Modeling the steering behavior of intoxicated drivers

국내의 주거지역화재사고는 2011년도에만 177명 사망자를 발생시켜 큰 사회적 위험 요인으로 인식되고 있다. 본 연구에서는 공동주택에서 화재시 인명피해의 주 원인이 되는 연기 및 유독가스에 대한 거동 특성을 파악하기 위한 실제 아파트를 대상으로 실규모 화재 재현 실험을 실시 하였다. 그 결과 질식사의 주범인 일산탄화소는 최대 7,600 ppm이 측정되었으며, 화재가 발생한 바로 위층에서도 최대 3,400 ppm이 측정되었다. 또한 현관문 개방시과 함께 급격하게 계단을 통한 연기 확산이 이루어져 재실자들의 가시도 확보에 큰 어려움을 줄 수 있음을 확인하였다. Domestic fire accidents in the residence area caused 177 fatalities only in 2011 and are recognised as a serious social risk factor. This study performed full scale fire reappearance experiments for actual apartments to analyze behavior characteristics of fume and harmful gas, major causes of casualties at the fire of apartment building. The outcomes found that carbon monoxide, major cause of suffocation casualty, was measured up to 7,600 ppm, and its measurement was up to 3,400 ppm on just above floor of fire floor as well. Also, it was confirmed that fast fume diffusion through stairs at the time of front door open might make occupants get in serious trouble in securing visibility.

We present a methodology for measuring the risks posed by drinking drivers that relies solely on readily available data on fatal crashes. The key to our identification strategy is a hidden richness inherent in two‐car crashes. Drivers with alcohol in their blood are seven times more likely to cause a fatal crash; legally drunk drivers pose a risk 13 times greater than sober drivers. The externality per mile driven by a drunk driver is at least 30 cents. At current enforcement rates the punishment per arrest for drunk driving that internalizes this externality would be equivalent to a fine of $8,000.

Numerous crashes are taking place every day due to the drivers’ behavioral errors and other human factors. This study seeks to address research gaps of driver’s fault assignment in the published literature related to driver performance and behavior that contribute to crashes by applying a quasi-induced exposure method with logistic regression modeling. To this end, the 2017-2020 traffic crash data, and drivers’ characteristics were collected from NHTSA’s CISS database. The modeling results indicate that drivers’ age, gender, distraction, alcohol, drugs, and belt use have statistically significant impacts on crash risk. In addition, the findings suggest that drivers who looked but did not see were 28.6% more likely to be at-fault while distracted drivers were 26.2% more likely to be at-fault in crashes than the attentive drivers. Furthermore, drivers refusing to take a BAC test or were drunk (BAC over 0.08) were more likely to have caused a crash than sober drivers with increases of 40.9% and 26.5%, respectively. Additionally, it was found that drugged drivers increase crash occurrence by 15.3% compared to sober drivers. These results are consistent with previous research. It is expected that the findings would help establish effective strategies to reduce traffic crashes by targeted education, engineering, and enforcement.

Realistic driver models can play an important role in developing new driver assistance technologies. A realistic driver model can reduce the time-consuming trial-and-error process of designing and testing products, and thereby reduce the vehicle's development time and cost. A realistic model should provide both driver path planning and arm motions that are physiologically possible. The interaction forces between a driver's hand and steering wheel can influence control performance and steering feel. The aim of this work is to develop a comprehensive yet practical model of the driver and vehicle. Consequently, a neuromuscular driver model in conjunction with a high-fidelity vehicle model is developed to learn and understand more about the driver's performance and preferences, and their effect on vehicle control and stability. This driver model can provide insights into task performance and energy consumption of the driver, including fatigue and cocontraction dynamics of a steering task. In addition, this driver model in conjunction with a high-fidelity steering model can be used to develop new steering technologies such as electric power steering.

Modeling driver steering behavior in restricted-preview boundary-avoidance tasks

Run-off-the-road (ROR) and unsuccessful recovery events account for a significant percentage of fatal vehicle crashes. Changes to roadway infrastructure have reduced the severity of automotive crashes; however, it is driver performance which remains a primary contributing factor to these incidents. An on-board vehicle control system, using combined steering and differential braking, provides enhanced safety by removing driver error from the recovery process once the ROR event has been identified. In this study, two control algorithms, based on sliding mode and state flow theories, have been designed to automatically recover a vehicle from this dangerous scenario. A driver model was also developed based on experimental data gathered at a tire manufacturer's test track. The controllers were numerically evaluated for a common road departure and return situation with comparison to the empirical driver model. The results show that both controllers safely recovered the vehicle and outperformed the driver steering model. Specifically, the state flow controller reduced recovery time by 40% compared to the sliding mode controller, while the driver model resulted in vehicle spin out.

Most existing driver models focus on predicting driving performance in normal and near-collision situations without considering the impact of collision warning parameters on driver behavior. This study develops a cognitive computational driver model based on the Queueing Network-Model Human Processor (QN-MHP) to quantify the effects of key warning parameters (i.e., warning lead time, warning reliability, and speech warning style) on driver performance in warning responses, in connected vehicle systems (CVSs). The model was validated by comparing its predictions of driver response time, response type, and braking and steering performance with data from thirty-two drivers collected in an experimental study. Once the route choice mechanism had been implemented, the driver model was found to explain the cognitive mechanism underlying how drivers process warnings in CVSs. Indeed, the validation results showed that the model was able to capture major changes in patterns of the experimental data, with R-squared values of 0.88 for warning response time, 0.69 and 0.65 for decision making in response type for the initial trial and across trials, 0.85 for braking performance, and 0.83 for steering performance. The model can be applied to optimize the interface design of CVSs based on driver needs.

As per the studies the college going students use their phone on an average of 6-7 hours a day. The screen time affects the overall health of the students. The study aims to determine if increased screen time affects reaction time in basketball players. Data was collected from the team of basketball players, measuring their daily screen usage and reaction time using the Ruler Drop Test. Their screen time duration is recorded and correlated with reaction time test results. Findings suggest a statistically significant association between prolonged screen exposure and delayed reaction times, highlighting the potential impact of digital device usage on reaction to the quick challenges faced among basketball players. These results emphasize the need for balanced screen usage and raise awareness about the potential consequences of excessive screen time on reaction time. The higher duration of screen exposure to the mobile lower is the reaction time in basketball players. Keywords Reaction Time, Basketball Players, Ruler Drop Test

Reinforced concrete (RC) frame-core tube buildings are widely constructed both in China and the United States (US). Their seismic performances greatly influence the economic loss of earthquakes. This study aims to compare the seismic losses of two typical RC frame-core tube tall buildings designed following the Chinese and the US seismic design codes. The prototype building is originally designed using the US seismic design codes, provided by the Tall Building Initiative (TBI) Project. Then the prototype building is redesigned according to the Chinese seismic design codes with the same design conditions and seismic hazard level. Detailed nonlinear finite element (FE) models are established for both designs. These models are used to evaluate their seismic responses at different earthquake intensities, including the service level earthquake (SLE), the design based earthquake (DBE) and the maximum considered earthquake (MCE). In addition, the collapse fragility functions of these two buildings are established using the incremental dynamic analysis (IDA). Subsequently, the seismic loss consequences (repair costs, repair workload, and casualties) of these two designs are calculated using the procedure proposed by FEMA P-58. The comparison shows that the Chinese design exhibits better seismic performances in most cases with smaller total repair cost, shorter repair time and a smaller number of casualties, except slightly longer repair time at the MCE level. For both designs, the repair cost of nonstructural components accounts for the majority of the total cost. The ceilings and elevators are the major causes of casualties at the MCE level.

The damage of buildings is the major cause of casualties of from earthquakes. The traditional pixel-based earthquake damaged building detection method is prone to be affected by speckle noise. In this paper, an object-based change detection method is presented for the detection of earthquake damage using the synthetic aperture radar (SAR) data. The method is based on object-level texture features of SAR data. Firstly, the principal component analysis is used to transform the optimal texture features into a suitable feature space for extracting the key change. And then, the feature space is clustered by the watershed segmentation algorithm, which introduces the concept of object orientation and carries out the calculation of the difference map at the object level. Having training samples, the classification threshold values for different grade of earthquake damage can be trained, and the detection of damaged building is achieved. The proposed method could visualize the earthquake damage efficiently using the Advanced Land Observing Satellite-1 (ALOS-1) images. Its performance is evaluated in the town of jiegu, which was hit severely by the Yushu Earthquake. The cross-validation results shows that the overall accuracy is significantly higher than TDCD and IDCD.

Pictorial Review of Radiographic Patterns of Injury in Modern Warfare: Imaging the Conflict in Afghanistan

Purpose: To examine the reaction time when human subjects process information presented in the visual channel under both a direct vision and a virtual rehabilitation environment when walking was performed.Method: Visual stimulus included eight math problems displayed on the peripheral vision to seven healthy human subjects in a virtual rehabilitation training (computer-assisted rehabilitation environment (CAREN)) and a direct vision environment. Subjects were required to verbally report the results of these math calculations in a short period of time. Reaction time measured by Tobii Eye tracker and calculation accuracy were recorded and compared between the direct vision and virtual rehabilitation environment.Results: Performance outcomes measured for both groups included reaction time, reading time, answering time and the verbal answer score. A significant difference between the groups was only found for the reaction time (p = .004). Participants had more difficulty recognizing the first equation of the virtual environment.Conclusions: Participants reaction time was faster in the direct vision environment. This reaction time delay should be kept in mind when designing skill training scenarios in virtual environments. This was a pilot project to a series of studies assessing cognition ability of stroke patients who are undertaking a rehabilitation program with a virtual training environment.Implications for rehabilitationEye tracking is a reliable tool that can be employed in rehabilitation virtual environments.Reaction time changes between direct vision and virtual environment.

Objective Compared to healthy athletes, athletes who have undergone anterior cruciate ligament (ACL) reconstruction have been shown to have delayed reaction time (RT). However, the reliability of RT measurements after ACL reconstruction remains unknown, and no study has measured RT after ACL reconstruction in the single-leg standing position. This study aimed to validate the reliability of simple RT measurement in a single-leg standing position within a single session after ACL reconstruction. Materials and methods Participants who underwent ACL reconstruction were recruited for this study. A response-measuring instrument synchronized with a mat sensor measured simple RT in the single-leg standing position. The participant assumed the single-leg standing position on the mat sensor and lifted the sole off the mat sensor as soon as possible after the lamp lighting. The RT was defined as the time from the lamp lighting to when the sole left the mat sensor. The RT of each operative and nonoperative side was measured 10 times randomly. After confirming the normality of the data, the frequency effect on the 10 trials was confirmed by repeated measures analysis of variance. Results The mean of 10 RT trials in the single-leg standing position was 0.444 ± 0.072 s on the operative side and 0.436 ± 0.060 s on the nonoperative side. Repeated measures analysis of variance showed no significant difference between measurements for either the operative or nonoperative side. After the second trial, the intraclass correlation coefficient exceeded 0.900 for both the operative and nonoperative sides. The standard error of each measurementfor the three trials was 0.006 s and 0.041 s for the operative and nonoperative sides, respectively. Conclusion The reliability of measurement within a single session of simple RT in the single-leg standing position after ACL reconstruction was acceptable.

To evaluate the accuracy of several methods aimed to detect Helicobacter pylori stool antigens in patients with upper gastrointestinal bleeding. Thirty-four patients with upper gastrointestinal bleeding because of peptic ulcer were included. The first stool sample during hospitalization was collected, and stool antigens were determined with: polyclonal enzyme-linked immunosorbent assay (Premier-Platinum-HpSA); monoclonal enzyme-linked immunosorbent assay (Amplified-IDEIA-HpStAR); and rapid monoclonal immunochromatographic test (ImmunoCard-STAT HpSA). A patient was considered infected when H. pylori was diagnosed with invasive tests (rapid urease test or histology) or with (13)C-urea breath test. When all tests were negative, a new breath test was repeated after stopping proton pump inhibitors. All patients were infected and, therefore, only sensitivity of the tests could be calculated: polyclonal enzyme-linked immunosorbent assay (74%), monoclonal enzyme-linked immunosorbent assay (94%), and rapid monoclonal immunochromatographic test (60%; concordance between the two observers was high, kappa = 0.9). Neither the presence of maelena nor the delay in obtaining stool samples explained false negatives. Neither the polyclonal enzyme-linked immunosorbent assay stool antigen test nor the rapid immunochromatographic stool antigen test can be recommended to diagnose H. pylori infection in patients with upper gastrointestinal bleeding. However, the monoclonal enzyme-linked immunosorbent assay stool antigen test is highly sensitive for detecting the infection in patients with this complication, although more studies are necessary to evaluate the specificity of the method.

The present study was aimed at deciphering whether the delay in choice reaction time (RT) and the silent period (SP) caused by transcranial magnetic stimulation (TMS) of the motor cortex in the ongoing electromyogram are due to the same physiological mechanism. To this end, the effect of TMS was studied in 6 healthy volunteers performing a between‐hand choice RT task. Specific predictions were derived from a logic inspired from the “postponed stages” hypothesis (Pashler & Johnson, 1989). This logic predicts a correlation between SP duration and RT when the stimulated cortex is involved in the response, and a stronger correlation when the stimulation is delivered later during the RT interval. The effect of TMS on RT was twofold: At early stimulation times, the stimulation shortened the RT and this effect was independent of the involvement of the stimulated motor cortex in the subsequent response. At later stimulation times, TMS had a disruptive effect, provided that the stimulated cortex was involved in the response. When the stimulated cortex was involved in the response, there was a correlation between SP and RT; this correlation was stronger when the stimulation occurred later. In contrast, there was no correlation between these two variables when the stimulated cortex was not involved.