Driving Conditions Research Articles

Driver Status Monitoring Systems for Smart Vehicles Using Physiological Sensors: A safety enhancement system from automobile manufacturers

Automobiles provide a convenient form of transportation, and the number of automobiles in the world has been increasing rapidly, from 193 million in 1970 to more than 796 million in recent years [1]. However, automobiles have created a number of serious problems, such as accidents, traffic congestion, and air pollution, among which traffic accidents are one of the most serious and urgent problems threatening the safety of automobile users.

Older Adults' Acceptance of Vehicle Functions that Compensate for Aging Effects

To investigate older drivers' perceptions of vehicle functions that compensate for aging effects, including different levels of automation and various types of warning functions, we conducted a survey among older drivers in South Korea. Participants (N = 118 men and 45 women; age range 55–75 years) reported on how important they perceived each function to be. Results showed that, for older drivers, the importance of warnings about the surrounding environment was significantly greater than that of warnings about the driver's condition and the car's status. Further, fully automatic functions were significantly less preferred to warning and assistive functions. These results indicate that the characteristics and needs of older adults should be taken into account in relation to the design of vehicle functions.

Gender differences in mobility of Hispanic immigrants

This paper examines the mobility of an increasingly important demographic group in the U.S., Hispanic immigrants. Using National Household Travel Survey data for 2009, this paper finds that, compared to the mobility of other populations, the mobility of Hispanic female immigrants is much lower, particularly when household income is low. Hispanic female immigrants are substantially less likely to be drivers than their male counterparts, and their probability of being a driver stagnates for decades after immigration, unlike female immigrants of other race/ethnicity groups. Hispanic female immigrants seem to remain non-drivers rather reluctantly. At the household level, Hispanic immigrants do not actively choose less auto-dependent lifestyles, and females clearly depend on others’ mobility. Yet once Hispanic female immigrants become drivers, they drive more than females of other race/ethnicity groups. Their high, hidden demand for driving is likely not related to domestic needs because their driving mileage is unassociated with child-caring duties, and is associated with household income level. The association between low-income status and driver status of Hispanic female immigrants has policy implications, especially if the barriers to becoming a driver limit Hispanic female immigrants' access to training or employment and contribute to their low-income status. More detailed analysis is anticipated to detail the mobility challenges Hispanic female immigrants face.

Biomarkers for the Clinical Use of PD-1/PD-L1 Inhibitors in Non-Small-Cell Lung Cancer: A Review.

The development of programmed cell death 1 (PD-1)/PD-1 ligand 1 (PD-L1) checkpoint inhibitors has changed the landscape of non-small-cell lung cancer (NSCLC) therapy, with 2 approvals from the US Food and Drug Administration of PD-1 inhibitors for second-line therapy. However, the rational use of these agents has been limited by the lack of a definitive predictive biomarker. Tumor PD-L1 expression is associated with an increased likelihood of NSCLC response to these agents, although responses can still occur at a low rate in PD-L1-negative tumors. The use of PD-L1 as a predictive biomarker for use of PD-1/PD-L1 inhibitors is limited by the multitude of PD-L1 antibodies, assays, scoring systems, and thresholds for positivity currently used. Alternative biomarkers such as tumor neoantigens identified through whole-exome sequencing and clinical parameters (eg, smoking or oncogene driver status) may also have predictive value. Biomarkers that can direct the rational use of PD-1/PD-L1 checkpoint inhibitors are crucial given the risk of life-threatening immune-related complications associated with these therapies and the reality that most patients still do not benefit from their use. The refinement of existing biomarkers and identification of novel predictive biomarkers will be key to ensuring the effective and safe use of these agents. Since most patients still do not benefit from these agents, it is critical to continue to work to define the select patient population who will derive durable benefit from PD-1/PD-L1 inhibition and identify markers that could have predictive value for combination therapies that could expand the population who benefit.

Математична модель інформаційно-керуючої системи контролю операційної діяльності машиніста швидкісного поїзда.

The article researches application possibility of Samsonkin – Druz’ system approach. This method increases the safety of system "train's driver – the train – infrastructure of railway transport" activity. Authors have developed a new method for monitoring driver status, using individual norm. This monitoring method is a mathematical model of information management operations control system operator in the management of high-speed train. Application of the theory Samsonkin – Druz’ possible to determine the actual problems and future directions of improving the reliability of the human factor in ensuring traffic safety problem.

OS1203 超小型車両用ステアバイワイヤシステムの制御 : 三角筋前部の筋活動に関する実験的考察

In recent years, ultra-compact electric vehicles have been increasingly demanded because of environmental issues and the aging of the society. We aim to design and manufacture a Steer-by-Wire System to control the driver's seat when the driver's entire body is on the active seat suspension system. In addition, we also aim to develop an intelligent seat that can provide an optimal driving environment in accordance with changing conditions by monitoring the driver's physical condition by the system on the basis of various biological signals. In this study, we carry out a driving experiment of an ultra-compact electric vehicle with the SBWS to evaluate changes in the operational feel perceived by drivers by means of a questionnaire on subjective evaluation or surface EMG, and collect basic data used to improve the operability.

Drowsiness Detection Based on the Analysis of Breathing Rate Obtained from Real-time Image Recognition

Road accidents and victims provoked by drowsiness are a worldwide relevant social and economic problem. In EU, 25% of road accidents are related with fatigue. The relation between drowsiness and accidents is supported by scientific evidences that relate microsleep and other fatigue episodes with the loss of control of the vehicle. Nowadays there exist different technological approaches for drowsiness mitigating while driving, which try to reduce these accidents by detecting driver's physical condition and acting on the driver response. The techniques for mitigating accidents are based on algorithms of detection of physiological parameters that ensure drivers confidence and an appropriate use of the technology. The most frequently used non-invasive system are the cameras, which have been explored and used, mainly detecting eye movement and eyelid closure (PERCLOS). These systems have limitations due to artifacts and noise related to environmental and emotional conditions, which might lead to false positives. With the purpose of solving these shortcomings, the goal of the research is to develop a system capable of detecting the level of drowsiness based on the involuntary movements of the driver provoked by the respiration captured by means of cameras. In the current research, robustness in front of different types of users and circumstances has been explored. A system of reduced size automotive cameras of high dynamics is proposed to be used as breathing rate sensor. Images captured by these sensors will be processed to obtain the driver's chest/abdomen movement. These data will be analyzed in real time by a validated algorithm that interprets the movement and obtains the level of fatigue and drowsiness of the driver. A twofold “gold standard” is used to compare the robustness of the developed system. An experimental design has been developed in order to take into consideration different anthropometric characteristics, clothing types, user and vehicle movement and light conditions. The result will provide the boundary conditions of any system based on on-board cameras. These data will be used for building the algorithms to detect and interpret breathing patterns. The results show the capabilities of this approach and also permit to define the needs and requirements of the resulting technological developments.

Integrating the effect of driver to improve handling performance of vehicle

Abstract: Considering the effect of human driver in the stability of vehicle has always been one of the most important nuances in vehicle literature. Yet, the lack of a standard framework for modeling the human behavior coerces the companies to mostly entrust to the drivers - as an unknown external input - for ultimate control tasks. This evinces that a key problem still to work out is a reliable control technique incorporating effect of human to serve driver’s request better. In this paper a simple model of human path following behavior is used that capture the most important parameters of a driver as well as the cumulative driver’s observation and the action delay. Integrating the driver model with a linear vehicle model, a linear parameter varying formulation is adopted. Assuming certain ranges for the parameters and their respective rate of changes, a controller robust to time delay is proposed and the corresponding disturbance attenuation in the sense of truncated l 2 norm is estimated. From an implementation point of view, the important advantage of the proposed controller is that only uses the IMU sensor and the vehicle velocity estimation, without requiring extra information about the desired path. The control action is based on the current vehicle states along with the estimation of driver status. In distributed wheel drive implementation, an optimal torque distributor transfers the torque to the wheels to achieve the best overall performance. The simulation results show that the proposed technique improves the vehicle overall performance compared to the case that the controller is designed separately.

Caregiving, Transport-Related, and Demographic Correlates of Sedentary Behavior in Older Adults: The Senior Neighborhood Quality of Life Study.

Excess sedentary time predicts negative health outcomes independent of physical activity. The present investigation examined informal caregiving duties and transportation-related factors as potential correlates of sedentary behavior in older adults. Average daily sedentary time was measured via accelerometer in adults ages 66 years and older (N = 861). Caregiving variables included dog ownership and informal family caregiving status. Transportation variables included driver status, walking distance to public transit, and reported presence of pedestrians and bicyclists in one's neighborhood. In multivariate models, owning a dog and being a driver were associated with less sedentary time (p ≤ .01). Educational status and geographic region modified the association between dog ownership and sedentary time, and age modified the association between driver status and sedentary time. This study identified that older adult dog owners and drivers were less sedentary. Both factors may create opportunities for older adults to get out of their homes.

Recognition of driver’s fatigue expression using Local Multiresolution Derivative Pattern

To develop the human-centric driver fatigue monitoring system for automatic understanding and charactering of driver's conditions, a novel, efficient feature extraction approach, named Local Multiresolution Derivative Pattern (LMDP), is proposed to describe the driver's fatigue expression images, and the Intersection Kernel Support Vector Machines classifier is then exploited to recognize three pre-defined classes of fatigue expressions, i.e., awake expressions, moderate fatigue expressions and severe fatigue expressions. With features extracted from a fatigue expressions dataset created at Southeast University, the holdout and cross-validation experiments on fatigue expressions classification are conducted by the Intersection Kernel Support Vector Machines classifier, compared with three commonly used classification methods including the k-nearest neighbor classifier, the multilayer perception classifier and the dissimilarity-based classifier. The experimental results of holdout and cross-validation showed that LMDP offers the better performance than Local Derivative Pattern, and the second order LMDP exceeds other order LMDP. With the second order LMDP and the Intersection Kernel Support Vector Machines classifier, the classification accuracies of the severe fatigue are over 90 in the holdout and cross-validation experiments, thus demonstrating the effectiveness of the proposed feature extraction method in automatically understanding the driver's conditions towards the human-centric driver fatigue monitoring system.

Intratumoral Heterogeneity of ALK-Rearranged and ALK/EGFR Coaltered Lung Adenocarcinoma.

Genetic intratumoral heterogeneity has a profound influence on the selection of clinical treatment strategies and on addressing resistance to targeted therapy. The purpose of this study was to explore the potential effect of intratumoral heterogeneity on both genetic and pathologic characteristics of ALK-rearranged lung adenocarcinoma (LADC). We tested ALK fusions and EGFR mutations in 629 patients with LADC by using laser-capture microdissection to capture spatially separated tumor cell subpopulations in various adenocarcinoma subtypes and to test for ALK fusions and EGFR mutations in ALK-rearranged, EGFR-mutated, and ALK/EGFR coaltered LADCs to compare the oncogenic driver status between different tumor cell subpopulations in the same primary tumor. Among the 629 patients, 30 (4.8%) had ALK fusions, 364 (57.9%) had EGFR mutations, and two had ALK fusions that coexisted with EGFR mutations. Intratumoral heterogeneity of ALK fusions were identified in nine patients by reverse-transcriptase polymerase chain reaction. In the two patients with an ALK/EGFR coaltered status, genetic intratumoral heterogeneity was observed both between different growth patterns and within the same growth pattern. The relative abundance of ALK and EGFR alterations was different in the same captured area. ALK fusions were positively associated with a micropapillary pattern (P = .002) and were negatively associated with a lepidic pattern (P = .008) in an expanded statistical analysis of 900 individual adenocarcinoma components, although they appeared to be more common in acinar-predominant LADCs in the analysis of 629 patients. Intratumoral genetic heterogeneity was demonstrated to coexist with histologic heterogeneity in both single-driver and ALK/EGFR coaltered LADCs. Altered oncogenic drivers in spatially separated subclones of the same tumor may be different.

Exploring factors contributing to crash injury severity on rural two-lane highways

ObjectiveCrash injury results from complex interaction among factors related to at-fault driver's behavior, vehicle characteristics, and road conditions. Identifying the significance of these factors which affect crash injury severity is critical for improving traffic safety. A method was developed to explore the relationship based on crash data collected on rural two-lane highways in China. MethodsThere were 673 crash records collected on rural two-lane highways in China. A partial proportional odds model was developed to examine factors influencing crash injury severity owing to its high ability to accommodate the ordered response nature of injury severity. An elasticity analysis was conducted to quantify the marginal effects of each contributing factor. ResultsThe results show that nine explanatory variables, including at-fault driver's age, at-fault driver having a license or not, alcohol usage, speeding, pedestrian involved, type of area, weather condition, pavement type, and collision type, significantly affect injury severity. In addition to alcohol usage and pedestrian involved, others violate the proportional odds assumption. At-fault driver's age of 25–39years, alcohol usage, speeding, pedestrian involved, pavement type of asphalt, and collision type of angle are found to be increased crash injury severity. Practical ApplicationsThe developed logit model has demonstrated itself efficient in identifying the effect of contributing factors on the crash injury severity.

Enhancing the Security of Vehicles and the Work of the DVLA Using GSM/SMS Technologies

Vehicle theft is seen as occurring almost on daily basis. From the survey carried out for this research, over 90 percent of respondents testified to its existence. This research took a closer look at collaborations of Driver and Vehicle Licensing Authority and the Security Services in combating this crime. It also sought the readiness of vehicle owners, DVLA and Ghana Police Service to embrace technology integration. A critical look at vehicle security in terms of its theft related problems, the integration of GSM/SMS technology into the Driver and Vehicle Licensing Authority Registration Database, making it easily accessible by the Security Services like the Ghana Police Service for quick and easy verification of driver or vehicle license status as well as the genuineness in relation to the ownership, in the fight against vehicle theft is very vital. Security personnel can send an SMS to the proposed system and instantly get reply containing significant particulars of the Vehicle and the owner.

Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigation.

We analyzed the spatial diversity of tumor habitats, regions with distinctly different intensity characteristics of a tumor, using various measurements of habitat diversity within tumor regions. These features were then used for investigating the association with a 12-month survival status in glioblastoma (GBM) patients and for the identification of epidermal growth factor receptor (EGFR)-driven tumors. T1 postcontrast and T2 fluid attenuated inversion recovery images from 65 GBM patients were analyzed in this study. A total of 36 spatial diversity features were obtained based on pixel abundances within regions of interest. Performance in both the classification tasks was assessed using receiver operating characteristic (ROC) analysis. For association with 12-month overall survival, area under the ROC curve was 0.74 with confidence intervals [0.630 to 0.858]. The sensitivity and specificity at the optimal operating point ([Formula: see text]) on the ROC were 0.59 and 0.75, respectively. For the identification of EGFR-driven tumors, the area under the ROC curve (AUC) was 0.85 with confidence intervals [0.750 to 0.945]. The sensitivity and specificity at the optimal operating point ([Formula: see text]) on the ROC were 0.76 and 0.83, respectively. Our findings suggest that these spatial habitat diversity features are associated with these clinical characteristics and could be a useful prognostic tool for magnetic resonance imaging studies of patients with GBM.

작업 부하에 따른 운전자의 생체신호 처리 및 특성 분석

졸음 운전 등 운전자의 상태 변화에 따른 자동차 사고가 급증하고 있으며 이를 방지하기 위한 시스템 구축 및 운전자의 상태를 판단하는 알고리즘 개발이 요구되어 지고 있다. 본 논문에서는 모의 주행 시스템을 통한 운전자의 심박변이도, 산호 포화도 (SPO2), 체온을 측정하여 운전자의 상태를 알려 주는 실험을 수행하였다. 즉, 심박변이도 (Heart rate variability, HRV) 분석을 위해 운전자의 심전도(Electrocardiogram, ECG) 신호를 획득 한 후 심전도 P,QRS, T 파형 중 R peak 을 자동으로 검출하였고 이를 통해 구한 R-R interval을 이용하여 HRV의 주요 파라메타를 시간영역(time domain)으로 해석하여 작업 환경에 따른 운전자의 상태를 비교 분석하였다. The accidents caused by drivers while driving are considered as the major causes along with other causes such as conditions of roads, weather and cars. In this study, we investigated the driver's workloads under three different driving conditions (Weather, Driving time zone, and Traffic density) through analyzing biological signals obtained from a car driving simulator system. The proposed method is able to detect R waves and R-R interval calculation in the ECG. Heart rate variability (HRV) was investigated for the time domain to determine the changes in driver's conditions.

The Driver Fatigue Status Classification Based On Multi Region Evidence Support

Multi-observation areas based ensemble learning models are proposed for general fatigue performance identification. It is believed that although the expression of fatigue is not obvious. If the observation areas are concerned, the performance of fatigue will be relatively concentrated and the law of expression changes will be much clearer. Another advantage of feature extraction from the observation areas is that it can greatly reduce the interference caused by redundant information in the face when learning classifiers. For each observation area, a C4.5 base classification model is built. Each base model is equivalent to an independent decision maker. But due to its limited capacity, it may not be able to give accurate decisions. However, if the information provided by each decision maker is combined together, it will form comprehensive evidence. Driver status classification results given by ensemble learning approach are more accurate and stable.

Stereo-vision-based cooperative-vehicle positioning using OCC and neural networks

Vehicle positioning has been subjected to extensive research regarding driving safety measures and assistance as well as autonomous navigation. The most common positioning technique used in automotive positioning is the global positioning system (GPS). However, GPS is not reliably accurate because of signal blockage caused by high-rise buildings. In addition, GPS is error prone when a vehicle is inside a tunnel. Moreover, GPS and other radio-frequency-based approaches cannot provide orientation information or the position of neighboring vehicles. In this study, we propose a cooperative-vehicle positioning (CVP) technique by using the newly developed optical camera communications (OCC). The OCC technique utilizes image sensors and cameras to receive and decode light-modulated information from light-emitting diodes (LEDs). A vehicle equipped with an OCC transceiver can receive positioning and other information such as speed, lane change, driver's condition, etc., through optical wireless links of neighboring vehicles. Thus, the target vehicle position that is too far away to establish an OCC link can be determined by a computer-vision-based technique combined with the cooperation of neighboring vehicles. In addition, we have devised a back-propagation (BP) neural-network learning method for positioning and range estimation for CVP. The proposed neural-network-based technique can estimate target vehicle position from only two image points of target vehicles using stereo vision. For this, we use rear LEDs on target vehicles as image points. We show from simulation results that our neural-network-based method achieves better accuracy than that of the computer-vision method.

‘Drive Safe’ initiatives: An analysis of improvements in mental health practices (2005–2013) to support safe driving

Statement of context Driving can be important for enabling community participation. Mental illness and its treatment may disrupt fitness to drive, or people’s opportunities for learning to drive. This paper reflects on practice improvements in an Australian mental health organization. Critical reflection on practice Occupational therapists identified gaps in knowledge among the multi-disciplinary workforce about service users’ driver status, how to identify and manage driving issues, and how to support their beginning or resuming driving during recovery. Implications for practice The key initiatives: policy and practice guidelines, workforce training, driver assessment and consultation services, and service user information resources — have become embedded supports within the organization for promoting safe driving.

Driver injury severity related to inclement weather at highway–rail grade crossings in the United States

ABSTRACTObjective: Previous studies on crash modeling at highway–rail grade crossings were aimed at exploring the factors that are likely to increase the crash frequencies at highway–rail grade crossings. In recent years, modeling driver's injury severity at highway–rail grade crossings has received interest. Because there were substantial differences among different weather conditions for driver's injury severity, this study attempts to explore the impact of weather influence on driver injury at highway–rail grade crossing.Method: Utilizing the most recent 10 years (2002–2011) of highway–rail grade crossing accident data, this study applied a mixed logit model to explore the determinants of driver injury severity under different weather conditions at highway–rail grade crossing.Results: Analysis results indicate that drivers' injury severity at highway–rail grade crossings is strongly different for different weather conditions. It was found that the factors significantly impacting driver injury severity at highway–rail grade crossings include motor vehicle speed, train speed, driver's age, gender, area type, lighting condition, highway pavement, traffic volume, and time of day.Conclusions: The findings of this study indicate that crashes are more prevalent if vehicle drivers are driving at high speed or the oncoming trains are high speed. Hence, a reduction in speed limit during inclement weather conditions could be particularly effective in moderating injury severity, allowing more reaction time for last-minute maneuvering and braking in moments before impacts. In addition, inclement weather-related crashes were more likely to occur in open areas and highway–rail grade crossings without pavement and lighting. Paved highway–rail grade crossings with installation of lights could be particularly effective in moderating injury severity.

Fitness-to-drive after mild traumatic brain injury: Mapping the time trajectory of recovery in the acute stages post injury

IntroductionLittle is known about the trajectory of recovery in fitness-to-drive after mild traumatic brain injury (mTBI). This means that health-care professionals have limited evidence on which to base recommendations to this cohort about driving. ObjectiveTo determine fitness-to-drive status of patients with a mTBI at 24h and two weeks post injury, and to summarise issues reported by this cohort about return to driving. MethodQuasi-experimental case-control design. Two groups of participants were recruited: patients with a mTBI (n=60) and a control group with orthopaedic injuries (n=60). Both groups were assessed at 24h post injury on assessments of fitness-to-drive. Follow-up occurred at two weeks post injury to establish driver status. Main MeasuresMini mental state examination, occupational therapy-drive home maze test (OT–DHMT), Road Law Road Craft Test, University of Queensland-Hazard Perception Test, and demographic/interview form collected at 24h and at two weeks. ResultsAt the 24h assessment, only the OT–DHMT showed a difference in scores between the two groups, with mTBI participants being significantly slower to complete the test (p=0.01). At the two week follow-up, only 26 of the 60 mTBI participants had returned to driving. Injury severity combined with scores from the 24h assessment predicted 31% of the variance in time taken to return to driving. Delayed return to driving was reported due to: “not feeling 100% right” (n=14, 23%), headaches and pain (n=12, 20%), and dizziness (n=5, 8%). ConclusionThis research supports existing guidelines which suggest that patients with a mTBI should not to drive for 24h; however, further research is required to map factors which facilitate timely return to driving.