Driver Risk Research Articles

Reducing the risk of droplet inhalation for taxi drivers by regulating the air supply position according to the exhalation source

The environment inside taxis is relatively closed, making it easy for the drivers to contact with droplets exhaled by the passengers and creating a high risk of respiratory diseases. To study the diffusion of droplets in taxis, the numerical simulation was performed based on gas–solid two–phase flow. The renormalization-group k–epsilon (RNG k–ε) turbulence model and the discrete phase model were used to study the transmission characteristics of droplets with different exhalation sources, and the air-supply positions were chosen to reduce the risk of driver infection from droplet inhalation. Based on the likelihood–exposure–consequence (LEC) method, the cumulative inhalation ratio of droplets was used to assess drivers' infection risk during working. The results show that, the driver's infection risk is least when the passenger sits in the front seat, reducing the risk by 32%–87%. Furthermore, the risk is reduced by 38%–85% when the air supply is changed from two-sided to central. Therefore, the combination that minimizes the risk of inhalation infection for taxi drivers is the passenger sitting in the front seat and the air being supplied centrally in the taxi, which can provide a reference for improving the car layout and ventilation system of the taxi.

Exposure and health risk assessment of volatile organic compounds among drivers and passengers in long-distance buses

Exposure to volatile organic compounds (VOCs) such as benzene, toluene, ethylbenzene, xylene, and formaldehyde from long-distance buses has been reported to adversely affect human health. This study investigates the concentrations of these five VOCs and evaluates their health risks to drivers and passengers on board. Ten trips from Taipei to Taichung were performed during the warm and cold seasons of 2021–2022. Two locations inside the bus were established to collect air samples by a 6-liter canister for drivers and passengers. Exposure concentrations of benzene, toluene, ethylbenzene, and xylene were analyzed via gas chromatography with a flame ionization detector and the formaldehyde concentration was monitored using a formaldehyde meter. Subsequently, a Monte Carlo simulation was conducted to evaluate the carcinogenic and non-carcinogenic risks of the five VOCs. Formaldehyde emerged as the highest detected compound (9.06 ± 3.77 μg/m3), followed by toluene (median: 6.11 μg/m3; range: 3.86–14.69 μg/m3). In particular, formaldehyde was identified to have the significantly higher concentration during non-rush hours (10.67 ± 3.21 μg/m3) than that during rush hours (7.45 ± 3.41 μg/m3) and during the warm season (10.71 ± 2.97 μg/m3) compared with that during the cold season (7.41 ± 4.26 μg/m3). Regarding non-carcinogenic risks to drivers and passengers, the chronic hazard indices for these five VOCs were under 1 to indicate an acceptable risk. In terms of carcinogenic risk, the median risks of benzene and formaldehyde for drivers were 2.88 × 10−6 (95% confidence interval [CI]: 2.11 × 10−6 – 5.13 × 10−6) and 1.91 × 10−6 (95% CI: 4.54 × 10−7 – 3.44 × 10−6), respectively. In contrast, the median carcinogenic risks of benzene and formaldehyde for passengers were less than 1 × 10−6 to present an acceptable risk. This study suggests that benzene and formaldehyde may present carcinogenic risks for drivers. Moreover, the non-carcinogenic risk for drivers and passengers is deemed acceptable. We recommended that the ventilation frequency be increased to mitigate exposure to VOCs in long-distance buses.

A maneuver indicator and ensemble learning-based risky driver recognition approach for highway merging areas

Abstract Due to the complex traffic characteristics in highway merging areas, drivers tend to exhibit high-risk driving behaviors. To address the characteristics of driving behavior in highway merging areas, we have developed a real-time identification model for risky drivers by combining a driver risk level labeling method with load balancing-ensemble learning (LB-EL). In this paper, we explore four types of maneuver indicator indexes (MIIs)—acute direction, stomp pedal, dangerous following, and dangerous lane changing—that can describe the negative behaviors of both individual vehicles and vehicle platoons in highway merging areas. To quantize the label driver risk level, we use the interquartile range (IQR) method and Criteria Importance Though Intercriteria Correlation (CRITIC), while we evaluate the reliability of the MII using spatial analysis. Furthermore, we balance the dataset using three load balancing (LB) algorithms and create nine ensemble strategies by pairing adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM) with the three LB algorithms. Finally, we validate the proposed model using trajectory data extracted from UAV videos. The results indicate that the distribution laws of risky driving behaviors in the acute direction and stomp pedal show a high degree of similarity and good matching with the distribution laws of traffic conflict points in existing research. Moreover, the SMOTE-LGBM ensemble model achieves the best performance, reaching an accuracy rate of 93.4% and a recall rate of 92.1%, which demonstrates the validity of our proposed model. This model can be widely applied to recognize risky drivers in video-based surveillance systems.

How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data

BackgroundTaxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability.MethodsThis study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers’ exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers’ activities.ResultsThe taxi drivers’ weekday and weekend 24-h PM2.5 exposure was 83.60 μg/m3 and 55.62 μg/m3 respectively, 3.4 and 2.2 times than the WHO’s recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the “Inner Ring Road”, while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the “Third Ring Road”.ConclusionThese findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.

Biochar from recovered cellulose as new admixture in constructed wetlands for micropollutant removal: A circular approach

This study aimed to evaluate the suitability of biochar produced by pyrolysis from recovered wastewater cellulose and activated biologically as an admixture in Constructed Wetlands (CWs) when applied as a post-treatment step to remove micropollutants (MPs) from municipal wastewater effluent. Two planted vertical flow mesocosm CWs with cellulose-based admixtures of different origins (plant residue and recovered toilet paper) were fed with a municipal wastewater effluent representative for rural catchments. The results showed an average MPs elimination of 89.1 % for the activated biochar produced from recovered cellulose when 15 relevant compounds are considered and a reduction of the risk from compounds cocktail below the maximum acceptable level having diclofenac, carbamazepine, PFOS, ciprofloxacin and clarithromycin as main risk drivers (Risk Quotient > 1). The implementation of a circular approach to reduce MPs was finally conducted for the Blies catchment (Saarland region in Germany) characterized by low population density and small, sensitive water bodies. This approach demonstrates the feasibility of combining cellulose recovery with a fine sieve in large wastewater treatment plants (WWTPs) and providing biochar produced from recovered cellulose as an admixture to small WWTP where CW is an affordable solution for MP mitigation.

Vehicle Identity Anonymity Framework with Accountability for VANET Environment

<p>In recent years, there has been rapid development in vehicle safety technology, with the emergence of various active safety systems including blind spot information systems, adaptive cruise control, and front collision warning systems. Simultaneously, car manufacturers and technology companies are actively exploring technologies in the realm of autonomous driving. To facilitate such applications, vehicles are required to communicate with each other, exchanging vital information such as position, speed, and acceleration. However, this exchange of information poses a potential risk of drivers’ personal data being compromised. For safety purposes, vehicles must undergo appropriate authentication before engaging in communication with other vehicles. Ensuring this authentication process maintains the anonymity of the vehicles is crucial. Yet, striking a balance between protecting vehicle anonymity and enabling vehicle identification when necessary remains a challenging issue. This paper introduces a multi-tier Vehicular Ad-Hoc Network (VANET) framework designed to uphold the conditional anonymity and traceability of vehicles. The implementation of a group signature mechanism facilitates anonymous authentication, thereby enabling the realization of conditional anonymity and traceability. Moreover, comprehensive simulations and security analyses were conducted to validate the effectiveness of the proposed framework, demonstrating its efficiency while incorporating robust safety considerations.</p> <p> </p>

Reducing crash risk for young drivers: Protocol for a pragmatic randomised controlled trial to improve young driver sleep

BackgroundRoad trauma is a leading cause of death and disability for young Australians (15–24 years). Young adults are overrepresented in crashes due to sleepiness, with two-thirds of their fatal crashes attributed to sleepy driving. This trial aims to examine the effectiveness of a sleep extension and education program for improved road safety in young adults. MethodsYoung adults aged 18–24 years (n = 210) will be recruited for a pragmatic randomised controlled trial employing a placebo-controlled, parallel-groups design. The intervention group will undergo sleep extension and receive education on sleep, whereas the placebo control group will be provided with information about diet and nutrition. The primary outcomes of habitual sleep and on-road driving performance will be assessed via actigraphy and in-vehicle accelerometery. A range of secondary outcomes including driving behaviours (driving simulator), sleep (diaries and questionnaire) and socio-emotional measures will be assessed. DiscussionSleep is a modifiable factor that may reduce the risk of sleepiness-related crashes. Modifying sleep behaviour could potentially help to reduce the risk of young driver sleepiness-related crashes. This randomised control trial will objectively assess the efficacy of implementing sleep behaviour manipulation and education on reducing crash risk in young adult drivers.

Analyzing road user risk perception among Indian drivers on varying curve geometry: A structural equation modeling approach

Driver risk perception ability depends on how they perceive and interpret information from different sources to safely and comfortably operate their vehicles based on the highway features ahead. The present study is divided into two parts to review these aspects: (a) identifying the factors that influence driver risk-taking behavior, and (b) evaluating the soundness of the identified factors by comparing them with quantitative risk perception. For that, 1075 user perception data were collected using a developed questionnaire form through face-to-face interviews. A questionnaire form consists of a total of 33 questions/variables. After applying the factor reduction techniques, namely principal component analysis, the most significant variables (19 questions/variables) influencing the user’s perception are identified. Then, considering these significant variables, five separate risk perception models were developed using the Structural Equation Modeling technique for five varying curve geometries. The results revealed that the user’s risk perception changes for the same user for varying curve geometry. It is also identified that male drivers have more risky behavior (lower risk perception) than female drivers. Besides, Garrett’s ranking technique is also adopted to rank the user risk perception for the varying curve geometry to identify the dangerous curves by assigning the rank. Lastly, to check the feasibility of the results, the risk perception score is also compared with the quantitative risk score. Based on the study results, it is concluded that the study findings will be helpful for highway planners and authorities to introduce some safety measures to alert users of the potential hazards on the roadside and increase their capacity for taking risks on complex curves; hence, the likelihood of crashes can be minimized.

Enhancing Privacy in the Internet of Vehicles via Hyperelliptic Curve Cryptography

The Internet of Things (IoT) is a technological paradigm that has gained significant momentum the last decade and, among other features, enables the development of intelligent and interoperable device networks. In this respect, it has triggered the creation and evolution of vehicular ad-hoc networks (VANETs), which are initially implemented in order to guarantee the safety of drivers and the avoidance of traffic accidents. The drawback is that this fast evolution comes with serious concerns in terms of the privacy of users, while the population of attackers or entities that try to eavesdrop and intercept information has significantly increased. This imposes a serious risk for drivers moving across a Smart City. The research presented in this paper aims to evaluate privacy protection mechanisms in VANET environments, based on the efficiency and security level they ensure, considering the fact that VANETs provide limited resources to users/drivers. Moreover, the usage of elliptic curve cryptography in reduced resources environments is discussed. Finally, this paper compares the performance of three cryptographic algorithms, elliptic curve cryptography (ECC), hyperelliptic curve cryptography genus 2 (HECC-2) and HECC genus 3 (HECC-3), employed for an efficient authentication and safe message transmission mechanism in VANETs, aimed at reaching conclusions related to the implementation of each cryptographic scheme in this specific application area. The evaluation results indicate that ECC supersedes HECC-2 and HECC-3 in most metrics. However, HECC-2 and HECC-3 demonstrate better responses than ECC does in selected energy metrics. Overall, it is observed that HECC algorithms are not yet mature enough to compete with ECC. This is due to the fact that the research community has not sufficiently progressed toward the optimization of HECC, and moreover, HECC builds on quite complex mathematics. There are indications, however, that once HECC curves are indeed optimized, HECC will outperform ECC in speed as well as in other metrics, sinceHECC-2 and HECC-3 use a significantly smaller key size with the same level of security as that of ECC.

Integrating visual large language model and reasoning chain for driver behavior analysis and risk assessment

Driver behavior is a critical factor in driving safety, making the development of sophisticated distraction classification methods essential. Our study presents a Distracted Driving Classification (DDC) approach utilizing a visual Large Language Model (LLM), named the Distracted Driving Language Model (DDLM). The DDLM introduces whole-body human pose estimation to isolate and analyze key postural features—head, right hand, and left hand—for precise behavior classification and better interpretability. Recognizing the inherent limitations of LLMs, particularly their lack of logical reasoning abilities, we have integrated a reasoning chain framework within the DDLM, allowing it to generate clear, reasoned explanations for its assessments. Tailored specifically with relevant data, the DDLM demonstrates enhanced performance, providing detailed, context-aware evaluations of driver behaviors and corresponding risk levels. Notably outperforming standard models in both zero-shot and few-shot learning scenarios, as evidenced by tests on the 100-Driver dataset, the DDLM stands out as an advanced tool that promises significant contributions to driving safety by accurately detecting and analyzing driving distractions.

Geometric field model of driver’s perceived risk for safe and human-like trajectory planning

Autonomous driving systems must provide safe, predictable, and consistent behaviors across diverse scenarios to enhance user experience. The geometric driver risk field (GDRF) method is proposed for human-like, interpretable, and fast risk estimations. The method models the driver’s subjectively perceived risk with fields formed by sets of geometric shapes. Unlike current safety measures adopted in trajectory planning algorithms, the proposed method aligns with driver risk perception properties. It provides risk estimations based on probable future vehicle states and the risk consequences at different locations. Compared with existing potential field methods for risk estimations, the mathematical form of the method has a high calculation efficiency while keeping the ability to model the details of the risk information. Influences of different obstacle types, multiple regions with different risk levels, and the relative motions between vehicles are modeled in a unified manner. The graph-search and optimization trajectory planning algorithms with a hierarchy framework are also designed to attain desired risk objectives in the planned trajectories. The framework mitigates the possible suboptimality problems in previous hierarchy trajectory planning algorithms. The proposed GDRF method and planning algorithms are examined in driving scenarios with different traffic states and road geometries. Results showed that the proposed risk estimation method outperforms previous risk-oriented potential field methods by more human-like and safer trajectories with evidence from existing studies and safety measures like time headway (THW), time to collision (TTC), and time to lane crossing (TLC). It also provides more consistent behaviors while maintaining a short computation time. The integration of the proposed risk estimation method and trajectory planning algorithms holds great potential for improving the safety and user experience of autonomous driving systems in real-world scenarios.

Human–Machine Shared Lateral Control Strategy for Intelligent Vehicles Based on Human Driver Risk Perception Reliability

Human–Machine Shared Lateral Control Strategy for Intelligent Vehicles Based on Human Driver Risk Perception Reliability

Metabolic syndrome and the 10-year cardiovascular disease risk in urban public transport drivers in the context of occupational risks and workloads

The specific risks and workloads of urban public transport drivers create a net for the development of several health issues. It is worth mentioning the impact of the work regime on this type of workers’ metabolic and cardiovascular health. This paper represents a sequence from an ample study that has followed the effects of this professional activity on worker’s health and work capacity and has established prevention / reduction of identified risk factors measures. The health state analysis was realized through a prevalence study on three cohorts (bus, tram, trolleybus drivers). The prevalence of metabolic syndrome was the highest in the group of the bus drivers, 16.7%. The 10-year fatal cardiovascular disease risk in both the increased risk category and very increased risk category was the highest in the trolleybus drivers’ cohort, 29%, respectively 6%. Major contributing pathology, such as hypertension and type II diabetes, starts in the 36-45 years of age group regardless of gender in all three cohorts signalizing an early wear and is already important in the 46-55 years of age group.

The Carpal Tunnel Syndrome and Ergonomic Risk Factor Profile in Online Motorcycle Taxi Drivers (ojol) in Jakarta

Introduction: The increasing need for online motorcycle taxi driver (ojol) transportation continues to increase in Jakarta. This increases the activity of motorcycle taxi drivers, which can cause Carpal tunnel syndrome risks due to the awkward posture of the wrist while riding the motorcycle (the wrist posture is too long on the motorcycle handlebars with repetitive motion), which will affect the nerves and blood supply to the wrist and hand. This study aims to determine the risk of the motorcycle taxi driver’s upper limb using Rapid Upper Limb Assessment (RULA) and Carpal tunnel syndrome risk factor using the Boston Carpal Tunnel Syndrome Questionnaire (BTCSQ). Method: This research used a cross-sectional study approach with 97 respondents online motorcycle taxi drivers. The respondents were collected based on the activity of online motorcycle taxi drivers who were in or passing through the data collection location and willing to take part in the Rapid Upper Limb Assessment (RULA) and the Boston Carpal Tunnel Syndrome Questionnaire (BTCSQ). The RULA assessment and analysis were performed and supported by video (recording from a cycle of the activity while the respondents were riding the motorcycles). The respondents’ criteria were an online motorcycle taxi driver with a minimum of 1 year of working experience, at least 3 hours working duration per day, and not clinically suffering from rheumatoid arthritis and wrist problems. This study has been approved by Universitas Kristen Krida Wacana ethical committee. Result: The results of BCTSQ and RULA in online motorcycle taxi drivers have found that there’s mild–moderate risk respectively in Jakarta. In RULA, it was found that there were 87.6% of respondents were reported at moderate risk which had an impact on musculoskeletal problems, especially the upper limb. In the BCTSQ, the result showed that there were mild complaints in 38.1% of respondents regarding the wrist which was at risk of developing carpal tunnel syndrome. Discussion: With RULA results, in the form of moderate risk, Further ergonomics investigation and assessment are needed to find out further risk factors, mitigation plans, and best improvements to reduce the risks. The occurrence of mild complaints in the wrist based on BCTSQ requires further clinical examination of carpal tunnel syndrome to reduce the incidence of CTS cases in online motorcycle taxi drivers. Conclusion: This research’s result has found mild-moderate risk factors from the activity and working posture among online motorcycle taxi drivers in Jakarta. Further investigation and research ergonomically are needed.

Predation and the Disproportionate Risk of Driver’s License Suspensions in Economically and Racially Marginalized Communities

Millions of people have their driver’s licenses suspended each year when they do not pay traffic fines or appear in court, with significant and compounding consequences for their lives. The increased use of fines and fees has been likened to a predatory system that exploits the most vulnerable groups to fill state and local budget gaps. States have begun limiting suspensions for unpaid traffic tickets out of economic and racial equity concerns, but many still sanction drivers for failing to appear in court or comply with other regulations. Merging the full universe of New York Department of Motor Vehicles suspension records in 2017 with the American Community Survey at the zip code level, we find that Black, Hispanic, and economically disadvantaged communities were disproportionately impacted by both nonpayment and noncompliance suspensions. Predictive models suggest that reforms ending suspensions for both the failure to appear in traffic court and the failure to pay traffic tickets could substantially reduce these disparities.

Modeling Human-Like Driving Behavior at a Signal Intersection Based on Driver Risk Field Model

Modeling Human-Like Driving Behavior at a Signal Intersection Based on Driver Risk Field Model

THE RELATIONSHIP OF DRIVER BEHAVIOR AND RISK OF ACCIDENTS IN COAL TRANSPORT TRUCK DRIVERS

The death rate caused by road traffic accidents in Indonesia is still quite high, where some of these accidents involve trucking vehicles. The aims of this research was to determine the relationship between behavior and the risk of accidents in coal transport truck drivers at PT. X. The type of research used is analytical descriptive with a cross sectional approach. The study population was coal hauling truck drivers working at PT. X with a total sampling of 44 respondents. Based on the results of the study, it was found that most (75.0%) drivers have good knowledge; More than half (54.4%) of drivers are supportive of driving behavior; More than half of drivers (63.6%) have good measures against the risk of workplace accidents in coal truck drivers; And more than half of drivers (59.1%) are at risk of workplace accidents. There was no significant relationship between driver behavior, knowledge, attitudes and actions towards occupational accident risk in coal truck drivers. Insignificant research results cannot reduce the importance of risk control in the company because accidents risk death. To reduce the accident rate among truck drivers, it is necessary to provide facilities to increase intensive education and safety training.

Exploring the occupational fatigue risk of short-haul truck drivers: Effects of sleep pattern, driving task, and time-on-task on driving behavior and eye-motion metrics

Driver fatigue is the leading cause of truck-related accidents. The most significant occupational fatigue factors among short-haul truck drivers are sleep patterns, the round-trip driving task, and the time-on-task. However, the underlying mechanisms of these influential factors remain unclear. This study aims to explore the interactive effects of sleep patterns, driving task, and time-on-task on driving behavior and eye-motion metrics among short-haul truck drivers. We obtained test data from eleven professional short-haul truck drivers, with each driver participating in a three-day test under the conditions of two driving tasks and three different sleep patterns. We applied three-way repeated-measure ANOVA and non-parametric tests to analyze the data. The results reveal that: (1) violation of sleep-related legal requirements, insufficient sleep, and unreasonable time-on-task can have negative effects on short-haul truck drivers' vigilance and driving performance; (2) both driving task and sleep pattern contribute to driver fatigue, and the interaction of time-on-task and sleep pattern exacerbates driver fatigue more than the effects of any single factor alone; and (3) short-haul truck drivers who are sleep deprived exhibit short periods of controlled compensatory behavior during the outbound task, and sleepiness is more prevalent during the inbound task compared to the outbound task due to the monotony and low workload of the driving process. These findings provide theoretical and practical guidance for transportation industry managers to strengthen company-wide fatigue-related regulations, ensure adequate sleep for drivers via regulations, and optimize work schedules to improve safety outcomes of short-haul truck drivers.

High cell-free DNA is associated with disease progression, inflammasome activation and elevated levels of inflammasome-related cytokine IL-18 in patients with myelofibrosis.

Myelofibrosis (MF) is a clonal hematopoietic stem cell disorder classified among chronic myeloproliferative neoplasms, characterized by exacerbated myeloid and megakaryocytic proliferation and bone marrow fibrosis. It is induced by driver (JAK2/CALR/MPL) and high molecular risk mutations coupled to a sustained inflammatory state that contributes to disease pathogenesis. Patient outcome is determined by stratification into risk groups and refinement of current prognostic systems may help individualize treatment decisions. Circulating cell-free (cf)DNA comprises short fragments of double-stranded DNA, which promotes inflammation by stimulating several pathways, including inflammasome activation, which is responsible for IL-1β and IL-18 maturation and release. In this work, we assessed the contribution of cfDNA as a marker of disease progression and mediator of inflammation in MF. cfDNA was increased in MF patients and higher levels were associated with adverse clinical outcome, a high-risk molecular profile, advanced disease stages and inferior overall survival, indicating its potential value as a prognostic marker. Cell-free DNA levels correlated with tumor burden parameters and markers of systemic inflammation. To mimic the effects of cfDNA, monocytes were stimulated with poly(dA:dT), a synthetic double-stranded DNA. Following stimulation, patient monocytes released higher amounts of inflammasome-processed cytokine, IL-18 to the culture supernatant, reflecting enhanced inflammasome function. Despite overexpression of cytosolic DNA inflammasome sensor AIM2, IL-18 release from MF monocytes was shown to rely mainly on the NLRP3 inflammasome, as it was prevented by NLRP3-specific inhibitor MCC950. Circulating IL-18 levels were increased in MF plasma, reflecting in vivo inflammasome activation, and highlighting the previously unrecognized involvement of this cytokine in MF cytokine network. Monocyte counts were higher in patients and showed a trend towards correlation with IL-18 levels, suggesting monocytes represent a source of circulating IL-18. The close correlation shown between IL-18 and cfDNA levels, together with the finding of enhanced DNA-triggered IL-18 release from monocytes, suggest that cfDNA promotes inflammation, at least in part, through inflammasome activation. This work highlights cfDNA, the inflammasome and IL-18 as additional players in the complex inflammatory circuit that fosters MF progression, potentially providing new therapeutic targets.

Preface to the Special Issue on Assessment and Effectiveness of Driver Monitoring Systems.

With vehicle automation becoming more commonplace, the role of the human driver is shifting from that of system operator to that of system supervisor. With this shift comes the risk of drivers becoming more disengaged from the task of supervising the system functioning, thus increasing the need for technology to keep drivers alert. This special issue includes the most up-to-date research on how drivers use vehicle automation, and the safety risks it may pose. It also investigates the accuracy that driver monitoring systems have in detecting conditions like driver distraction and drowsiness, and explores ways future drivers may respond to the broader introduction of this technology on passenger vehicles.