The effects of joint angle variability and different driving load scenarios on maximum muscle activity – A driving posture simulation study

To date, no studies have been conducted on the main and interaction effects of joint angles on maximum muscle activity in different driving load scenarios. To investigate the influence of joint angle variability on the muscular system, this study calculated maximum muscle activity during three static driving load scenarios through the use of musculoskeletal inverse dynamic simulation. Six joint angles in sagittal plane were varied with reference to reported driving posture angles in the literature. A digital manikin with a height of 180 cm and weight of 70 kg was used with simple muscles and a minimum fatigue criterion for muscle activation optimization. Three static driving load scenarios were simulated: sitting with no external forces except gravity, steering, and pedaling operation. Prediction models were developed for each driving load scenario using Least Squares Support Vector Machine. Finally, the Pareto optimization method was applied for multi-objective optimization combining the three developed models.The results indicate that the developed models can be used for the prediction of simulated maximum muscle activity. The six joint angles explain a higher percentage of maximum muscle activity variance in the steering and pedaling operation scenarios compared to the sitting scenario. The six joint angles differ in their main and interaction effects on maximum muscle activity depending on the driving load scenario. The optimum joint angle values of the driving posture depend on the driving load scenarios. The different driving postures based on minimum maximum muscle activity are presented for the three driving load scenarios.Relevance to industryThe results of this study can be utilized in establishing driving posture simulation models to improve vehicle interiors during the early development stage. Furthermore, the results of this study can provide base data for the development of a tool for real driving posture evaluation of maximum muscle activity.     

A look at driving rain spells at three cities in Great Britain

In this article, driving rain spell amounts are calculated from hourly wind and rainfall data for three cities in Great Britain, and comparisons are made with average annual driving rain values, also from hourly wind and rainfall data. The results show that the maximum observed driving rain spell intensities versus wall angles are qualitatively similar to angular distributions of annual driving rain both at and between the stations. For driving rain spell intensity expressed as the average number of exceedances of a threshold spell intensity value versus wall angle, results are both qualitatively and quantitatively very similar to angular distributions of annual driving rain. This suggests that average annual driving rain might be useful as a proxy measure of spell driving rain intensity in situations where relative risk of repeated penetration of masonry walls in building facades is of interest but quantitative spell data are not available.     

1990~2015年吉林省西部耕地变化与“旱改水”时空特征研究

以Landsat遥感影像为数据源,利用面向对象和决策树方法获得多期土地覆被数据;以此为基础,分析了1990~2015年吉林省西部耕地变化与旱田水田转化特征及驱动因素。结果表明：1990~2015年期间,吉林省西部耕地面积增加了2159.33 km²,增速逐渐变缓。旱田面积在1990~2000和2000~2010年期间有小幅增加,但在2010~2015年期间呈减少趋势。水田面积持续扩张,25年间增加了1139.39 km²（51.7%）,旱田净转化为水田的面积不断增加,1990~2000年为69.13 km²,2000~2010年为156.19 km²,2010~2015年为288.27 km²。人口和经济的增长是导致耕地面积迅速增长的主要原因,影响水田面积扩张和旱田向水田转化的驱动因素有：科技进步、水利设施建设、政策倾向和利益驱动。最后提出了吉林省西部地区耕地保护的建议,为区域农业生产和生态建设提供科学借鉴。     

A smartphone based technique to monitor driving behavior using DTW and crowdsensing

Safety issues while driving in smart cities are considered to be top-notch priority in contrast to traveling. Today’s fast paced society, often leads to accidents. In order to reduce the road accidents, one key area of research is monitoring the driving behavior of drivers. Understanding the driver behavior is an essential component in Intelligent Driver Assistance Systems. One of potential cause of traffic fatalities is aggressive driving behavior. However, drivers are not fully aware of their aggressive actions. So, in order to increase awareness and to promote driver safety, a novel system has been proposed. In this work, we focus on DTW based event detection technique, which have not been researched in motion sensors based time series data to a great extent. Our motivation is to improve the classification accuracy to detect sudden braking and aggressive driving behaviors using sensory data collected from smartphone. A very significant feature of DTW is to be able to automatically cope with time deformations and different speeds associated with time-dependent data which makes it suitable for our chosen application where data might get affected due to factors such as: high variability in road and vehicle conditions, heterogeneous smartphone sensors, etc. Our technique is novel as it uses fusion of sensors to enhance detection accuracy. The experimental results show that proposed algorithm outperforms the existing machine learning and threshold-based techniques with 100% detection rate of braking events and 97% & 86.67% detection rate of normal left & right turns and aggressive left & right turns respectively.     

Effects of Quantity and Size of Buttons of In-Vehicle Touch Screen on Drivers’ Eye Glance Behavior

This article investigates the effects of the quantity and size of touch screen buttons and the task-interleaving strategies on drives’ eye glance behavior. An experiment was conducted on a fixed-base driving simulator with 20 participants. The participants were asked to perform a button search-and-press task on an in-vehicle touch screen while driving. A full-factorial within-subject design was used with three button quantities (4, 8, and 15) and three button sizes (14 mm, 24 mm, and 33 mm). Although a normal distribution was often assumed for the eye glance data in previous studies, our results show that the total eyes-off-road time (TEORT) and glance durations are generally not normally distributed (positively skewed) even after a log transformation. The results show that the number of buttons has an increasing effect on task completion time, TEORT, and long (2+ s) glances. However, in general, no such differences were found for button sizes. Further analysis shows that long glances were strongly associated with drivers completing the task with a single glance. It seems to suggest that a major cause of long glances is that drivers are reluctant to switch the task back to driving at subtask boundaries that are probably associated with the high cost of interruption. These findings confirm the importance of task resumability for in-vehicle user interfaces and have implications that careful task analysis needs to be conducted in the context of multitasking. Certain subtask combinations, such as a visual search followed by pressing the search target, may discourage task interleaving and ultimately compromise driving safety.     

基于KAYA 恒等式和LMDI分解法的淮安市生产用水驱动因素分析

分析了2008—2017年淮安市主要用水指标的变化特征,综合考虑了水资源禀赋、水资源开发利用、人口规模、经济水平、产业结构、用水效率6个因素对淮安市生产用水的影响,基于扩展的KAYA恒等式建立了用水驱动因素分解模型。并应用对数均值迪氏指数法对2008—2017年淮安市生产用水变化进行分解分析,以评价逐年和多年时间尺度上各类驱动因素对生产用水的相对影响程度。结果表明:2008—2017年淮安市用水总量和生产用水量总体呈下降态势,主要用水效率指标均有改善;经济水平和用水效率提升是淮安市2008—2017年生产用水变化的主要驱动因素,两者在逐年和多年时间尺度上对生产用水变化的相对贡献率分别为56.49%、-127.71%和56.63%、-126.57%。研究成果对了解淮安市用水总量及主要用水效率指标的历史发展趋势,正确理解淮安市产业结构与用水结构协调发展具有参考价值。     

基于计算机视觉的驾驶员低头行为检测

在驾驶过程中使用手机会引起驾驶员的注意力分散,为了对这种行为进行监督和提醒(在公共交通中检测更有意义),提出了一种基于脸部特征提取的驾驶员低头行为的检测方法。该方法使用主动型状模型(Active Shape Model,ASM)算法得到脸部特征点,在此基础上通过脸部特征点的位置信息计算出头部姿势描述信息,最后通过SVM将上述信息分类进而得出头部姿势,其可用于判断驾驶员是否在驾驶过程中低头看手机行为,该方法能够有效检测出驾驶员在驾驶过程中低头使用手机的行为。实验结果表明,该方法的平均检出率在94%以上。     

基于8051单片机的多功能汽车开车控制器

控制器以8051单片机为核心,主要由密码保护、酒精检测及超声波测距防撞等三个了系统模块构成。密码锁系统设定四位十进制数为密码,增强了汽车的防盗性能。酒精检测用气敏传感器检测驾驶者呼气中酒精浓度,可有效防止酒后驾车。超声波测距防撞系统以超声波存空气中传播速度为己知条件,根据发射和接收的时间差计算出发射点到障碍物的实际距离,可预防追尾事件的发生。该控制器克服了传统控制装置功能相对单一、精度低、响应速度慢等缺点,具有广阔的应用前景。     

Development of statistical geometric models for prediction of a driver's hip and eye locations

The Society of Automotive Engineers (SAE) J1517 and J941 models of a driver-selected seat position and a driver's eye location mainly rely on their statistical linear relationships with seat configuration and package variables. Although the SAE models are useful for vehicle interior design, their prediction performance was not provided. The present study was intended to develop accurate prediction models of a driver's hip location (HL) and eye location (EL) based on their statistical geometric relationships with anthropometric dimensions and driving postures. A driving simulation experiment was conducted with 40 Korean drivers (20 males and 20 females) in a seating buck reconfigurable to various package conditions. The anthropometric measurements, HLs, ELs, and joint angles of the participants were collected using an anthropometer, a motion capture system, and a digital human model simulation program. Two types (full model and simplified model) of statistical geometric models (SGMs) for HL and EL prediction were developed by multiple regression analysis of the anthropometric measurements and driving postures on the HLs and ELs. The average adjusted R² and RMSE of the SGMs were .82 (± .06) and 25.7 (±3.3) mm, respectively. The SGMs showed accurate and stable prediction performance because the SGMs additionally incorporated the geometric relationships of HL and EL with anthropometric dimensions and joint angles. The SGMs would be useful to predict the HLs and ELs of drivers with various body sizes and joint angles in occupant packaging.     

The effect of lighting conditions and use of headlights on drivers’ perception and appraisal of approaching vehicles at junctions

Use of Daytime Running Lights (DRL) is mandatory in many countries for motorcycles, and in some for cars. However, in developing countries, DRLs may be optional or compliance low. The effect of car or motorcycle headlights and lighting conditions on Malaysian drivers’ ability to perceive and judge the safety of pulling out was investigated. Stimuli were photographs depicting either daytime or nighttime taken at a T-junction with approaching vehicles with headlights on or off. Headlights improved drivers’ ability to perceive cars and motorcycles in the nighttime photographs but not the daytime photographs, although this could be due to the bright weather in the photographs. Drivers judged it less safe to pull out when approaching motorcycles had headlights on than off, regardless of the lighting conditions, supporting the utility of DRL for motorcycles. Headlights did not affect judgements for cars, questioning the utility of DRL for cars.

Practitioner Summary: The effect of headlights and lighting conditions on drivers’ ability to perceive and make judgements about the safety of pulling out was investigated. Daytime Running Lights influenced drivers’ decision-making about the safety of pulling out in front of motorcycles, illustrating the importance of having automatic headlights equipped.