交叉口黄灯期间驾驶员行为的模糊决策树模型

采集黄灯期间驾驶员行为的相关数据,考虑车辆位置、车速、倒计时表3个影响因素,分别设定其隶属度函数,应用模糊决策树中的FID3算法,以模糊信息熵为启发信息,构建驾驶员选择的模糊决策树模型,生成决策规则。利用测试样本对模型进行检验,结果表明,基于模糊决策树的预测结果准确率总体达到84.8%。     

Predictive car-following scheme for improving traffic flows on urban road networks

Driving behavior is one of the main reasons that causes bottleneck on the freeway or restricts the capacity of signalized intersections. This paper proposes a car-following scheme in a model predictive control (MPC) framework to improve the traffic flow behavior, particularly in stopping and speeding up of individual vehicles in dense urban traffic under a connected vehicle (CV) environment. Using information received through vehicle-to-vehicle (V2V) communication, the scheme predicts the future states of the preceding vehicle and computes the control input by solving a constrained optimization problem considering a finite future horizon. The objective function is to minimize the weighted costs due to speed deviation, control input, and unsafe gaps. The scheme shares the planned driving information with the following vehicles so that they can make better cooperative driving decision. The proposed car-following scheme is simulated in a typical driving scenario with multiple vehicles in dense traffic that has to stop at red signals in multiple intersections. The speeding up or queue clearing and stopping characteristics of the traffic using the proposed scheme is compared with the existing car-following scheme through numerical simulation.     

Approaching and stopping behaviors to the intersections of aged drivers compared with young drivers

According to the many reports of the traffic accidents, the number of the accidents near the intersections was increased in the cases of the aged drivers. The purpose of the research was to measure the sensitivity of elder drivers through behavioral responses in approaching and stopping to the intersections and to obtain the difference of the responses for braking and stopping comparing with young drivers. In the field tests of real running on experimental proving ground, elder and young drivers were observed their driving behaviors in various conditions of approaching and stopping at intersections. Comparing the results of the elder with the young, the unstable driving behaviors were examined in elderly and they were apt to run fast in approaching the intersection and also stopping rapidly. The rates of deceleration change (Jerk) with braking operation were unstable and the driving behaviors were affected by the environments or conditions of the intersection. These driving performances should be interfered with the traffic flow and exposed to the risk of accidents. If the features of these aged driver's behaviors were clarified better, the more effective driving assistant systems for elderly could be developed based on characteristics of elderly driver's driving performances and their physical and psychological features of driving.     

考虑直线式公交停靠站影响的交叉口通行能力计算模型*

为了更为准确计算信号控制交叉口通行能力,在HCM2010方法的整体框架下,针对交叉口上游直线式公交停靠站对通行能力的影响进行了研究。运用交通流理论,分析了在不同到达时刻条件下,单辆公交车停靠对所在车道绿灯损失时间的影响。在此基础上,运用概率论,分别建立了公交停靠站所在车道为直右共用车道、右转专用车道和右转与公交共用车道三种情况的交叉口通行能力计算模型,并对影响因素进行了敏感性分析。研究结果表明,公交停靠站与交叉口的间距以及公交停靠站所在车道的车辆到达率对通行能力起主要影响,实践中应保证公交停靠站与交叉口的间距能满足该车道红灯期间车辆排队长度要求。     

Older driver failures of attention at intersections: using change blindness methods to assess turn decision accuracy

A modified version of the flicker technique to induce change blindness was used to examine the effects of time constraints on decision-making accuracy at intersections on a total of 62 young (18-25 years), middle-aged (26-64 years), young-old (65-73 years), and old-old (74+ years) drivers. Thirty-six intersection photographs were manipulated so that one object (i.e., pedestrian, vehicle, sign, or traffic control device) in the scene would change when the images were alternated for either 5 or 8 s using the modified flicker method. Young and middle-aged drivers made significantly more correct decisions than did young-old and old-old drivers. Logistic regression analysis of the data indicated that age and/or time were significant predictors of decision performance in 14 of the 36 intersections. Actual or potential applications of this research include driving assessment and crash investigation.     

Do in-vehicle advanced signs enhance older and younger drivers’ intersection performance? Driving simulation and eye movement results

An experimental study was conducted to determine if intersection behaviour benefited from advanced in-vehicle signs presented to older and younger drivers in a head-up display (HUD) format. The University of Calgary Driving Simulator (UCDS) was used to evaluate intersection performance. Measures of those who were able to stop or ran the yellow light, speed over the span of the intersection, perception response time, and eye movements were analyzed to determine if performance improved or whether undesirable adaptive behaviours occurred. In-vehicle signs facilitated an increase in the frequencies of stopping for both younger and older drivers at intersections with relatively short yellow onsets. The speed at the yellow light onset for both those who stopped and those who proceeded through the intersection was reduced by the presence of the in-vehicle signs. The primary behavioral influence of the in-vehicle signs was to cause the drivers’ to reduce their velocity in advance of an intersection. Eye movement analyses indicated that younger drivers looked at the in-vehicles signs more often and for longer overall durations than older drivers. Older drivers had slower intersection approach speeds, stopped more accurately, and were more likely to not clear the intersection before the traffic light turned to an all-red phase than younger drivers. The implications of the in-vehicle sign results are discussed in terms of in-vehicle information systems (IVIS) design guidelines and evaluation methods.     

The role of working memory, field dependence, visual search, and reaction time in the left turn performance of older female drivers

Older drivers have difficulties at intersections, especially in their performance of left turns. Older females are especially at risk in intersection maneuvers. Information processing abilities have been most promising in explaining the crash involvement of elderly drivers. This study examines the relationship of field dependence, visual search skills, and working memory to the decision to make a left turn at an intersection as well as to gap choice. Participants included thirty-three women ranging in age from 61 to 84 years. The results show that working memory plays a very important role in left-turn performance. Implications for appropriate interventions are discussed.     

自动驾驶车辆在无信号交叉口右转驾驶决策技术研究

利用深度强化学习（deep reinforcement learning,DRL）技术实现自动驾驶决策已成为国内外研究热点,现有研究中的车辆交通流缺乏随机性与真实性,同时自动驾驶车辆在环境中的有效探索具有局限性。因此利用TD3算法进行自动驾驶车辆在无信号交叉口下的右转驾驶决策研究,首先在Carla仿真平台中开发无信号交叉口的训练与测试场景,并添加交通流管理功能,提高系统训练和测试随机性。其次,为了提高自动驾驶车辆的探索性,对TD3算法中的Actor网络进行改进,为目标动作添加OU噪声。最后使用通行成功率和平均通行时间评估指标评价自动驾驶行为决策。结果表明,在不同交通流场景下,改进后的TD3算法通行成功率与基于DDPG算法控制的车辆相比平均提升6.2%,与基于规则的AEB模型相比平均提升23%。改进后的TD3算法不仅能够探索更多可能,而且其通行决策表现更加突出。     

DRIVER JUDGMENT AND ERROR DURING THE AMBER PERIOD AT TRAFFIC LIGHTS With an Appendix on the Calculation of Thresholds and Subjective Scales

This paper describes tho behaviour of a small group of drivers who took part in an experiment on stopping at traffic lights. The data of their judgments and subsequent performance aro given and compared. Generally, the judgments of when to try to stop whon the amber signal appeared wore found to be consistent and fairly accurate. However, tho judgment thresholds at each speed—the distances from the traffic lights whon thoy showed amber at which drivers decided to stop on 50 per cent of the occasions—were found to require almost the peak vehicle braking performance, and a third of these decisions to stop lod to errors, hesitations or changes of mind. Equations havo been derived from tho data which link tho proportion of occasions the drivers decided to stop, tho proportion of successful stops, tho overall stopping distance, the initial speod, the drivers' rosponso times, and the average decelerations during the hraking period.

Examination of tho errors, hesitations anil changes of mind suggost that the distances in which 95 per cent of the drivers stopped successfully might bo used as design parameter in the calculations of tho minimum amber poriod for a traffic light.

In the Appendix tho theory of the subjective scale of difficulty ia devoloped as it applies to this experiment.     

A cell-based robust optimal coordinated control on urban arterial road

Process of optimizing coordinated control is divided into two stages.At the first stage,the study improves a robust optimal control model of single-point intersection to optimize cycle and split.At the second stage,the study combines all links with intersections of arterial road as a complete system,and applies cell transmission model to simulate traffic flow on urban signalized arterial road.We propose a coordinated control model based on the platform to optimize offset between adjacent intersections.Genetic algorithm is executed by MATLAB to solve the model.The performance evaluations show that the model not only effectively reduces average delay and stopping rate of vehicles on arterial road and largely enhances traffic capacity of arterial road,but also lowers the sensitivity of signal control for flow fluctuations.     

Using data mining techniques for optimizing traffic signal plans at an urban intersection

A key problem in traffic engineering is the optimization of the flow of vehicles through urban intersections by improving the timing policy of traffic signals. Current methods of signal control policy are based on the junction topography and prespecified static traffic volumes. However, the actual daily traffic volumes can be affected by many time‐dependent factors making a static policy hardly optimal. In this paper, we induce nonstationary predictive models of traffic flow by applying novel methods of time‐series data mining to the traffic sensors data collected from a signalized intersection in Jerusalem over a period of 3 years. Our methodology for modeling dynamic traffic volumes combines clustering and segmentation algorithms. The results of a case study based on real‐world traffic data demonstrate that a dynamic signal policy using the data mining approach can produce a decrease of about 33.7% in the total waiting time of drivers during 1 year, in comparison to the existing static traffic policy. The resulting savings for this junction only would be about 13,800 driving hours, which are worth of about $52,000 per annum in terms of Israeli economy. © 2011 Wiley Periodicals, Inc.     

A cognitive engineering approach to improving signalized left turn intersections.

This research evaluated the effect of providing advanced left turn information to individuals faced with deciding whether or not it is safe to turn at a signalized intersection. Younger (mean age 37 years) and older (mean age 71 years) drivers were tested in simulations of approaching an intersection with and without advanced cueing. Experiment 1 used a featureless background; Experiment 2 used an animated presentation of traffic control displays. In both experiments the subjects had to determine whether or not they had right-of-way to make a left turn. Exaggerated error rates and increased decision latencies for displays conveying unprotected turn status occurred in both studies. Cueing drivers with advanced notice of the decision rule through a redundant upstream posting of sign elements improved both accuracy and latency of younger and older drivers' decisions.     

Night-time driving: the use of seat-belts and alcohol

Abstract

The objective risks of accident involvement associated with night driving, seat-belt use and alcohol intoxication have been reported from many countries. However, much less is known about the risks associated with combinations of these activities, even though these are common. A brief review of the relevant literature suggests that young drivers and drinking drivers are less likely to wear seat-belts at night. The results of a night-time survey in the Netherlands support this finding, with the lowest rate of belt use, 21%, being for young drivers with blood alcohol levels in excess of the legal limit (0·50%). Thus those drivers who are most likely to become involved in a crash are least likely to protect themselves from injury. The implications of these findings for accident countermeasures are briefly outlined.     

How do environmental characteristics at intersections change in their relevance for drivers before entering an intersection: analysis of drivers’ gaze and driving behavior in a driving simulator study

Accident studies in Germany found that in about 90 % of intersection accidents, failure to acquire the relevant information of the driving situation was the main reason for drivers’ errors (Vollrath et al. in Ableitung von Anforderungen an Fahrerassistenzsysteme aus Sicht der Verkehrssicherheit. Wirtschaftsverlag NW, Bremerhaven, 2006). Studies of bicycle–car accidents assume that improper attention allocation strategies and unjustified expectations by drivers are important for this kind of error (Räsänen and Summala in Accid Anal Prev 30:657–666, 1998). Aim of the study was to examine the psychological processes of drivers’ attention allocation and driving behavior in different intersection situations varied by two environmental characteristics. A give way T-intersection was varied by (1) low and high traffic density of oncoming cars from the left and (2) number of task-relevant information areas (in addition to the oncoming cars from the left with or without pedestrians on the right). It was examined how these environmental characteristics change in their relevance for drivers while entering the intersections. The analysis was conducted in three intersection epochs (Approaching, Waiting, Accelerating). A total of 40 subjects (26 male, 14 female), ranged in age from 19 to 55 years (M = 31.0 years), participated in the study. The results showed that drivers’ attention allocation (e.g., mean gaze duration) and driving behavior (e.g., waiting time) systematically depends on these environmental characteristics which require different actions of the driver and change in their relevance when entering an intersection. The results support the idea of attention allocation strategies by drivers which are specific for certain driving situations. These findings can support approaches of driver modeling at intersections.     

A method for predicting the risk of virtual crashes in a simulated driving task using behavioural and subjective drowsiness measures

Abstract

This study proposed a procedure for predicting the point in time with high risk of virtual crash using a control chart methodology for behavioural measures during a simulated driving task. Tracking error, human back pressure, sitting pressure and horizontal and vertical neck bending angles were measured during the simulated driving task. The time with a high risk of a virtual crash occurred in 9 out of 10 participants. The time interval between the successfully detected point in time with high risk of virtual crash and the point in time of virtual crash ranged from 80 to 324 s. The proposed procedure for predicting the point in time with a high risk of a crash is promising for warning drivers of the state of high risk of crash.

Practitioner Summary: Many fatal crashes occur due to drowsy driving. We proposed a method to predict the point in time with high risk of virtual crash before such a virtual crash occurs. This is done using behavioural measures during a simulated driving task. The effectiveness of the method is also demonstrated.     

Mental load and risk in traffic behaviour

Abstract

Car driving means accomplishing a variety of continuously varying driving subtasks which constitute workload on the driver. Total workload can be analysed by type; for instance, the amount of information to be processed while driving, or the effort of car control. One of the driver's main tasks is to cope with the hazards with which he can be confronted on each particular route. This task places demands on his mental capacities. Therefore, coping with hazards is part of the total workload of car drivers. Based on this premise, the following topics are discussed in this paper.

(1)The mental-load approach in modelling traffic behaviour is described in some detail.

(2)Procedures and results of investigations into load factors in car driving are presented, emphasizing workload by reference to hazards. These include a job-analytic study of driving behaviour, a simulation study of hazard perception, and a field study of drivers' exposure to different road conditions.

(3)Hazards and risks must be perceived before making adequate decisions. Some aspects of hazard perception are therefore mentioned.

(4)To understand ‘risky decision making’ in car driving it is necessary to consider the opportunities drivers have to engage in risky situations. Those opportunities are discussed.

(5)To complement these discussions of traffic behaviour, certain issues of risk-taking behaviour in non-traffic situations are considered in order to assess whether knowledge from these areas can be applied to driving.     

Development and application of an integrated traffic simulation and multi-driving simulators

Professional virtual reality experiment tools, including driving simulators and traffic simulators, have their strengths and weaknesses. The integration of the two simulators will enhance the ability of both traffic modeling and driving simulation and present a new area of applications. This paper develops, implements, and validates an experimental platform that integrated a traffic simulator with multiple driving simulators (TSMDS). As a connected multi-user framework that allows multiple drivers who are simultaneously handling many driving simulators, it not only allows driver behavior experiments to be more accurate, controlled, and versatile but also simulates special driving behavior or multi-vehicle interactions under more realistic traffic flow environments. To validate the performance of TSMDS, 27 drivers were recruited to attend the lane changing experiments at a recurring on-ramp bottleneck and left-turn experiments at a two-phase signalized intersection in Shanghai. Both experiments required several drivers to drive the TSMDS and fulfill several complicated lane changing/crossing behaviors through their interaction. The results show that both the participants’ response and lane changing/crossing data that were obtained from the experiment are consistent with the field observation, which confirms the validity of the integrated platform.     

Development of a fuzzy logic traffic system for isolated signalized intersections in the State of Kuwait

In a conventional traffic signals controller, the lights change at constant cycle time. In many cities, automatic traffic signals are often based on a constant green-to-red cycle. The time period for green light (or red light) to be on is determined based on a stochastic model. The traditional vehicle-actuated control of isolated intersections attempts continuously to adjust green times. The decision to change green light duration involves fuzzy factors that cannot be precisely determined.The main objective of this paper is to develop a fuzzy logic traffic system that considers the two two-way intersections and is able to adjust changes in time intervals of a traffic signal based on traffic situation level.The proposed system has been applied and tested using real data collected from signalized intersection in Hawalli governorate in the State of Kuwait. Twenty-seven iterations have been done; the results show that the proposed fuzzy logic traffic system provides better performance in terms of total waiting time, total moving time, and vehicle queue. Finally, it can be observed from the results that the proposed system can be used to accelerate the cycle time and to give other phases the chance to gain more benefit from the green time lost.     

Analysis and Classification of Human Driving Behaviour in an Urban Environment*

Intelligent vehicle systems have introduced the need for designers to consider user preferences so as to make several kinds of driving features as driver friendly as possible. This requirement raises the problem of how to suitably analyse human performance so they can be implemented in automatic driving tasks. The framework of the present work is an adaptive cruise control with stop and go features for use in an urban setting. In such a context, one of the main requirements is to be able to tune the control strategy to the driver’s style. In order to do this, a number of different drivers were studied through the statistical analysis of their behaviour while driving. The aim of this analysis is to decide whether it is possible to determine a driver’s behaviour, what signals are suitable for this task and which parameters can be used to describe a driver’s style. An assignment procedure is then introduced in order to classify a driver’s behaviour within the stop and go task being considered. Finally, the findings were analysed subjectively and compared with a statistically objective one.     

Application of fuzzy set theory on the change intervals at a signalized intersection

The main objective of this paper is to develop a new procedure used to calculate the change and clearance intervals of a traffic signal from a rule-based fuzzy logic system. This procedure is based on the theory that a driver’s decision making at the signalized intersections is based on imprecise or fuzzy information. The procedure requires no analytical or mathematical modeling of the system’s phenomena and provides the flexibility for modification to improve the performance of the system.