驾驶适性检测与行车安全分析

根据驾驶适性理论，论述了进行驾驶适性检测的必要性，阐述了驾驶适性检测的基本内容，指出汽车驾驶员的心理和生理素质与行车安全的关系是驾驶适性研究的重要内容。     

军车驾驶员驾驶适性检测管理信息系统的设计

文章从事故倾向性理论出发，从心理学和人机工程学角度，提出了军车驾驶员驾驶适性检测模式和甜蜜生评价模型，运用这些模型开发了军车驾驶员驾驶适性检测的信息管理系统。该系统的开发为军车驾驶员和科学管理提供了依据．     

驾驶适性检测计算机管理系统的开发研究

本文从驾驶员事故倾向性理论出发，从心理学和人机工程学角度，阐述了驾驶员职业适宜性理论，在实验和大量的事故倾向性调查资料基础上，建立了驾驶适性检测模式和适性的评价模型。运用此模型开发出适性检测的计算机管理系统。通过对部分驾驶员的试运行，结果与理论分析相吻合。系统软件的开发为驾驶适性研究 系统化，科学化奠定了基础。     

我国职业汽车驾驶员适性检测体系的研究

本文通过对我国具有代表性的十四个省（市）２０００余名驾驶员和随机抽样调查。验证了驾驶适性我国驾驶员群体中的客观存在性。采用多元统计分析方法对各项心理、心理指标检测数据进行处理分析，建立了一套适合国情的驾驶适性检测体系。     

兰州军区举办军车驾驶适性检测业务培训

<正>2010月10～12日,兰州军区举办了l期军车驾驶适性检测培训。培训采取专家理论授课、现场设备实践、座谈讨论交流等相结合的方法,对来自全区各司训机构主管训练的助理员和军区军车检测站检测骨干进行了系统的业     

不同微观驾驶行为对基本路段交通流运行特征的影响

为了提高道路服务水平,减少道路交通事故,改善车辆设计,开发车载安全装置通过驾驶模拟器和交通仿真相结合的方法,深入探讨个体驾驶行为交通流特征的影响.首先,选取反映驾驶员特征的静态驾驶适性和动态驾驶适性2类指标,通过因子分析、主成份分析及聚类分析方法对驾驶行为特征进行提取,构建不同类型的驾驶行为特征分类.其次,利用驾驶模拟器采集到的实验数据,对不同类型驾驶行为的仿真模型参数进行校准.最后,通过交通仿真技术对不同类型的驾驶行为下交通流特征研究,主要集中在不同车道位置、不同行为下的交通流特征差异.研究结果表明,激进的驾驶行为仿真产生的最大交通量(2200 pcu/h)中等驾驶行为(1800 pcu/h)保守驾驶行为(1700 pcu/h),但激进驾驶行为下的交通流较其他行为显得更加不稳定.因此,提高道路安全水平,需针对呈现此类驾驶行为特征的驾驶员进行重点教育及培训.     

基于生物电信号的驾驶疲劳检测方法

驾驶疲劳检测技术是当前安全辅助驾驶技术的一个研究热点,其中基于生物电信号的检测方法最直接。本文主要介绍驾驶疲劳的生成机理、检测方法的分类和基于脑电、心电、眼电和肌电等主要生物电信号检测方法的研究现状。最后,针对当前研究领域的不足给出了建议,以供研究者参考。     

巡航控制系统及自适应性巡航控制系统

巡航控制系统可控制汽车自动以恒定的速度行，有利于减轻驾驶员的操作负担、节省燃油和提高驾驶舒适性。介绍了巡航控制系统及基于其上的自适性巡航控制系统的组成、工作原理及各主要部件的功能。     

自动驾驶仿真系统中网络安全测试方法研究

本文介绍了高级自动驾驶汽车网络安全的检测方法,形成将网络安全模型与车辆仿真测试模型相结合的测试框架。仿真测试综合汽车动力学模型、路况、天气、感知等多种模型,能够全面系统地测试自动驾驶系统的功能和性能。通过分析得到的针对自动驾驶的网络安全威胁模型与仿真模型相结合,可以体系化地验证自动驾驶系统对抗网络安全威胁的能力,是推动自动驾驶网络安全检测的积极尝试。     

机器视觉在汽车驾驶辅助系统中的应用

机器视觉是汽车驾驶辅助系统应用领域的重要技术,文章主要综述了机器视觉在车道检测技术、交通标志识别技术、车辆识别技术、行人检测技术和驾驶员状态检测技术等领域的应用,着重介绍了机器视觉技术在上述领域目前的研究现状,为机器视觉在汽车驾驶辅助领域的进一步研究提供了参考。     

Analysis of Driver's Decelerating Strategy in a Car-following Situation

This report describes a decelerating driver-model expressed by driving mode transition in car-following situations. The assumptions for constructing the model are that decelerating strategy of a driver is classified into several simple driving modes and that a driver changs his driving modes based on his perceptible characteristics and experiential rules. Deceleration action is divided into three states; following, standing and braking, which are applied to the model. The model has two paths for driver's decelerating action, one of which is selected by the driver based on the perceptible characteristics and experiential rules. The suitability of the model has been experimentally verified.     

汽车操纵稳定性的模拟器闭环评价与试验方法

本文以汽车操纵稳定性的闭环评价为目的，利用吉林大学汽车动态模拟国家重点实验室的开发型驾驶模拟器（ADSL驾驶拟器）进行了汽车操纵稳定性的闭环试验研究。提出汽车操纵稳定性的评价应是包含驾驶员在内的闭环系统评价，并建立了汽车操纵稳定性的模拟器闭环试验方法，利用模拟器试验对汽车操纵稳定性综合评价指标的合理性进行了验证。     

Dynamic stability of forklift trucks in cornering situations: parametrical analysis using a driving simulator

This paper describes the examination of the vehicle dynamics and stability of four-wheeled forklift trucks (FLTs) in cornering situations. Cornering at excessive speed is one major reason for fatal accidents with forklifts caused by lateral tipover. In order to increase the lateral stability of this kind of working machinery, the influence of certain important design properties has been studied using an appropriate vehicle simulation model and a driving simulator. The simulation model is based on a multi-body system approach and includes submodels for the propulsion system and the tyres. The driving behaviour of the operator has not been modelled. Instead, a driving simulator has been built up and a real human driver was employed for ensuring adequate and realistic model input. As there have not been any suitable standardised test manoeuvres available for FLTs, a new driving test has been developed to assess the lateral stability. This test resembles the well-known J turn/Fishhook turn, but includes a more dynamic counter-steering action. Furthermore, the dimensions of the test track are defined. Therefore, the test is better adapted to the driving dynamics of forklifts and reflects the real driver behaviour more closely. Finally, a parametrical study has been performed, examining the influence of certain important technical properties of the truck such as the maximum speed, the position of centre of gravity, rear axle design features and tyre properties. The results of this study may lead to a better understanding of the vehicle dynamics of forklifts and facilitate goal-oriented design improvements.     

Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles

A highly accurate and reliable vehicle position estimation system is an important component of an autonomous driving system. In generally, a global positioning system (GPS) receiver is employed for the vehicle position estimation of autonomous vehicles. However, a stand-alone GPS does not always provide accurate and reliable information of the vehicle position due to frequent GPS blockages and multipath errors. In order to overcome these problems, a sensor fusion scheme that combines the data from the GPS receiver and several on-board sensors has been studied. In previous researches, a single model filter-based sensor fusion algorithm was used to integrate information from the GPS and on-board sensors. However, an estimate obtained from a single model is difficult to cover the various driving environments, including urban areas, off-road areas, and highways. Thus, a multiple models filter (MMF) has been introduced to address this limitation by adapting multiple models to a wide range of driving conditions. An adaptation of the multiple model is achieved through the use of the model probability. The MMF combines several vehicle models using the model probabilities, which indicate the suitability of the current driving condition. In this paper, we propose a vehicle position estimation algorithm for an autonomous vehicle that is based on a neural network (NN)-based MMF. The model probabilities are determined through the NN. The proposed position estimation system was evaluated through simulations and experiments. The experimental results show that the proposed position estimation algorithm is suitable for application in an autonomous driving system over a wide range of driving conditions.     

酒后驾驶行为对驾驶安全性影响研究

酒后驾驶是造成机动车道路伤害的危险因素,对道路交通安全的影响非常严重。本文通过一系列适宜性检测试验,比较饮酒前与饮酒后以及随饮酒量的逐步变化酒精对受试者驾驶能力的影响。研究发现,饮酒后受试者心理活动受到影响,反应变差,感知变差,手和脚的动作变迟缓,处置判断错误率上升,并随着饮酒量的逐渐增加,驾驶技能逐渐恶化。     

整车电平衡试验台设计

介绍整车电器系统室内电量平衡试验台的系统设计原理和试验方法。以某车型在常温、高速行驶工况下的试验数据曲线为例,初步验证了该试验台能够较好地模拟车辆在实际道路行驶时的整车电器系统充放电电量状态。     

Practicability study on the suitability of artificial,neural networks for the approximation of unknown steering torques

For steer-by-wire systems, the steering feedback must be generated artificially due to the system characteristics. Classical control concepts require operating-point driven optimisations as well as increased calibration efforts in order to adequately simulate the steering torque in all driving states. Artificial neural networks (ANNs) are an innovative control concept; they are capable of learning arbitrary non-linear correlations without complex knowledge of physical dependencies. The present study investigates the suitability of neural networks for approximating unknown steering torques. To ensure robust processing of arbitrary data, network training with a sufficient volume of training data is required, that represents the relation between the input and target values in a wide range. The data were recorded in the course of various test drives. In this research, a variety of network topologies were trained, analysed and evaluated. Though the fundamental suitability of ANNs for the present control task was demonstrated.     

基于VP分布的乘用车运行工况设计方法研究

提出了一种用于设计汽车运行工况的新方法,以速度和功率（velocityandpower,VP）分布一致性为设计目标,建立满意准则模型进行汽车运行工况设计。在分析处理实车试验数据的基础上,设计开发了长春市乘用车运行工况,并且通过分析设计工况的油耗特性,验证了基于VP分布的运行工况设计方法的合理性。     

波纹钢板管涵在A30公路中的应用

场拼装式波纹钢板管涵结构属新型技术,通过对该管涵结构在A30公路北段工程中的应用效果的调查,表明波纹钢板管涵能适应软土地基路堤的沉降变形、有利于改善软土地基结构物与路堤交界处的“错台现象”,提高行车的舒适度与安全性。     

Modeling and control of engine starting for a full hybrid electric vehicle based on system dynamic characteristics

This paper focuses on the dynamic modeling and control of engine starting for a Full Hybrid Electric Vehicle (FHEV) consisting of an Integrated Starter Generator (ISG) and Dual Clutch Transmissions (DCTs). The dynamic characteristics of the engine, the ISG motor and the main clutch are analyzed respectively. The dynamic models of the main components of the powertrain system are also established taking the system dynamic characteristics into consideration. The FHEV dynamic model of engine starting during electric driving mode has been investigated in detail. The coordinated control strategy of engine starting has been proposed based on the powertrain system dynamic characteristics. The simulation for the engine starting control during electric driving mode has been performed based on the Matlab/Simulink platform. The simulation results show that the proposed control strategy satisfies the requirements of response and smoothness during engine starting process. Furthermore, a bench test has been carried out to analyze the system characteristics during engine starting process. The test data is highly agreeable to the simulation data and the effectiveness of engine starting control strategy is validated by the comparison between simulation results and the test data.