发动机冷却风扇温控液力驱动系统

本文介绍了发动机冷却风扇温控系统，这种新型的控制系统具有自动测量，微机控制，液压驱动，无级变速等特点，可以使发动机在最佳温度下工作，文中详细介绍了系统构成，测控元件选择及软件设计。     

基于支持向量数据描述的驱动桥异响检测

针对驱动桥异响检测缺乏故障样本的问题,提出了异响检测的单值分类法———支持向量数据描述法(SVDD)。这种方法只需要正常运行状态的数据样本,就可以建立单值分类器,区分出正常和异常状态。试验中,提取驱动桥振动信号频谱的二维谱熵作为特征指标,输入到SVDD分类器。结果表明SVDD算法计算效率高,分类效果好,可以满足在线检测的要求。     

基于CVT的混联式电动汽车动力切换平稳性研究

针对四轮驱动越野车提出了一种基于金属带式无级自动变速器(CVT)的混联式结构,其动力切换平稳性是这种混联式电动汽车必须解决的关键技术之一。通过发动机、电机、离合器和CVT的协调控制可以使系统在驾驶员发出加速命令时快速响应以及在模式切换时平稳,这将保证驾驶员在车辆切换过程中没有异常感觉。     

大功率车载取力自发电系统结构研究

为充分解决装备野外环境下电源保障问题,为系统中的设备提供不低于80 kW供电电源,对比了两种供电技术方案,择优确定80 kW车载取力发电的供电形式;为保证动力输出,采用分动器取力;为保证布局的紧凑性,采用二级传动形式,通过传动轴传动和同步带传动的组合传动模式实现动力传递;电机安装平台采用铰接调整式结构,实现传动带的松紧...     

基于客户关联的纯电动轻卡驱动电机可靠性台架试验研究

为研究轻卡BEV驱动电机可靠性台架试验标准与实际客户的关联,文章基于整车CAN网络,采集实际客户日常行驶过程中的电机扭矩、转速等数据,通过Ncode软件计算出电机的扭矩谱。根据Miner准则,计算出电机20万公里的疲劳伪损伤。对比电机可靠性台架试验的电机伪损伤,确定电机台架试验标准是否满足客户需求。     

Study on driver model for hybrid truck based on driving simulator experimental results

In this paper, a proposed car-following driver model taking into account some features of both the compensatory and anticipatory model representing the human pedal operation has been verified by driving simulator experiments with several real drivers. The comparison between computer simulations performed by determined model parameters with the experimental results confirm the correctness of this mathematical driver model and identified model parameters. Then the driver model is joined to a hybrid vehicle dynamics model and the moderate car following maneuver simulations with various driver parameters are conducted to investigate influences of driver parameters on vehicle dynamics response and fuel economy. Finally, major driver parameters involved in the longitudinal control of drivers are clarified.     

Alcohol and traffic accidents in Japan

Alcohol induces many traffic accidents. Recently, the number of incidences of driving under the influence of alcohol (DUIA) has decreased in Japan; however, the number of drink-driving situations remains constant. Toughening the penalty has not been a particularly effective method for reducing the number of DUIAs. To reduce accidents resulting from DUIA, an approach other than sanctions may be necessary. Alcohol impairs attentional control for simple tasks and increases the propensity for risk-taking behavior; these are the major perils of DUIA. There are many risk factors for DUIA, including area, time, personal traits, the use of other substances, and alcohol use disorder. Each individual has a different risk factor for drink-driving, so it is important to provide more education for individuals and interventions for alcohol usage according to each risk factor.     

Vehicle sensor data-based analysis on the driving style differences between operating indoor simulator and on-road instrumented vehicle

Abstract

Indoor simulator and on-road instrumented vehicle are the most popular ways to analyze driving behaviors by using collected Vehicle Sensor Data (VSD). However, for a same driver, the driving performance could be different in the real world and in the simulated world. Even though many studies have been conducted to discover the differences of driving behaviors in these two circumstances, little research has focused on analyzing the differences in driving style, which can provide more integrated knowledge of a driver from the natural structure, stimulus–response mechanism, of driving behaviors. Therefore, in this paper, the driving styles in both the real world and the simulated world are extracted by implementing the nonnegative matrix factorization method on the collected VSD data. Through this analysis, the driving style differences can be quantitatively described and discussed in detail. It is found that the drivers tend to be more unstable and sometimes aggressive when driving the simulator and the deviation in the perception of temporal gap in two circumstances is also discovered. The research findings are particularly valuable to calibrate the driving simulator and construct more reliable driving behavior models.     

基于深度学习的驾驶员状态识别

提出了基于驾驶员脸部及周围信息的驾驶员状态检测方法。文章通过实车摄像头采集了驾驶员驾驶状态视频数据,利用Dlib和OpenCV库对采集的驾驶员图像进行脸部检测,基于驾驶员脸部数据建立了深度学习数据集,然后基于该数据集设计了一种卷积神经网络模型FaceNet,利用PyTorch深度学习框架在数据集上对模型进行训练,最终得到了有较高准确率的驾驶员状态检测模型,其可识别抽烟、睡觉、左手打电话和右手打电话四种驾驶员状态。