vehicle safety assurance frameworks are challenged by Automated Driving Systems (ADSs) that enable dynamic driving tasks to be performed without active involvement of a human driver. Further, an ADS’s driving functionality can be changed during in-service operation, using software updates developed using Machine Learning (ML). Learnings from real-world cases will be a key input to reforming current regulatory frameworks to assure ADS safety. However, ADSs are yet to be deployed in mass volumes, and limited data are available regarding their in-service safety performance. <em>Method:</em> To overcome these limitations, a collective case study was undertaken, drawing upon three relevant real-world cases involving automated control systems that were a causative factor in major transport safety incidents. <em>Results:</em> A range of findings were identified, which informed recommendations for reform. The study found some assurance processes, decisions and oversight were not commensurate with risk or safety integrity levels, including a lack of independence with reviews and approvals for safety–critical system components. Two cases were also impacted by conflict or bias with regulatory approvals. Other commonalities included a lack of safeguards to ensure systems were not operated outside their design domain, and a lack of system redundancy to ensure safe operation if a system component fails. Further, the identification and validation of system responses to scenarios that could be encountered within design domain boundaries was lacking. For the two cases in which safety–critical functionality was developed using ML, it’s concerning no regulator reports provided detailed findings regarding the role of ML models, algorithms, or training data.</div></div>","PeriodicalId":48224,"journal":{"name":"Journal of Safety Research","volume":"92 ","pages":"Pages 27-39"},"PeriodicalIF":3.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}