Driving Conditions Research Articles

Identification of Driver Status Hazard Level and the System.

According to the survey statistics, most traffic accidents are caused by the driver's behavior and status irregularities. Because there is no multi-level dangerous state grading system at home and abroad, this paper proposes a complex state grading system for real-time detection and dynamic tracking of the driver's state. The system uses OpenMV as the acquisition camera combined with the cradle head tracking system to collect the driver's current driving image in real-time dynamically, combines the YOLOX algorithm with the OpenPose algorithm to judge the driver's dangerous driving behavior by detecting unsafe objects in the cab and the driver's posture, and combines the improved Retinaface face detection algorithm with the Dlib feature-point algorithm to discriminate the fatigue driving state of the driver. The experimental results show that the accuracy of the three driver danger levels (R1, R2, and R3) obtained by the proposed system reaches 95.8%, 94.5%, and 96.3%, respectively. The experimental results of this system have a specific practical significance in driver-distracted driving warnings.

자율주행 환경에서 비운전과제 유형과 도로 혼잡도에 따른 청년 및 고령운전자의 제어권 인수와 운전수행 차이

The take-over time (TOT) and driving performances of young and old drivers according to the types of non-driving related tasks (NDRTs) and road complexity were compared by using 3-level automated driving simulation. NDRTs were divided into four types according to the stimulus modality and response modality: visual stimulus-verbal response(V-V), visual stimulus-manual response(V-M), visual/auditory stimulus-manual response(AV-M), and auditory stimulus-verbal response(A-V). Control condition where NDRT was not performed was also included. The participant group was divided into a young driver group and an elderly driver group, and the road condition was divided into a no-traffic condition and a heavy traffic condition based on the surrounding traffic volume. The main results of this study are as follows. First, TOT was the slowest in the visual stimulus-manual response condition, but the fastest in the auditory stimulus-verbal response condition. In particular, this tendency was more prominent in the elderly driver group than in the younger driver group and in the heavy traffic condition than in the no-traffic condition. Second, the main effect of the NDRT type was not significant in the driving performance measurements including steering wheel manipualtion variability and brake input after taking over control, but greater steering wheel manipulation variability and brake input were observed in the elderly driver group compared to the young driver group and in the heavy traffic condition compared to the no-traffic condition. These results suggest that the driver's age, NDRT type, and road conditions can have an integrated effect on TOT and driving performance after taking over control in autonomous driving situations.

Barriers and Facilitators of People with and without Disabilities before and after Autonomous Shuttle Exposure

The deployment of autonomous shuttles (ASs) holds health and safety benefits for people with and without disabilities. Transportation is critical in helping people with disabilities (PWDs) access health care, services, and jobs, but the current transportation system has not afforded them ubiquitous access. To understand the acceptance of ASs, we (1) quantified PWDs’ (N = 42) perceptions before and after riding in an AS (Level 4) and (2) developed a model of facilitators and barriers from 143 participants. For Objective 1, after riding in the AS, PWDs (n = 42) expressed increased Intention to Use (p < 0.001) and Acceptance (p < 0.001), and decreased Perceived Barriers (p < 0.001), compared with baseline. For Objective 2, four multiple linear regression models were conducted to predict the outcomes for Intention to Use, Perceived Barriers, Well-being, and Acceptance among all participants (N = 143). The results indicated that optimism and ease of use negatively predicted Perceived Barriers and positively predicted Intention to Use, Well-being, and Acceptance. Driving status (i.e., active driver) negatively predicted Intention to Use, Well-being, and Acceptance. Predictors of user Acceptance included optimism, perceived ease of use, driver status, and race/ethnicity—with 30.7% of the variance in Acceptance explained. We also recommended deployment strategies to project stakeholders.

RLY-4008, the First Highly Selective FGFR2 Inhibitor with Activity across FGFR2 Alterations and Resistance Mutations.

Patients with FGFR2-driven cancers derive limited benefit from pan-FGFRi due to multiple FGFR1-4-mediated toxicities and acquired FGFR2 resistance mutations. RLY-4008 is a highly selective FGFR2 inhibitor that targets primary alterations and resistance mutations and induces tumor regression while sparing other FGFRs, suggesting it may have broad therapeutic potential. See related commentary by Tripathi et al., p. 1964. This article is featured in Selected Articles from This Issue, p. 1949.

Comparative Analysis of Gender-Based Differences in NSCLC between Europe and Asia

Background: The demographic makeup of lung cancer has shifted over the last decades. Females are more likely to be non-smokers, diagnosed at a younger age, earlier stage, have adenocarcinoma and a family history of cancer. Objectives: the primary objective is to analyze the gender-based differences with regards to clinicopathologic features in Europe and Asia. Methods: This study was based on Oncology Dynamics, using a physician questionnaire. In Q1 2022, IQVIA engaged 1,181 physicians from Europe and 1,402 from Asia. The following items were analyzed: gender, age, smoking status, clinical stage, oncogenic driver status, location of metastases, ECOG, and comorbidities. Results: A total of 18217 cases were analyzed; 7006 (38.5%) were females (F) and 11211 (61.5%) males (M). The percentage of Asian female non-smokers was substantially higher compared to males and European population. Females had more adenocarcinoma and less squamous cell carcinoma cases on both continents. Most patients had metastatic disease at diagnosis. However, in Asia there was a higher proportion of localized disease compared to Europe regardless of sex. The frequency of brain metastases in Asian females was significantly higher (20%) than in Asian males (14%) or the European population (F: 16%, M: 10%). EGFR mutation was the most common oncogenic driver in both genders, but its prevalence was higher in Asian females. Conclusion: This research highlights the differences in cancer staging between Asian and European patients and the importance of gender-based differences suggesting that lung cancer may be increasingly considered a distinct disease in females.

Identification of prognostic and driver gene mutations in acute myeloid leukemia by a bioinformatics analysis.

The representative gene mutation in the prognostic groups of acute myeloid leukemia (AML) patients is not yet known. The purpose of this study is to identify representative mutations that can help physicians better predict patient prognosis and thus develop better treatment plans. The Cancer Genome Atlas (TCGA) database was queried for clinical and genetic information, and individuals with AML were classified into 3 groups based on their AML Cancer and Leukemia Group B (CALGB) cytogenetic risk category. Each group's differentially mutated genes (DMGs) were evaluated. Simultaneously, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to assess the function of DMGs within the 3 distinct groups. We used the driver status and protein impact of DMGs as additional filters to narrow down the list of significant genes. Cox regression analysis was used to examine the survival features of gene mutations in these genes. A cohort of 197 AML patients were divided into 3 groups according to their prognostic subtype: favorable (n=38), intermediate (n=116) and poor (n=43). There were significant differences in age and tumor metastasis rates among the three groups of patients. Patients in the favorable group had the highest rate of tumor metastasis. Different prognosis groups' DMGs were detected. The DMGs were examined for the driver and harmful mutations. We considered the DMGs that had driver and harmful mutations and that affected the survival outcomes in the prognostic groups as the key gene mutations. The group with a favorable prognosis carried specific gene mutations for KIT and WT1. The intermediate prognostic group contained mutations in the genes IDH2, NRAS, NPM1, FLT3, RUNX1, DNMT1A, and MUC16. In the group with a poor prognosis, the representative genes were KRAS, TP53, IDH1, IDH2, and DNMT3A, with TP53 mutations substantially correlated with overall patient survival. We performed the systemic analysis of the gene mutation in patients with AML and identified representative and driver mutations between the prognostic group. identification of representative and driver mutations between the prognostic group can help predict the prognosis of patients with AML and guide treatment decisions.

Real-Time Driver Drowsiness Detection Using Images

Abstract: The suggested technology intends to reduce the amount of accidents caused by driver sleepiness and exhaustion, hence increasing transportation safety. In recent years, this has been a prevalent cause of accidents. Several expressions and body motions, including exhaustion in the eyes and yawning, are seen as symptoms of sleepiness and fatigue in drivers. These characteristics indicate that the driver's condition is poor. The EAR (Eye Aspect Ratio) calculates the distance ratio between horizontal and vertical eye landmarks, which is essential for detecting sleepiness. A YAWN value is calculated for yawn detection utilising the distance between the lower and upper lips, and the distance is compared to a threshold value. We installed an eSpeak module (text to speech synthesiser) to provide suitable voice notifications when the driver becomes tired or yawns. The suggested method is intended to reduce the number of accidents and to contribute to technology in order to avoid fatalities caused by road accidents.

Association between car driving and successful ageing. A cross sectional study on the "S.AGES" cohort.

Assessing successful ageing (SA) is essential to identify modifiable factors in order to enforce health promotion and prevention actions. SA comprises 3 dimensions: an active engagement with life, a low probability of disease and disease-related disability, and a high cognitive and physical functional capacity. Driving seems to be linked to SA as it is a mean to preserve social interactions and requires preserved functional and cognitive status. This study aims to investigate whether driving status can be considered a proxy of SA, by describing determinants associated with driving status in the 65+. This cross-sectional study is ancillary to the S.AGES (Sujets AGÉS-Aged Subjects) study, an observational prospective cohort study which included patients suffering from chronic pain, type-2 diabetes mellitus or atrial fibrillation from 2009 to 2014. SA was defined by the success of three dimensions: physiological comprised of comorbidity and autonomy scores, psychological comprised of cognitive status and emotional state, and a social dimension. 2,098 patients were included of whom 1,226 (58.4%) reported being drivers. 351/2,092 (16.7%) were classified as successful agers: 292/1,266 (23.8%) in the driver group vs. 59/872 (6.8%) in the non-driver group; p < .001. In the final logistic model, after adjustment for relevant variables, SA was associated with driver status OR 1.94 [1.36-2.77]. Driving may be considered as a proxy to SA: it reflects elders' independence, cognitive ability and a means to maintain social interactions. To preserve their mobility and enable them to achieve SA, regular screening of driving skills, specific rehabilitation programs are needed. Moreover development and communication on special transports services, communal rides or even driverless car to avoid apprehension around older adults driving could be solutions.

Driver distraction detection via multi‐scale domain adaptation network

AbstractDistracted driving is the leading cause of road traffic accidents. It is essential to monitor the driver's status to avoid traffic accidents caused by distracted driving. Current research on detecting distracting behaviours focuses on analysing image features using convolutional neural networks (CNNs). However, the generalisation ability of the current distracted driving models is limited. This paper aims to improve the generalisation ability of distracted driving models that are affected by factors such as the driver himself, the background, the monitoring angle, and so on. A new driver distraction detection method, which is referred to as multi‐scale domain adaptation network (MSDAN), was proposed to improve model adaptability. The method consists of three stages: first, multi‐scale convolution was introduced to build a new backbone to accommodate better the valuable feature of the target on different scales. Secondly, the authors designed the domain adaptation network to improve the model's adaptability to the difference in data sources through adversarial training. Finally, dropout is added to the fully connected layer to increase the model's generalisation ability. The comparison results on the large‐scale driver distraction detection dataset show that the authors’ method can accurately detect driver distraction and has good generalisation performance, with an accuracy improvement in the cross‐driver and cross‐dataset experiments.

Driver Drowsiness Detection Using Face Recognition

Abstract: Every year, hundreds of people die in traffic accidents all over the world. According to national statistics, human factors play a determining role in 90-95% of car accidents in Iran. Globally, 25% of accidents are caused by driver fatigue and around 60% of road accidents result in death or serious injury. In a National Transportation Research Institute (NTSRB) study of 107 randomly selected car crashes, fatigue accounted for 58% of all crashes. Drowsy driving is a major factor in serious road accidents that claim thousands of lives every year. The application of intelligent systems in automobiles has developed considerably in recent years. These systems use wireless sensor networks to monitor and transmit car and driver status. Smart cars that use software technology to control engine speed, steering, transmission, braking, etc. Management and control, the quality of management has been greatly improved. Ad hoc networks are the first systems to develop automatic navigation in cars. A notable weakness of these systems is that their response to changes in the environment is not in real time. This is especially important in driving, where time is a key factor in a driver's decision. On the other hand, another way to check driver fatigue is to monitor the physical condition and facial expression of the driver, which cannot be processed and transmitted accurately enough by wireless sensor networks. Driver fatigue is a major factor in a large number of road accidents. Recent statistics estimate that 1,200 deaths and 76,000 injuries are attributable to fatigue-related accidents each year.

Automated Driver’s Condition Monitoring to Prevent Dangerous Situations

The purpose of the research is the use of modern simulation tools to determine the driver’s condition and the software development of the algorithm used to monitor and alarm the driver about the dangerous condition. A review of studies on this topic in recent years is given. The choice of modeling tools and the use of the apparatus of fuzzy logic are analyzed and substantiated. It is given the rationale for the development of a Kazakhstani system for monitoring driver behavior using a smartphone. As a result of the research, a software product was developed in the C# programming language. The correctness of the algorithm for recognizing the driver's condition has been tested, ways to improve the development and its use in the experiment directly in the vehicle cabin have been determined.

Comparative analysis of influencing factors on pedestrian road accidents

Road accident data includes detailed information about incidents that occurred, such as where they happened, the severity of the accident, and the number of people on the road at the time. Such information is useful in determining the causes of accidents and developing potential countermeasures. This research aims to determine the factors that contribute to pedestrian fatalities and injuries in traffic accidents. This study examined 150 pedestrian-vehicle accidents that took place between 1990 and 2021 in forty countries. Eleven factors have been identified as the major causes of accidents. The categorical principal component analysis (CATPCA) technique is used to reduce the number of dimensions and identify the elements that contribute to accidents. The eleven variables are classified into three groups: human factors, roadway environment, and vehicle attributes. The study found that car speed, weather, lighting, traffic conditions, area types, accident locations, and road conditions all had a significant impact on pedestrian accidents and fatalities. The findings show that a pedestrian's state (walking, running) and intention significantly increase the risk of serious injuries and death. The analysis of the driver's status suggests that the driver's intentions may also play a role in car accidents.

Smart V2V Communication Based on Li-Fi

In this article, Li-Fi is introduced as a communication system with its modulation techniques, and the entire architecture is explained. The challenges and benefitsof Li-Fi are described with its goal to provide high-speed data transmission being one of its biggest benefits. The project's realization of a V2V communication application, in which the speed of both engines is controlled using Li-Fi, is how the Li-Fi implementation is demonstrated.The data is transmitted from one control unit to another and that controls the speed of the engine. The required system analysis with the block diagrams is also provided. It is therefore concluded that the Li-Fi is used as a communication tool and that both engine speeds are checked. In addition to the above, this framework will identify, and detect the vehicle drivers who drink alcohol and get inside the vehicle for access to drive the vehicle. After detection of the driver's status, this framework sends the details to the concerned authority. Once the concerned authority receives the data about the particular vehicle, it will alert the neighboring vehicle drivers about its current status . The issue of more or heavy traffic during vehicle movement in a particular region is identified. The vehicle speed of the neighboring vehicles is also calculated and if it violates the traffic rules, this proposed framework shall send data to the concerned authority for further action againstthe vehicle driver. The above-said issues are discussed in this paper with the help of Li-Fi technology implemented with the microcontroller-based Arduino.

The factors impacting the use of navigation systems: A study based on the technology acceptance model

Navigation systems are important in daily driving situations. The user experience of using navigation systems can influence drivers’ attitudes towards them and their intentions to use them. The objective of this paper was to identify the mechanisms underlying the use of navigation systems and analyse the psychological factors that influence drivers’ use of navigation systems. The technology acceptance model (TAM) was used to explore the factors impacting navigation system usage, and related factors such as trust and professional driver status were also considered. Using data collected from 372 drivers in China, the study demonstrated that two dimensions of the TAM, perceived usefulness (PU) and perceived ease of use (PEOU), as well as trust, were positively related to the intention to use navigation systems. Meanwhile, these positive associations were mediated by drivers’ attitudes towards navigation systems. Furthermore, professional drivers showed different trends from nonprofessional drivers in terms of the path from PEOU and trust to positive attitudes.

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors.

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver's health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle's central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver's heart condition and vehicular traffic have been found in this analysis.

Розробка алгоритма моніторингу стану водія за допомогою Аndroid-застосунку з метою підвищення рівня активної безпеки

The purpose of the research is the analysis of existing systems for monitoring the driver's condition using an Android mobile application to increase the level of active safety with the development of an algorithm for monitoring the driver's condition. At the same time, such parameters as turns and tilts of the head, duration of eyelid blinking, yawning are taken into account. As a result, the state of drowsiness, impaired attention, and drunkenness of the driver is analyzed. The article analyzes the methods of monitoring the dangerous condition of the driver during the movement of the vehicle. The list of programs used in this case is given. The Drive Safely mobile application was chosen for the research, which is based on monitoring the condition of the driver's eyes and mouth. Based on research, eye PERCLOS should not exceed 28% and mouth PERCLOS should not exceed 30%. An algorithm for recognizing emergency situations in the vehicle cabin based on the image of the driver's eyes and mouth from the front camera of a smartphone has been developed. As a result of the analysis of existing Android applications, an algorithm for recognizing emergency situations in the cabin of the vehicle was developed. The algorithm was obtained based on the image of the smartphone's front camera. This algorithm takes into account all possible dangerous conditions of the driver behind the wheel of the vehicle. As a result of the conducted research, an analysis of the existing systems for monitoring the driver's condition using the Drive Safely mobile Android application was carried out. As a result of the analysis of existing Android applications, an algorithm for recognizing emergency situations in the cabin of the vehicle was developed. The use of this algorithm in the program and the connection of its results to the cloud environment will allow it to self-improve and increase the efficiency of its operation, thus increasing the level of active security.

Smart Edge-Based Driver Drowsiness Detection in Mobile Crowdsourcing

Traffic accidents caused by drowsy drivers represent a crucial threat to public safety. Recent statistics show that drowsy drivers cause an estimated 15.5% of fatal accidents. With the widespread use of mobile devices and roadside units, these accidents can be significantly prevented using a drowsiness detection solution. While several solutions were proposed in the literature, they all fall short of presenting a distributed architecture that can answer the needs of these applications without breaching the driver’s privacy. This paper proposes a two-stage Driver Drowsiness Detection System using smart edge computing. Mobile devices in the car are used to capture and analyze the current condition of the drivers without sharing their data. The smart edge is deployed as a decision-maker where the drowsiness is confirmed when the information about the driver status received from the mobile client and the observed car path match. Our approach relies on a) a distributed edge architecture that has two levels of hierarchy, namely the Main Edge Node (MEN) and Local Edge Node (LEN), to better manage the area of interest and b) a data fusion offloading strategy that considers: 1) local detection of driver drowsiness through facial expressions using CNN model, 2) global detection of car path through acceleration readings using YoLov5 algorithm, and finally, 3) a two-layer LSTM algorithm for drowsiness detection based on the local and the global detection. The proposed framework achieves drowsiness detection with an average accuracy of 97.7%.

Over-Dimension and Overloading Truck Accident Model with Structural Equation Modeling Partial Least Square

Road accidents are an unavoidable problem, especially accidents caused by Over-dimension and Overloading trucks. This study involved environmental factors, driver factors, vehicle factors, and road factors which had previously been analyzed using the multinomial logistic regression method with questionnaire data of 194 respondents and resulted in 6 indicators including road surface conditions, traffic flow volume, accident history, vehicle load, vehicle dimension, and speed limit signs. The purpose of this study was to find out relationship between latent variables using the Structural Equation Modeling Partial Least Square (SEM PLS) method to obtain a model of the factors causing Over-dimension and Overloading truck accidents. The model results obtained indicate that weather conditions, road surface conditions, lighting condition, certified training, driver's physical condition, vehicle type, vehicle dimension, vehicle load, road geometry, accident location, traffic facilities, as well as vehicle dimension, and speed limit signs were the variables that had the greatest influence.The model obtained was good and fit (fit model) based on the R2 and Chi-Square values of 65.7% and 76.3%, respectively.Keywords: Over-dimension and Overloading Trucks; Road Accidents; SEM PLS; Factors Causing Accidents

Endometrial hyperplasia with loss of APC in a novel population of Lyz2-expressing mouse endometrial epithelial cells.

Loss of heterozygosity and promoter hypermethylation of APC is frequently observed in human endometrial cancer, which is the most common gynecological cancer in the USA, but its carcinogenic driver status in the endometrial epithelium has not been confirmed. We have identified a novel population of progenitor endometrial epithelial cells (EECs) in mice that express lysozyme M (LysM) and give rise to approximately 15% of all EECs in adult mice. LysM is a glycoside hydrolase that is encoded by Lyz2 and functions to protect cells from bacteria as part of the innate immune system. Its expression has been shown in a subset of hematopoietic stem cells and in specialized lung and small intestinal epithelial cells. Conditional deletion of Apc in LysM + EECs results in significantly more epithelial cells compared to wild-type mice. At 5 months of age, the ApccKO mice have enlarged uterine horns with pathology that is consistent with endometrial hyperplasia with cystic endometrial glands, non-villous luminal papillae and nuclear atypia. Nuclear accumulation of β-catenin and ERα, both of which are known to induce endometrial hyperplasia, was observed in the EECs of the ApccKO mice. These results confirm that loss of APC in EECs can result in a phenotype similar to endometrial hyperplasia.

A Novel Physiological-Based System to Assess Drivers’ Stress during Earth Moving Simulated Activities

Earth-moving vehicles (EMVs) are vital in numerous industries, including construction, forestry, mining, cleaning, and agriculture. The changing nature of the off-road environment in which they operate makes situational awareness for readiness and, consequently, mental stress crucial for drivers and requires a high level of controllability. Therefore, the monitoring of drivers’ acute stress patterns may be used as an input in identifying various levels of attentiveness. This research presents an experimental evaluation of a physiological-based system that can be useful to evaluate the readiness of a driver in different conditions. For the experimental validation, physiological signals such as electrocardiogram (ECG), galvanic skin response (GSR) and speech data were collected from nine participants throughout driving experiments of increasing complexity on a specific simulator. The experimental results show that the identified parameters derived from the acquired physiological signals can help us understand the driver status when performing different tasks, the engagement of which is related to different road environments. This multi-parameter approach can provide more reliable information compared to single parameter approaches (e.g., eye monitoring with a camera) and identify driver status variations, from relaxed to stressed or drowsy. The use of these signals allows for the development of a smart driving cockpit, which could communicate to the vehicle the driver’s status, to set up an innovative protection system aiming to increase road safety.