Self- and peer-assessments of ambulance drivers' driving performance

The aim of the present study was to develop and examine the quality of the Ambulance Driver Self-assessment Questionnaire (ADSQ) and the Ambulance Driver Peer-assessment Questionnaire (ADPQ) measuring aspects of, driving performance, driving style and driving competence. In addition the ADSQ measures self-reflection and safety-attitudes. The aim of the study was also to examine ambulance drivers' self- and peer-assessments as well as to examine the accuracy of self-assessments by comparing self-assessed and peer-assessed driving performance, driving style and competence. 76 ambulance drivers employed at two ambulance stations in northern Sweden completed ADSQ and ADPQ. Item analyses were conducted to examine the psychometric properties of the items, and based on the results some revisions were made to improve the questionnaires. The revised questionnaires were functioning rather well, although some subscale demonstrated low internal consistency. Subscale inter-correlations provided support for construct validity. Self- and peer-assessments indicated safe driving performance and good driver competence, which is positive from a traffic safety perspective. A comparison of mean self- and peer-assessment ratings, controlling for age, gender and driving experience showed no significant differences, except for the subscale overtaking. This indicates that ambulance drivers' self-assessments are realistic in most areas.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

基于SOM神经网络的多工况驾驶风格识别

不同的驾驶员对车辆的各项性能可能有个性化地要求,因此有必要对驾驶风格的分类与识别问题进行研究.首先在驾驶模拟器上采集不同驾驶员在多工况下的数据,利用主成分分析法选取驾驶员在各个工况下的特征参数,SOM神经网络分别对起步、加速及制动工况下的驾驶数据进行了聚类分析,然后以驾驶风格聚类分析结果为基础,建立了基于SOM神经网络...     

Application of a driving simulator to the development of in-vehicle human-machine-interfaces

The use of a driving simulator in the development of human-machine-interfaces (HMI) such as a navigation, information or entertainment system is discussed. Such use addresses the need to study and evaluate the characteristics of a candidate HMI early in the R&D and design stage to ensure that it is likely to meet various objectives and requirements, and to revise the HMI as may be necessary. Those HMI requirements include such things as usability, driver comfort, and an acceptable level of attentional demand in dual task conditions (driving while using an HMI). Typically, such an HMI involves an information display to the driver, and a means for driver input to the HMI. Corresponding simulator requirements are discussed, along with typical simulator features and components. The latter include a cab, control feel systems, visual image generator, real time scenario control (task definitions), a motion system (if provided), and data acquisition. Both fixed and moving base systems are described, together with associated benefits and tradeoffs. Considerations in the design of the evaluation experiment are discussed, including definition of primary and secondary tasks, and number of driver subjects (experimental participants). Possible response and performance measures for the primary and secondary tasks are noted, together with subjective measures such as task difficulty and ease of using the HMI. The advantages of using a driving simulator to support R&D are summarized. Some typical and example simulator uses are noted.     

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.     

实现汽车夜间行驶会车远光自动变近光系统的探讨

本文探讨了汽车夜间行驶中对向会车时,大灯远光产生的灯光对驾驶员视线的影响,并且基于该问题研究了大灯远近光自动切换的方案。     

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.     

Risk assessment based on driving behavior for preventing collisions with pedestrians when making across-traffic turns at intersections

Traffic accident statistics in Japan show the necessity of preventing vehicle-on-pedestrian accidents. If the risk of a vehicle colliding with pedestrians could be evaluated in advance, driver-assistance systems would be able to support drivers to avoid potential collisions. Here, features of driving behavior and methods for assessing the risk of collision were investigated for a right turn at an intersection in left-hand traffic, which is a typical vehicle-on-pedestrian accident scenario. The results showed that pedestrian-collision risk can be evaluated from how the driver slows the vehicle and where the driver looks while turning during the maneuver. Moreover, pedestrian-collision risk could be predicted based on driving behavior upon commencement of steering when making an across-traffic turn.     

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。     

Study on behavior of cab-over truck driver looking at a display while driving

This paper discusses how a cab-over truck driver looks at information shown in a display while driving, to give information to the driver safely and securely. The authors investigated a driver's behavior in changing focus to an information display while driving an actual truck and a driving simulator. Relation between distribution of attention and individual lengths of time to change focus to the display was examined. Average diversion time away from road was found to be approximately 1.7 s, longer than that for passenger cars under similar traffic conditions. Driving skills do not have any influence on individual lengths of time.     

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.     

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.     

驾驶分心检测方法综述

道路交通事故已经成为世界人民非自然死亡的主要原因,有学者认为90％的交通事故与驾驶员自身因素关联,而其中又有超过50％与驾驶分心相关.为了对驾驶员分心进行识别以及为下一步的分心预警干预提供结果判断,文章介绍了常见的驾驶分心检测与识别方法,包括人脸识别方法,眼睛瞳孔位置识别以及基于驾驶员生理信号(EEG)的分心监测方法....     

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。     

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.     

节能减排汽车驾驶技术浅析

驾驶员的驾驶技术和责任心会直接影响最终的油耗,驾驶节油的关键在于看驾驶人员是否按照车辆运行要求,采用相应的驾驶操作,使人车之间合理搭配保持车辆最佳运行状态,驾驶员的驾驶技术和驾驶习惯会对节油产生非常大的影响,因此,在日常行车过程中,驾驶员需要逐渐提升自身的驾驶水平,时刻牢记驾车注意事项,这样才能达到最终的节约目标.     

汽车驾驶人机系统目标评价树的建立及应用

以汽车安全行驶概率为评价目标,列举与之相关的驾驶员、汽车、环境3方面因素,分析因素间关系,确定重要因素并赋予权重,建立汽车驾驶人机系统目标评价树。介绍了该目标评价树的使用方法与适用范围。对哈尔滨市两个事故多发地段的驾驶环境进行评价和改进,取得预期效果。     

HRV分析在驾驶疲劳中的应用研究

驾驶员在驾驶过程中易因疲劳、烦躁和压力等不良生理反应引发事故,而心率变异性(HRV)作为评价人体自主神经系统活性,反映人体生理状态的重要指标,能够准确评估驾驶员的精神状态。为了有效预防事故发生,文章介绍了驾驶员在驾车过程中HRV指标的变化及其应用。结果表明HRV指标结合相应算法模型对驾驶员生理状态识别的准确率高达90%以上,能够有效降低事故发生率。     

In-vehicle display HMI safety evaluation using a driving simulator

Recently, telematics services and in-vehicle display devices such as the CNS (Car Navigation System) have become new causes of traffic accidents. These accidents are caused by ‘Inattention’ from the increase of the driver’s mental workload while he/she is driving. The driver of a vehicle (except for emergency or police vehicles) must not use a hand-held mobile phone while the vehicle is moving. To address this problem, Australia, England, Italy, Brazil and some states in the US have banned the use of hand-held mobile devices during driving. However, there are no restrictions on the use of in-vehicle displays or on the display’s positions. The position of a navigation system in a vehicle should be assessed objectively, and the effect of the position on the driver’s attention should be studied. Some existing research reports that in-vehicle distraction not only leads to reduced speeds and more frequent lane switching, but also more gazing by the driver to the centre of the road. In this study, to develop an assessment method and to propose the proper position of a CNS, an experiment is carried out in a driving simulator environment. Different methods to track the gaze and physical parameters of the driver are used for HMI (Human-Machine Interface) assessment. The experiment is carried out in a driving simulator to observe the glancing distribution during driving according to the position of the navigation system. Fourteen subjects participated in this experiment. Changes in subjects’ physiological signals and glancing distribution rates were collected.