基于安全的前照灯提醒与自动切换装置系统设计

在夜晚开车时一旦使用远光灯,强烈的远光灯将会造成对方驾驶员炫目,导致对方驾驶员无法看清路面状况,出现行车安全隐患,甚至可能造成交通事故。在分析现有前照灯提醒技术的基础上,设计出新的提醒与自动切换装置系统。根据车辆前方光线的强弱,自动切换本车的远、近灯光,如果对方车辆没有将灯光切换到近光灯时,通过灯光的自动变换来提醒对方车辆。同时语音模块通过扬声器发出"对方强光,请减速慢行",提醒本车驾驶员注意行车安全。     

基于驾驶风格识别的AEB控制策略

为了考虑个性化的驾驶员特性对AEB控制策略的影响,提出了一种基于不同驾驶员驾驶风格的AEB控制策略。根据AEB危险场景下的驾驶员反应时间和情境风险度评价得分提出了驾驶风格识别系数的评价指标,通过驾驶员特性所呈现的人群聚类规律,将驾驶员分为谨慎型、普通型和激进型,同时引入危险系数来分级控制安全距离模型的制动减速度,完成紧急状况下的车辆制动。Simulink与Trucksim联合仿真结果表明,不同驾驶风格驾驶员对AEB系统介入时机与最小安全距离的心理预期具有不同的个性化需求,基于不同驾驶风格的AEB控制策略可以有效改善AEB系统的适应性,提高驾驶员的舒适性。     

基于驾驶行为的汽车节油提醒系统的应用

本文主要提出一种汽车节油提醒的方法及系统的应用,通过实时监测驾驶员的驾驶行为并对驾驶员的驾驶行为进行判断,当驾驶员处于非节油驾驶状态时进行警示并给出驾驶建议,从而帮助驾驶员改善驾驶习惯,达到汽车节油降耗的目的。本文针对不同的驾驶阶段采取的判断逻辑和提醒措施进行阐述。     

基于视点移动速度的视认距离确定方法

视认距离法是研究隧道入口行车安全的重要研究方法。基于驾驶员正常驾驶中,不同眼动行为的区别,利用眼动仪采集的视点坐标,提出了一种确定实车实验中视认距离的方法。利用典型实验数据,通过视点坐标的速度变化,确定开始发现障碍物的时刻,即开始视认的时刻。通过眼动仪前置摄像头视频,确定障碍物经过驾驶员的时刻。两时刻作差,得出从发现障碍物到经过障碍物的时间,乘车辆匀速行驶的速度,得出视认距离。该方法充分利用了眼动仪的视点采集技术,为研究自然驾驶实验中的驾驶员视认行为,提供了新的研究方法。     

安全行车五要诀

车辆在行驶过程中,驾驶员应时刻观察路面信息,及时做出反应。文中依据作者自己的驾车体会,归纳出了安全行车的“五要诀”。     

基于驾驶工作负荷的心生理指标研究

驾驶员是道路交通系统的核心,道路交通系统的设计应考虑驾驶员的行为特征需求。从驾驶员认知过程研究出发,分析了驾驶员认知、道路环境条件和驾驶工作负荷的关系,通过动态EEG、眼动、心电、皮电等生理心理测量评价驾驶工作负荷,并对具体的心生理学指标反应驾驶工作负荷的原理和规律进行了研究。     

新手驾驶员紧张度与道路线形及行驶速度相关性分析

本文首先从驾龄和驾驶里程数两个方面给出了新手驾驶员的界定,分析其驾驶特征及心理紧张度。通过室外试验,利用生物反馈仪等仪器,分析了道路条件对驾驶员脑电波的β波变化的影响,建立驾驶员β波增率与道路圆曲线半径R和车速V的相关模型;对比分析得出,在相同的半径和速度条件下,新手驾驶员β波增率比老驾驶员高出12%以上,在道路设计时应充分考虑新手驾驶员的生心理反应。     

满12分驾驶员的气质类型分布情况研究

采用陈会昌气质量表对北京市1 010名满12分驾驶员进行气质类型的调查。研究结果表明,在满12分驾驶员群体中,多血质、多血-粘液质、胆汁-多血-粘液质、胆汁-多血-抑郁质、胆汁-粘液-抑郁质、多血-粘液-抑郁质、胆汁-多血-粘液-抑郁质气质类型的驾驶员比例比在普通人群中的比例高。具有这些气质类型的驾驶员更易发交通违法行为。此外,胆汁质、抑郁质、胆汁-多血-粘液-抑郁三种类型的违法驾驶员的心理健康状况最差,需得到更多的心理辅导。     

公交车车门防夹系统设计与仿真

为实现通过声音提醒和控制车门自动开关来达到车门对乘客防夹的目的,以单片机AT89C52为控制模块、语音芯片ISD1700及蜂鸣器、指示灯作为语音提醒和报警装置、红外线传感器为检测装置、以继电器、电磁阀作为控制车门开关装置来设计公交车车门防夹系统。该系统可以使公交车车门处,在有人或物的情况下既能实现语音报警提醒驾驶员和乘客,又能及时自动地打开车门,防止夹到乘客或者物体,确保乘客出行的安全。     

公交车发动机舱温度预警系统的优化和应用

介绍了一款升级优化的公交车发动机舱温度预警系统。利用处理器分析公交车发动机舱的温度值和温度上升速率等信息，并综合车辆等因素进行研究，当发动机工作温度过高时，可通过语音、灯光等方式发出报警信息提醒驾驶员，避免危险发生。     

车队认知能力增强的通信区块间隙优化方法

为了在单车超越车队的过程中缩短超车车辆与车队间通信范围，减少车队通信压力，锁定影响车辆入队的关键车队区块，同时通过将待进入关键区块的车队进行间隙优化调整，为驾驶人提供定制化换道入队引导服务，提出了基于驾驶人超车风格特征参数的车队内信息传输关键区块锁定算法，通过分析影响驾驶人换道入队位置范围的关键因素，将驾驶人换道入队过程分为本车道速度调整过程与入队速度调整过程，利用非参数贝叶斯算法获取驾驶人超车换道特征数据并提出基于关键区块所在车队位置序列的车辆间隙优化调整策略。研究结果表明：超车车辆加速度、与前车预计碰撞时间、与车队相对速度是影响驾驶人换道入队范围的关键因素；通过非参数贝叶斯算法将超车车辆运行数据分类获取的驾驶人换道入队驾驶操作基元，可准确提供驾驶人行为特征关键参数；通过将驾驶人换道特征分为48个子类型，可锁定驾驶人换道入队范围且车队关键区块范围随着超车车辆与车队速度差值不同在各个特征类型上呈现不同变化趋势；针对驾驶人入队特征对待进入车队关键区块的车辆间隙进行优化调整，不仅可以为驾驶人提供可接受的驾驶辅助信息，同时减少了车队间隙产生过程中车辆加速度范围，提升了车队运行的舒适性。     

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.     

基于车辆运行数据的纯电动汽车用户驾驶风格识别与驾驶能耗分析

驾驶风格是用来体现驾驶员在车辆运行状态下对车辆操作的行为特征,对用户驾驶风格进行识别与分析,有利于推进智能驾驶的发展。根据基于116 辆纯电动汽车的车辆运行数据,通过主成分分析方法与K-means 聚类算法,对用户驾驶行为进行分类分析,对驾驶风格进行了分类识别。利用XGBoost 算法构建纯电动汽车驾驶行为与能耗输入模型,利用SHAP 对模型进行解释。结果表明,将驾驶风格聚为3 类具有较好的分类效果,可分别对应冷静型、普通型与激进型;当驾驶员的驾驶风格趋向于激进型时时,车辆的驾驶能耗越高,驾驶风格激进一个层级,车辆百公里电耗增加3~4倍。当驾驶员行车时,其车速越高,油门踏板踩得越深,车辆加速度的绝对值越大,车辆的驾驶能耗越高。驾驶员的驾驶风格越激进,车辆的驾驶能耗越高。     

针对自适应巡航的驾驶人风格辨识

车辆进入自适应巡航工况下行驶时,不同风格的驾驶人会对自适应巡航控制系统(Adaptive Cruise Control,ACC)有不同的需求。文章首先通过对不同驾驶人在9种跟随试验下获取的实验数据分析,选取表征驾驶人风格的驾驶特征参数;其次对所有驾驶人驾驶特征参数利用K-mean算法聚类分析,将驾驶人三类,并利用BP神经网络建立辨识模型对驾驶人风格进行辨识。结果表明;文章提出的方法可以较高的准确率对驾驶人风格进行分类,提高自适应巡航系统适应驾驶人的能力。     

Which factors lead to driving errors? A structural equation model analysis through a driving simulator experiment

As driving error is a main contributory factor of road accidents, its causes and consequences are of great interest in the road safety decision making process. This paper investigates several factors (including driver distraction, driver characteristics and road environment) that affect overall driving error behaviour and estimates a new unobserved variable which underlines driving errors. This estimation is performed with data obtained from a driving simulation experiment in which 95 participants covering all ages were asked to drive under different types of distraction (no distraction, conversation with passenger, cell phone use) in rural and urban road environment, as well as in both low and high traffic conditions. Driving error was then modeled as a latent variable based on several individual driving simulator parameters. Subsequently, the impact of several risk factors such as distraction, driver characteristics as well as road environment on driving error were estimated directly. The results of this complex model reveal that the impact of driver characteristics and area type are the only statistically significant factors affecting the probability of driving errors. Interestingly, neither conversing with a passenger nor talking on the cell phone have a statistically significant impact on driving error behaviour which highlights the importance of the present analysis and more specifically the development of a measure that represents overall driving error behaviour instead of individual driving errors variables.     

An adaptive lateral preview driver model

Successful modelling and simulation of driver behaviour is important for the current industrial thrust of computer-based vehicle development. The main contribution of this paper is the development of an adaptive lateral preview human driver model. This driver model template has a few parameters that can be adjusted to simulate steering actions of human drivers with different driving styles. In other words, this model template can be used in the design process of vehicles and active safety systems to assess their performance under average drivers as well as atypical drivers. We assume that the drivers, regardless of their style, have driven the vehicle long enough to establish an accurate internal model of the vehicle. The proposed driver model is developed using the adaptive predictive control (APC) framework. Three key features are included in the APC framework: use of preview information, internal model identification and weight adjustment to simulate different driving styles. The driver uses predicted vehicle information in a future window to determine the optimal steering action. A tunable parameter is defined to assign relative importance of lateral displacement and yaw error in the cost function to be optimized. The model is tuned to fit three representative drivers obtained from driving simulator data taken from 22 human drivers.     

Preliminary classification of driving style with objective rank method

This paper puts forward a method of preliminary driver classification, which applies two-criteria based analysis of the phenomenon of driving style. The resulting rank enabled the author to order the drivers according to their driving style, from the most active to the extremely mild. The most active driver in the sense of the two-criteria analysis is the one who covered a test stretch of the road the fastest while changing the position of the accelerator pedal most intensively. The classification of the drivers, along with the measurement data registered during road tests, will subsequently provide the foundation for later modeling of a dynamic classification of the driving style, in the form of a recurrent learning neural network of the Elman’s type.     

驾驶风格对驾驶行为的影响

为了揭示驾驶风格对驾驶行为的影响规律,进而提取表征驾驶风格的特征参数,对不同风格驾驶人在感知层和操作层的驾驶行为数据进行了量化分析。首先,基于驾驶行为问卷对18名中国非职业驾驶人进行了驾驶风格问卷调查,并采用主成分分析、K-均值聚类等方法将被试驾驶人分为谨慎型、正常型和激进型3种类型。接着,被试驾驶人在搭载了SmartEye眼动仪的驾驶模拟器上开展了高速公路行车环境下的驾驶试验,同步采集了感知层的视觉特性参数和操作层的驾驶绩效参数,并采用判断抽样的方式将驾驶样本按照驾驶风格和驾驶模式（换道意图和车道保持）进行了划分,共选取了810组有效样本。最后,采用方差分析法分析了不同风格驾驶人在不同驾驶模式下的注视行为、扫视行为、横向控制特性、纵向控制特性方面相关参数的差异显著性,并提取了不同风格间存在显著差异的参数作为表征驾驶风格的特征参数。研究结果表明：驾驶风格越激进,驾驶人对周围环境关注越少,对车辆的横向控制稳定性越差,急加速和急减速行为发生的频次越高;不同风格驾驶人在意图时窗内对后视镜的注视次数（p=0.002）、方向盘转角熵值（p=0.04）、加速踏板开度（p=0.01）、制动踏板开度（p=0.02）这4个参数的差异均较为显著,因此可作为表征驾驶风格的特征参数。     

考虑肌电信号的驾驶人弯道操纵行为分析

驾驶人是"人-车-路"闭环系统中的核心。近年来，研发人性化、个性化的汽车驾驶辅助系统逐渐成为行业热点。为了更加透彻地理解弯道驾驶行为特性，为弯道驾驶辅助系统提供功效评估与优化，提出了一种考虑肌电信号的驾驶人弯道行驶过程操纵行为分析方法。招募12名驾驶人在试验场标准路面上进行实车试验，其中包含6名专业试车师与6名普通驾驶人，要求驾驶人分别以30，40，50 km·h^-1的不同初速度驶入U形弯道并自由驾驶。试验过程中记录驾驶人颈部肌电信号数据和车辆运动状态数据，分析转弯行驶车辆侧向运动对不同驾驶能力的驾驶人生理体验的影响，同时进一步探讨不同类型驾驶人在不同入弯速度条件下颈部肌电信号与侧向加速度的关联差异特性。试验结果表明：相同工况下，专业驾驶人和普通驾驶人颈部肌电特征值存在显著差异，专业驾驶人颈部肌电信号特征与车辆侧向加速度呈现一定的线性关系；随着驾驶任务难度的增加，驾驶能力好的驾驶人能够较好地适应任务的变化，在进行纵侧向耦合操纵时能够较好地协调身体生理反应与车辆侧向运动保持较好的关联特性。研究成果为进一步探索并完善驾驶体验评价方法提供了新的研究思路，同时，可为汽车辅助驾驶系统功能设计与智能汽车行驶性能的用户体验测评提供技术支撑。     

Comparative study between Korea and UK: relationship between driving style and real-world fuel consumption

It is known that differences in driving styles have a significant impact on fuel efficiency and driving styles are affected by various factors such as driver characteristics, street environment, traffic situation, vehicle performance, and weather conditions. However, existing knowledge about the relationship between driving style and fuel consumption is limited. Thus, the aim of this study was to analyze the relationship beteen driving style and fuel consumption. The analysis presented in this paper used data from three on-road experiments were conducted independently in two different countries, i.e. South Korea and the United Kingdom. In this study, 91 participants, consisting 44 UK drivers and 47 Korean drivers, were asked to drive approximately 28 km of UK road and 21 km of Korean road, respectively. Driving data, including real-time fuel consumption, vehicle speed, and acceleration pedal usage were collected. The results suggested that driving styles including average vehicle speed and average throttle position were highly correlated with the real-world fuel consumption, and the cultural factors, e.g. road environment, traffic design, and driver’s characteristics affected the driving styles and, consequently, fuel efficiency.