一种试车场驾驶行为的监控方法

文章设计了一种试车场特定试验道上驾驶行为的监控方法,利用总线数据记录仪实时在总线上采集处理行驶数据,并评估驾驶行为是否满足试验规范的要求.该方法可用于监控和保障耐久试验质量、分析驾驶行为和评估驾驶能力.     

个体差异对转向指标疲劳辨识能力的影响分析

驾驶人个体差异是影响疲劳驾驶辨识准确性的重要因素。为了探究个体差异与基于转向行为的疲劳辨识效果之间的关系,量化个体差异对转向特征指标疲劳辨识能力的影响程度,通过自然驾驶试验,采集被试在清醒状态和疲劳状态下的真实驾驶行为数据,结合观察员问询打分和被试面部视频得到疲劳水平信息。设置双层滑动时间窗对每位驾驶人的自然驾驶行为数据进行处理,挖掘出9个疲劳驾驶转向特征指标。对每位驾驶人清醒和疲劳状态下的指标样本进行Wilcoxon检验,用Wilcoxon检验的|Z|值表示指标对驾驶疲劳的分类性能。以清醒和疲劳状态下指标有显著差异的被试数目最多为优化目标,得到指标最优的双层时间窗设定值。将|Z|值最大的被试逐个与其他被试两两组合,对清醒和疲劳状态下混合两被试指标样本数据进行Wilcoxon检验,得到被试组合指标的|Z|值。计算两被试的综合个体差异值,基于线性模型拟合两被试组合Wilcoxon检验的|Z|值和个体差异值,以拟合直线的斜率绝对值|k|量化个体差异对指标疲劳辨识能力的影响。研究得到基于自然驾驶行为数据的9个疲劳驾驶转向特征指标的最优时间窗,发现指标对疲劳驾驶的分类性能存在个体差异,并且指标的疲劳辨识能力会随个体差异增加而降低,进而影响基于转向行为指标疲劳辨识的准确性,其中方向盘转角下四分位标准差（X_q1std）的斜率绝对值最大（1.17）,方向盘转角标准差（X_jstd）的斜率绝对值最小（0.44）,疲劳辨识能力受个体差异影响最大和最小的指标分别是X_q1std和X_jstd。研究结果可为利用自然驾驶行为数据的疲劳驾驶特征提取及考虑个体差异的疲劳驾驶建模提供参考。     

Evaluation of automotive forward collision warning and collision avoidance algorithms

Collision warning/collision avoidance (CW/CA) systems target a major crash type and their development is a major thrust of the Intelligent Vehicle Initiative. They are a natural extension of adaptive cruise control systems already available on many car models. Many CW/CA algorithms have recently been proposed but the existing literature mainly focuses on algorithm development. Evaluations of these algorithms have been usually based on subjective ratings. The main contribution of this paper is the utilization of a naturalistic driving data set for the evaluation of CW/CA algorithms. We first collect manual driving data from the ICCFOT project, then process the data by Kalman smoothing, and finally identify 'threatening' and 'safe' data sets according to vehicle brake inputs and vehicle range behavior. Five CW/CA algorithms published in the literature are evaluated against the identified data sets. The performance of these algorithms is determined through a performance metric commonly used in signal detection and information retrieval under unbalanced data population.     

Human driving data-based design of a vehicle adaptive cruise control algorithm

This paper presents a vehicle adaptive cruise control algorithm design with human factors considerations. Adaptive cruise control (ACC) systems should be acceptable to drivers. In order to be acceptable to drivers, the ACC systems need to be designed based on the analysis of human driver driving behaviour. Manual driving characteristics are investigated using real-world driving test data. The goal of the control algorithm is to achieve naturalistic behaviour of the controlled vehicle that would feel natural to the human driver in normal driving situations and to achieve safe vehicle behaviour in severe braking situations in which large decelerations are necessary. A non-dimensional warning index and inverse time-to-collision are used to evaluate driving situations. A confusion matrix method based on natural driving data sets was used to tune control parameters in the proposed ACC system. Using a simulation and a validated vehicle simulator, vehicle following characteristics of the controlled vehicle are compared with real-world manual driving radar sensor data. It is shown that the proposed control strategy can provide with natural following performance similar to human manual driving in both high speed driving and low speed stop-and-go situations and can prevent the vehicle-to-vehicle distance from dropping to an unsafe level in a variety of driving conditions.     

Evaluation of automotive forward collision warning and collision avoidance algorithms

Collision warning/collision avoidance (CW/CA) systems target a major crash type and their development is a major thrust of the Intelligent Vehicle Initiative. They are a natural extension of adaptive cruise control systems already available on many car models. Many CW/CA algorithms have recently been proposed but the existing literature mainly focuses on algorithm development. Evaluations of these algorithms have been usually based on subjective ratings. The main contribution of this paper is the utilization of a naturalistic driving data set for the evaluation of CW/CA algorithms. We first collect manual driving data from the ICCFOT project, then process the data by Kalman smoothing, and finally identify ‘threatening’ and ‘safe’ data sets according to vehicle brake inputs and vehicle range behavior. Five CW/CA algorithms published in the literature are evaluated against the identified data sets. The performance of these algorithms is determined through a performance metric commonly used in signal detection and information retrieval under unbalanced data population.     

Deviated state detection method in driving around curves based on naturalistic driving behavior database for driver assistance systems

The objective of this study is to propose the indices which detect the deviated state of drivers while driving by considering drivers’ judgment process and using road environment and naturalistic driving behavior database. To realize this objective, drivers’ speed choice behavior around curve situations was focused and the speed choice process was formulated. Moreover, a deviated state detection method considering the formulated speed choice process around curve situations was proposed and the validity of the method was examined.     

驾驶人驾驶决策机制遵循最小作用量原理

为提升智能汽车的自主决策能力，使其能够学习人的决策智慧以适应复杂多变的道路交通环境，需要揭示驾驶人决策机制。首先通过对自然驾驶数据的分析，发现在车辆行驶过程中能够反映驾驶人决策行为的主要运动特征参数存在极值现象，而产生极值现象的内在动因是驾驶人遵循“趋利避害”的基本决策机制，即驾驶过程中驾驶人力图实现机动性和安全性综合性能最优。受自然界包括物理和生物行为上的众多极值现象遵循最小作用量原理的启发，提出驾驶人决策机制遵循最小作用量原理的假设。随后建立抽象描述驾驶过程的物理模型，并提出最小作用量决策模型（Least Action Decision-making Model，LADM），通过与传统驾驶决策模型（经典跟车模型和换道模型）对比，分析结果显示LADM模型更具通用性。最后开展了实车试验，采集20名驾驶人在自由行驶、跟车行驶和邻车切入3种工况下的试验数据，分析计算并检验了不同驾驶人行车过程的理论最小作用量和实际作用量。试验结果表明：驾驶人在驾驶过程中的实际作用量与最小作用量之间无显著性差异，体现出驾驶人在行车过程中对安全和高效具有共性追求，验证了驾驶人决策机制遵循最小作用量原理。     

Belief and fuzzy theories for driving behavior assessment in case of accident scenarios

The estimation of the overspeed risk before the accident is among the main goals of this paper. The proposed method uses the Energy Equivalent Speed (EES) to assess the severity of an eventual accident. However, the driver behavior evaluation should take into account the parameters related to the Driver, the Vehicle and the Environment (DVE) system. For this purpose, this paper considers a two-level strategy to predict the global risk of an event using the Dempster-Shafer Theory (DST) and the Fuzzy Theory (FT). This paper presents two methods to develop the Expert Model-based Basic Probability Assignment (EM based BPA), which is the most important task in the DST. The first one is based on the accident statistics and the second method deals with the relationship between the Fuzzy and Belief measurements. The experimental data is collected by one driver using our test vehicle and a Micro-intelligent Black Box (Micro-iBB) to collect the driving data. The sensitivity of the developed models is analysed. Our main evaluation concerns the Usage Based Insurance (UBI) applications based on the driving behavior. So, the obtained masses over the defined referential subsets in the DST are used as a score to compute the driver’s insurance premium.     

An investigation of factors contributing to major crash types in Japan based on naturalistic driving data

Traffic accident statistics suggest that the human errors contributing to major crash types in Japan are predominantly failures in safety confirmation and hazard recognition that result in delayed response. A naturalistic driving data acquisition system was developed to investigate the human factors that contribute to such accidents. A preliminary analysis was performed to evaluate the efficiency of the collected naturalistic data. An analysis of vehicle-to-motorcycle conflict data demonstrated that types of recognition failure differ by types of traffic situation encountered. This result suggests that naturalistic driving data can provide valuable information for investigating the factors that contribute to the risk of human error.     

Characteristic analysis for cognition of dangerous driving using automobile black boxes

Automobile black boxes are devices that collect information regarding vehicle operation and the driver’s operating situation in the case of a traffic accident. The information collected from the automobile black box, which can also be used during normal driving, can provide information about dangerous driving cognition. This study was designed to analyze characteristics of dangerous driving data and build a dangerous driving cognition system as follows. First, dangerous driving is divided into four types by considering the vehicle’s movement, such as acceleration, deceleration, turning and statistical data of traffic accidents. Second, dangerous driving data were collected by vehicle tests using the automobile black box, and characteristics of the driving data were analyzed to classify dangerous driving. Third, a standard threshold was chosen to recognize dangerous driving, and an algorithm of dangerous driving cognition was created. Finally, verification was conducted by vehicle tests with automobile black boxes embedded with the developed algorithm. The presented recognition methods of dangerous driving can be used for on/off-line management of drivers and vehicles. Scientific traffic accident databases can be built with this driving and accident information, and can be used in various industrial areas.     

Anticipatory speed control model applied to intersection left turns

This paper presents a new speed control model applicable to real-world driving. It is developed for intersection left turns and is based on anticipated acceleration reference (AAR) inputs. This addresses combined visual anticipation of lateral and longitudinal accelerations for the approach to an intersection where both stopping and turning outcomes are possible. The relationship between the AAR and the resulting vehicle accelerations are studied for both stopping and turning events using naturalistic driving data. A closed-loop model is developed, including braking to rest when the left turn is not attempted and for the turn and exit stages when it is. Parameter ranges are estimated, and as a demonstration of model applicability, Monte Carlo simulations are conducted to generate representative left turns using a full simulation model. Extension of the AAR model to other speed control problems, for example, driving on curved roads, is also discussed.     

Development of a driving cycle for the measurement of fuel consumption and exhaust emissions of automobiles in Bangkok during peak periods

The exhaust emissions and fuel consumption rates of newly registered automobiles in Thailand are currently assessed using the standard driving cycle of the Economic Commission of Europe (ECE). Because of the highly different driving conditions, the assessment results may not reflect realistic amounts of emissions and fuel consumption for vehicles in Bangkok traffic, which is well known for its congestion. The objective of this study is therefore to develop a new driving cycle for vehicles traveling on Bangkok’s main roads during peak traffic hours. This paper first presents the development of a method for selecting representative road routes with traffic conditions that are representative of traffic in Bangkok for conducting real-world driving speed data collection. These real-world data are obtained by driving a car equipped with a speed-time data logger along those selected road routes. Several driving characteristics, including various profiles of microtrips, are analyzed from the collected speed-time data, and a number of target driving parameters are then defined for use as a set of criteria to justify the best driving cycle. A procedure for generating driving cycles from the analyzed real-driving data is also developed, and the method to select the cycle that is most representative of Bangkok traffic is described. Comparisons found in the study show that the target driving parameters of the newly developed driving cycle are much closer to those obtained from the real-world measured data than those calculated from the presently used European drive cycle. This would imply that the obtained driving cycle will produce more realistic results of the emissions and fuel consumption assessment tests for vehicles traveling in Bangkok. The methods developed in this study for route selection and driving cycle construction can easily be adopted by other big cities to develop their own vehicle driving cycles. Furthermore, although the developed methods are for passenger cars, similar approaches can be applied to develop driving cycles for other types of vehicle, such as city buses and pick-up trucks.     

驾驶模拟环境中激进驾驶行为的影响因素分析

为了分析驾驶人在驾驶模拟试验过程中出现的相对实际驾驶的激进驾驶行为的影响因素,采用计划行为理论构建心理认知模型。基于计划行为理论设计问卷调查私家车驾驶人对"在驾驶模拟过程中激进驾驶"行为的信念、态度、主观规范、行为感知控制、意向与行为。采用结构方程模型得到观察变量与基本构念以及基本构念内部的相关关系,并最终分析得到影响驾驶人激进驾驶行为的主要因素。通过先导性调查问卷以及正式调查问卷的投放,最终得到217个有效的样本。研究结果表明：心理认知模型具有良好的适配性,其卡方自由度比为1.802,RMSEA值为0.062;态度、行为感知控制是影响驾驶人行为的主要因素,主观规范对行为的影响相对较小;各信念与对应的态度、主观规范及行为感知控制之间存在显著关联,各信念的测量模型的适配性良好,卡方自由度比、RMSEA等指标基本满足要求。采用完整的计划行为理论结构同时从标准获取构念和自行获取构念的角度解释了驾驶人对"在驾驶模拟过程中激进驾驶"行为的心理认知,研究成果可用于驾驶模拟-自然驾驶行为数据差异性控制,驾驶模拟试验规范化方法构建。     

Trip characteristics impact on the frequency of harsh events recorded via smartphone sensors

The Internet of Things (IoT) constantly offers new opportunities and features to monitor and analyze driver behavior through wide use of smartphones, effective data collection and Big Data analysis, resulting in assessment and improvement of driver behavior and safety. The objective of the present study is to investigate the impact of detailed trip characteristics on the frequency of harsh acceleration and harsh braking events through an innovative smartphone application developed within the framework of BeSmart project. A 200-driver naturalistic experiment spanning 12 months is carried out since July 2019. During the first two months, participants were asked to drive in the way they usually did, without receiving any feedback on their driving behavior from the application. Over the subsequent two months, participants were provided with personalized feedback, a trip list and a scorecard regarding their driving behavior, allowing them to identify their critical deficits or unsafe behaviors. Some of the most important risk factors, such as speed and driving above the speed limit, usage of mobile phone while driving and harsh events (acceleration and braking) are recorded through the application and subsequently analyzed. Generalized Linear Mixed-Effects Models were fitted to the trips of car drivers who made frequent trips for both experiment phases in order to model the frequencies of harsh events. Results indicate that maximum speed, the percentage of speeding duration and total trip duration are positively correlated with both harsh acceleration and harsh braking frequencies. On the other hand, the exposure metric of total trip distance was found to be negatively correlated with both harsh event types. A small positive correlation of the percentage of mobile use duration with harsh accelerations was also detected.     

Development of E-rickshaw driving cycle (ERDC) based on micro-trip segments using random selection and K-means clustering techniques

In India, auto rickshaws are the most convenient and cheapest mode of near-to-door transport in both rural and urban areas. Such vehicles powered with internal combustion engines (ICEs) are one of the main sources of pollutants on urban corridors. One way to minimize tail-pipe emissions is to use electric motors in place of ICE. To evaluate the vehicle performance, energy consumption, driving behavior, optimal design and management of such electric vehicles, driving cycle is an important tool. So far, only limited studies exist on the development of a driving cycle for e-rickshaw. Moreover, these studies are concentrated in urban traffic environment and research accounting rural and urban environment together remain unexplored. In this study, real world driving data for 100 trips of e-rickshaw are collected on a road stretch passing through rural and urban setting. A high-end GPS data logger was used to collect vehicle kinematics such as continuous speed profile, acceleration/deceleration, heading, and vehicle position coordinates. Nine different driving characteristics representing actual traffic conditions are identified and used for developing e-rickshaw driving cycle (ERDC). Two approaches, random selection and k-means clustering are explored to arrive at best representative ERDC using micro-trips technique. The analysis results revealed that k-means clustering outperforms the random selection method with additional benefit of accounting traffic conditions systematically. The insights from this study can be used to understand and model the performance of e-rickshaw, in terms of energy consumption and driving characteristics, compared to other fossil-fuel driven automobiles.     

基于Prescan的自然驾驶场景构建与研究

文章主要描述基于Prescan软件对实车的自然驾驶场景片段转换成虚拟场景,用于ADAS或自动驾驶模拟测试。通过配备毫米波雷达、相机、Mobileye摄像头等传感器采集道路驾驶数据,提取场景片段,转化成虚拟软件中相应的实车场景,用于HIL或MIL测试,可实现重复性测试,降低测试成本,模拟恶劣天气等状况,复现实车测试状态等测试优点。     

基于自然驾驶数据的跨江大桥小客车跟驰特性研究

为明确跨江大桥的跟驰行为特征以及驾驶模式,在重庆菜园坝大桥展开了30位被试的小客车实车驾驶试验,使用华测航姿测量系统和前视碰撞预警系统Mobileye 630采集自然驾驶状态下汽车的连续行驶速度、车头时距和车头间距等数据。通过筛选得到了725条有效跟驰轨迹数据,对比分析发现跨江大桥与城市一般道路的跟驰行为存在一定差异性,明确了菜园坝大桥车头时距和车头间距的分布特征,并且对强跟驰(小于1.6 s)、过渡区间(1.6~2.6 s之间)以及弱跟驰(大于2.6 s)3种跟驰状态和驾驶人性别差异下的跟驰数据进行了分析。结果表明:桥梁段车头时距分布集中在1.6 s处,车头间距分布集中在18 m处;超过1/3的跟驰轨迹处于强跟驰状态,此状态下前车驾驶行为对跟驰车辆具有较强制约性;当车辆处于弱跟驰状态时,前车对于后车的约束性会随车头时距的增大而快速降低;过渡区间的设立更好地揭示了强/弱跟驰状态之间的转变并不是只有一个临界值,而是存在一个转换过程,并且其间车辆跟驰特性的变化与驾驶人本身的操作行为存在较大关联;驾驶人的性别差异对跟驰距离几乎没有影响,但男性驾驶人往往会采取更加冒险的驾驶行为,平均车头时距、车头间距以及相对速度均高于女性驾驶人。     

基于新能源高频大数据的驾驶行为与能耗分析

近年来,在新能源汽车示范推广和财政补贴的大背景下,我国新能源汽车产业快速发展。但与传统燃油车相比,新能源汽车的技术成熟度尚且不足,在研发、运行阶段仍存在诸多问题等待解决,其中能耗和续航问题的关注度尤为突出。本文基于车载终端采集到的新能源高频大数据,提取能够反映驾驶行为精细时空变化特征的特征参数集,采用主成分分析方法将特征参数集进行优化,利用K-means算法实现驾驶行为的自动分级,并分析了不同级别驾驶行为的能耗分布情况。分析结果表明,驾驶行为影响新能源汽车能耗水平,其中平稳驾驶对应的能耗较低,对新能源汽车产品升级和用户驾驶习惯优化具有一定的参考价值。     

老年驾驶人的驾驶自我调节研究述评

随着社会人口老龄化的发展,老年驾驶人的占比逐年增加,提升老年人的驾驶安全性对于其安全自主出行和公共交通安全均具有重要意义。驾驶自我调节是老年驾驶人为适应身体、认知功能变化而对驾驶行为做出的主动调整,是其提升驾驶安全性、延长驾驶生命和维持自主行动能力的有效补偿策略。通过对已有关于老年驾驶人的驾驶自我调节研究进行系统回顾,介绍了老年驾驶人的驾驶自我调节行为的定义及其表现,归纳分析了其驾驶自我调节行为的影响因素及产生机制,在此基础上总结了现有研究的局限,并指出了未来进一步研究的主要思路和方向。对文献的梳理和分析表明：老年驾驶人的驾驶自我调节包括减少驾驶频率和回避具有挑战性的驾驶情境2种主要形式,并可分为策略性、战术性和生活目标性自我调节3种不同的层次水平;驾驶自我调节是一个复杂的过程,社会人口因素、生理健康和功能状况、心理因素等均可对其产生影响;驾驶自我调节的产生机制可以被概括为是个体从认知到态度改变,再到形成调节行为意向,直至最终发生驾驶行为改变的动态决策过程。未来对老年驾驶人的驾驶自我调节行为研究应更进一步将客观驾驶行为数据、医疗机构数据与驾驶人主观自我报告相结合,适当开展追踪研究,考察驾驶自我调节行为随年龄的发展变化趋势,深入探究驾驶自我调节的产生机制及其在降低事故发生和提升驾驶安全性方面的作用。     

网联环境下基于精简车头时距特性的驾驶风格分类

基于现有网联数据获取技术与条件，从车联网系统提取车头时距参数并将3 s内的车头时距特征值定义为驾驶模式，根据驾驶模式进而对驾驶风格（即驾驶人的驾驶行为习惯）进行分类。通过车头时距特性对驾驶模式进行量化分类，根据标定好的驾驶风格结果，辨识每种驾驶风格包含的典型驾驶模式；运用模糊分类方法赋予典型驾驶模式相应分值，通过计算每位驾驶人分值并结合已标定的驾驶风格结果设定每种驾驶风格的阈值；利用该阈值对测试集中的驾驶人风格进行识别，以验证识别准确率。采集了44名驾驶人网联环境行车数据将驾驶人标定为激进型、普通（即既不保守也不激进）型和保守型。按上述方法设置各驾驶风格阈值，结果表明:各驾驶风格的阈值分别为:S < 64.67为保守型，64.67 ≤ S < 181.20为普通型，S ≥ 181.20为激进型；使用所提方法来识别驾驶人风格，总体准确率为85.7%。所提出的基于车头时距的驾驶风格分类方法，使用了极精简的驾驶行为参数，为驾驶风格分类应用提供了新思路。