Effects on driving behavior of congestion information and of scale of in-vehicle navigation systems

The purpose of this research was to compare the usage of a traditional paper map and electronic route maps during driving, and to consider the effects of congestion information and map scale sizes on driving performance, workload and subjective feelings. Experiments were conducted in desktop virtual driving environments with a 17-in. color monitor simulating driving environments and a 14-in. color monitor showing different kinds of navigation systems. A total of 20 undergraduate students of National Tsing Hua University were paid to participate in the Experiment I. The criteria for driving performance were trip duration, driving speed and number of navigation errors. Heart rate was measured as an index for workload. The 5-point Likert-type questionnaire was used to reflect the perceived nervousness, fatigue and task difficulty. Results indicated that the performance difference between a paper map and electronic route maps depended on the design characteristics of electronic route maps. Comparisons among four electronic maps revealed two significant main effects of congestion information and scale sizes on trip duration. Besides, it seemed that the availability of congestion information was useful for reducing navigation errors. Neither statistically significant main effects nor interaction was found on subjective feelings and driving speed. Another 18 subjects were used in the Experiment II to determine the optimum map scale size. Finally, the implications of the findings may provide suggestions on designing safer and more efficient in-vehicle navigation systems.     

A robust,data-driven methodology for real-world driving cycle development

This paper develops a robust, data-driven Markov Chain method to capture real-world behaviour in a driving cycle without deconstructing the raw velocity–time sequence. The accuracy of the driving cycles developed using this method was assessed on nine metrics as a function of the number of velocity states, driving cycle length and number of Markov repetitions. The road grade was introduced using vehicle specific power and a velocity penalty. The method was demonstrated on a corpus of 1180 km from a trial of electric scooters. The accuracies of the candidate driving cycles depended most strongly on the number of Markov repetitions. The best driving cycle used 135 velocity modes, was 500 s and captured the corpus behaviour to within 5% after 1,000,000 Markov repetitions. In general, the best driving cycle reproduced the corpus behaviour better when road grade was included.     

Fit-for-duty test for estimation of drivers’ sleepiness level: Eye movements improve the sleep/wake predictor

Driver sleepiness contributes to a considerable proportion of road accidents, and a fit-for-duty test able to measure a driver’s sleepiness level might improve traffic safety. The aim of this study was to develop a fit-for-duty test based on eye movement measurements and on the sleep/wake predictor model (SWP, which predicts the sleepiness level) and evaluate the ability to predict severe sleepiness during real road driving. Twenty-four drivers participated in an experimental study which took place partly in the laboratory, where the fit-for-duty data were acquired, and partly on the road, where the drivers sleepiness was assessed. A series of four measurements were conducted over a 24-h period during different stages of sleepiness. Two separate analyses were performed; a variance analysis and a feature selection followed by classification analysis. In the first analysis it was found that the SWP and several eye movement features involving anti-saccades, pro-saccades, smooth pursuit, pupillometry and fixation stability varied significantly with different stages of sleep deprivation. In the second analysis, a feature set was determined based on floating forward selection. The correlation coefficient between a linear combination of the acquired features and subjective sleepiness (Karolinska sleepiness scale, KSS) was found to be R = 0.73 and the correct classification rate of drivers who reached high levels of sleepiness (KSS ⩾ 8) in the subsequent driving session was 82.4% (sensitivity = 80.0%, specificity = 84.2% and AUC = 0.86). Future improvements of a fit-for-duty test should focus on how to account for individual differences and situational/contextual factors in the test, and whether it is possible to maintain high sensitive/specificity with a shorter test that can be used in a real-life environment, e.g. on professional drivers.     

A real-time system for monitoring driver fatigue

ABSTRACT

This paper presents a nonintrusive prototype computer vision system for real-time fatigue driving detection. First, we use Haar-like features to detect a driver’s face and conduct tracking by introducing an improved Camshift algorithm. Second, we propose a new eye-detection algorithm that combines the Adaboost algorithm with template matching to reduce computational costs and add an eye-validation process to increase the accuracy of the detection rate. Third, and different from other methods focusing on detecting eyes using the ‘bright pupil’ effect, which only works well only for certain constrained lighting conditions, our method detects and estimates the iris center in the hue (H) channel of the hue, saturation, value color space and fits the iris with an ellipse. After extracting the eye fatigue features, we calculate the PERCLOS measurement for fatigue evaluation. This system has been tested on the IMM Face Database, which contains more than 200 faces, and in a real-time test. The experimental results show that the system possesses good accuracy and robustness.     

Importance of scenic byways in route choice: a survey of driving tourists in the United States

In order to develop more effective tourist information systems for use along scenic byways, it is important to know the characteristics of those people who include the presence of scenic byways in their selections of routes. Gaining a better understanding of these characteristics was the purpose of this study. The data presented here are from a United States (US) survey of the driving tourist's information needs and preferences weighted to be representative of US tourist travelers. The study showed that when planning a route to a destination on an overnight automobile trip, the driving tourist is most concerned with factors related to the actual driving of the route, such as the directness, safety, amount of congestion, and distance. Of secondary importance are factors that make the route entertaining or pleasant to drive, including whether the route is a scenic byway. Analysis of the importance of scenic byways by several demographic factors showed little difference in importance ratings except for age and household income. Examination of importance ratings by trip characteristics showed that the presence of scenic byways in selecting a route was more important for the traveler whose trip purpose is a vacation, who is in the midst of a long distance and duration trip, who will be either camping or staying in a hotel, and who has planned the trip well in advance. These results suggest that scenic byways are an excellent area for the implementation and testing of in-vehicle information systems for the driving tourist.     

驾驶疲劳监测方法综述

随着国民经济的发展,汽车持有量的增加,交通事故也成为交通事业发展的一个严峻挑战。针对这些情况,车辆驾驶安全技术的研究取得了很大发展。文章从驾驶疲劳的研究现状、驾驶疲劳研究存在的问题等方面对驾驶疲劳监测方法进行了综述性研究,着重对基于机器视觉的驾驶疲劳监测方法进行了研究,为驾驶疲劳的检测技术的开展提供了参考。     

Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users

Wider deployment of alternative fuel vehicles (AFVs) can help with increasing energy security and transitioning to clean vehicles. Ideally, adopters of AFVs are able to maintain the same level of mobility as users of conventional vehicles while reducing energy use and emissions. Greater knowledge of AFV benefits can support consumers’ vehicle purchase and use choices. The Environmental Protection Agency’s fuel economy ratings are a key source of potential benefits of using AFVs. However, the ratings are based on pre-designed and fixed driving cycles applied in laboratory conditions, neglecting the attributes of drivers and vehicle types. While the EPA ratings using pre-designed and fixed driving cycles may be unbiased they are not necessarily precise, owning to large variations in real-life driving. Thus, to better predict fuel economy for individual consumers targeting specific types of vehicles, it is important to find driving cycles that can better represent consumers’ real-world driving practices instead of using pre-designed standard driving cycles. This paper presents a methodology for customizing driving cycles to provide convincing fuel economy predictions that are based on drivers’ characteristics and contemporary real-world driving, along with validation efforts. The methodology takes into account current micro-driving practices in terms of maintaining speed, acceleration, braking, idling, etc., on trips. Specifically, using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54 million seconds of vehicle trajectories on more than 60,000 trips, made by 3000 drivers. To generate customized driving cycles, a new tool, known as Case Based System for Driving Cycle Design, is developed. These customized cycles can predict fuel economy more precisely for conventional vehicles vis-à-vis AFVs. This is based on a consumer’s similarity in terms of their own and geographical characteristics, with a sample of micro-trips from the case library. The AFV driving cycles, created from real-world driving data, show significant differences from conventional driving cycles currently in use. This further highlights the need to enhance current fuel economy estimations by using customized driving cycles, helping consumers make more informed vehicle purchase and use decisions.     

Will a driving restriction policy reduce car trips?—The case study of Beijing,China

A driving restriction policy, as one of the control-and-command rationing measures, is a politically acceptable policy tool to address traffic congestion and air pollution in some countries and cities in the world. Beijing is the first city in China to implement this policy. A one-day-a-week driving restriction scheme was expected to take 20% of cars off the road every week day. Using household survey and travel diary data, we analyze the short-term effect of this driving restriction policy on individual travel mode choice. The data also allow us to identify which demographic groups are more likely to break the restriction rule. The estimates reveal that the restriction policy in Beijing does not have significant influence on individuals’ decisions to drive, as compared with the policy’s influence on public transit. The rule-breaking behavior is constant and pervasive. We found that 47.8% of the regulated car owners didn’t follow the restriction rules, and drove “illegally” to their destination places. On average, car owners who traveled during peak hours and/or for work trips, and whose destinations were farther away from the city center or subway stations, were more likely to break the driving restriction rules. Therefore, Beijing is probably in need of more comprehensive and palatable policy instruments (e.g., a combination of congestion tolls, parking fees, fuel taxes, and high-speed transit facilities) to effectively alleviate traffic congestion and air pollution.     

Communication with non-drivers for promoting long-term pro-environmental travel behaviour

The decision whether to obtain a driving license has a substantial effect on a person’s travel behaviour for the rest of his/her life and on lifetime CO₂ emissions. In heavily motorized societies, non-drivers often decide to obtain a driving license simply because others one, and with little unawareness of the negative aspects of automobile use. It is hypothesized that providing non-drivers with better information would influence their choice on whether to obtain a license. To obtain objective information, a field experiment was conducted followed by a survey. When information about the risks, costs, and enjoyment of automobile use was made available, it affected a person’s attitude toward life using an automobile. This information also influenced whether respondents possessed a driving license 18 months after the experiment.     

Understanding the impacts of mobile phone distraction on driving performance: A systematic review

The use of mobile phones while driving—one of the most common driver distractions—has been a significant research interest during the most recent decade. While there has been a considerable amount research and excellent reviews on how mobile phone distractions influence various aspects of driving performance, the mechanisms by which the interactions with mobile phone affect driver performance is relatively unexamined. As such, the aim of this study is to examine the mechanisms involved with mobile phone distractions such as conversing, texting, and reading and the driving task, and subsequent outcomes. A novel human-machine framework is proposed to isolate the components and various interactions associated with mobile phone distracted driving. The proposed framework specifies the impacts of mobile phone distraction as an inter-related system of outcomes such as speed selection, lane deviations and crashes; human-car controls such as steering control and brake pedal use and human-environment interactions such as visual scanning and navigation. Eleven literature-review/meta-analyses papers and 62 recent research articles from 2005 to 2015 are critically reviewed and synthesised following a systematic classification scheme derived from the human-machine system framework. The analysis shows that while many studies have attempted to measure system outcomes or driving performance, research on how drivers interactively manage in-vehicle secondary tasks and adapt their driving behaviour while distracted is scant. A systematic approach may bolster efforts to examine comprehensively the performance of distracted drivers and their impact over the transportation system by considering all system components and interactions of drivers with mobile phones and vehicles. The proposed human-machine framework not only contributes to the literature on mobile phone distraction and safety, but also assists in identifying the research needs and promising strategies for mitigating mobile phone-related safety issues. Technology based countermeasures that can provide real-time feedback or alerts to drivers based on eye/head movements in conjunction with vehicle dynamics should be an important research direction.     

Driver fatigue: a vision-based approach to automatic diagnosis

In this paper, we describe a system that locates and tracks the eyes of a driver. The purpose of such a system is to perform detection of driver fatigue. By mounting a small camera inside the car, we can monitor the face of the driver and look for eye movements which indicate that the driver is no longer in condition to drive. In such a case, a warning signal should be issued. This paper describes how to find and track the eyes. We also describe a method that can determine if the eyes are open or closed. The primary criterion for this system is that it must be highly non-intrusive. The system must also operate regardless of the texture and the color of the face. It must also be able to handle changing conditions such as changes in light, shadows, reflections, etc. Initial experimental results are very promising even when the driver moves his/her head in a way such that the camera does not have a frontal view of the driver’s face.     

Theory of jam-absorption driving

“Can a single car really absorb a traffic jam without making new jams?” In this paper, we focus on this frequently-discussed question, and have succeeded in making a theoretical framework of a driving technique how to absorb a traffic jam by using a minimal microscopic model. Jam-absorption driving comes from Beaty (Beaty, 1998, Beaty, 2013), and it is composed of a sequence of two actions termed the “slow-in” and “fast-out”. The “slow-in” is the action to avoid being captured by a jam and remove it by decelerating and taking a longer headway in advance. The “fast-out” is performed after the “slow-in”, and it is the action to follow the car in front without unnecessary time gaps by accelerating quickly. In our theoretical framework, we have represented the recipe of the actions such as the time–space points and the velocity. Moreover, we have clarified the condition of no secondary jams due to this driving, i.e., the condition that compression and expansion waves caused by this driving meet each other and disappear. Particularly, we have calculated how these waves propagates to the following cars and the point where and when they disappear. Besides, we have analyzed how this point moves in time–space diagrams by varying the timing to start the jam-absorption, and revealed that the pattern of this movement is not constant but changes greatly by the velocity-headway relationships. Furthermore, as a more realistic problem, we have formulated the driving for jam-absorption in two steps of deceleration, which brings rich patterns of collisions among compression and expansion waves.     

飞机降落冲击荷载作用下高铁隧道动力响应及疲劳损伤研究北大核心CSCD

针对运营期高铁隧道衬砌结构在飞机降落冲击荷载作用下的动力响应规律及疲劳损伤问题,以成自高铁下穿天府机场东二跑道区间隧道为工程背景,采用有限元分析的方法研究隧道动力响应及疲劳损伤规律。结果表明:B747-400型飞机在粗暴着陆后0.05 s时刻动力载荷达到最大值,约为500 kN;在单次粗暴着陆工况下,拱顶位移和受力最大,位移最大峰值为1.58 mm,拉应力最大峰值为437.79 kPa,压应力最大峰值为556.24 kPa,衬砌结构未出现塑性损伤;飞机荷载长期作用下,随着循环次数的增加,结构损伤部位和程度也随之增加,拱顶损伤最突出,其次为边墙,隧道衬砌在上方飞机长期粗暴着陆作用下的疲劳寿命大致为25 a。     

The design of an in-vehicle assistance system to support eco-driving

This driving simulator study was the second of two studies investigating the most effective and acceptable in-vehicle system for the provision of guidance on fuel efficient accelerator usage. Three eco-driving interfaces were selected for test (a second-order display visual display with auditory alerts and two haptic accelerator pedal systems) following a pilot study of 12 different interfaces. These systems were tested in a range of eco-driving scenarios involving acceleration, deceleration and speed maintenance, and assessed through their effects on fuel economy, vehicle control, distraction, and driver subjective feedback. The results suggest that a haptic accelerator pedal system is most effective for preventing over-acceleration, whilst minimal differences were observed between systems in terms of the effect of the assistance provided to prevent under-acceleration. The visual–auditory interface lowered the time spent looking towards the road, indicating a potential negative impact on driver safety from using this modality to provide continuous green driving support. Subjective results were consistent with the objective findings, with haptic pedal systems creating lower perceived workload than a visual–auditory interface. Driver acceptability ratings suggested a slight favouring of a haptic-force system for its usefulness, whereas the more subtle haptic-stiffness system was judged more acceptable to use. These findings offer suggestions for the design of a user-friendly, eco-driving device that can help drivers improve their fuel economy, specifically through the provision of real-time guidance on the manipulation of the accelerator pedal position.     

Optimal driving for single-vehicle fuel economy

This study uses fuel consumption simulators for 15 late-model automobiles to determine how one ought to drive to maximize fuel economy. The simulation is based on extensive on-road and dynamometer testing of the 15 cars. Dynamic programming is used to determine the optimal way to accelerate from rest to cruising speed, to drive a block between stop signs, and to cruise on hilly terrain while maintaining a given average speed. The dependence of fuel economy on cruising speed is also characterized for various road grades. Findings include that optimal speeds are generally higher for larger cars and higher on downgrades than on upgrades, and that the relative fuel penalty for exceeding the speed limit is no worse for small cars than large cars. Optimal control for accelerate-and-cruise and for driving between stop signs varies considerably from car to car; in the latter case fuel economy is much improved by achieving a rather low peak speed. Optimal control on hills is consistent from car to car and can achieve fuel economy 7% to 30% better than that of constant-speed driving on 3% to 6% grades. Results that appear generalizable to other cars are reduced to advice for the fuel-conscious driver.     

基于PERCLOS的驾驶员疲劳检测方法

PERCLOS法能方便的对驾驶员进行实时、非接触式疲劳检测,文章介绍了PERCLOS测评驾驶疲劳的机理、测量步骤及具体算法的实现过程,为驾驶员疲劳检测提供了一种新颖快捷的识别方法。     

An intersection turning movement estimation procedure based on path flow estimator

Estimation of intersection turning movements is one of the key inputs required for a variety of transportation analysis, including intersection geometric design, signal timing design, traffic impact assessment, and transportation planning. Conventional approaches that use manual techniques for estimation of turning movements are insensitive to congestion. The drawbacks of the manual techniques can be amended by integrating a network traffic model with a computation procedure capable of estimating turning movements from a set of link traffic counts and intersection turning movement counts. This study proposes using the path flow estimator, originally used to estimate path flows (hence origin–destination flows), to derive not only complete link flows, but also turning movements for the whole road network given some counts at selected roads and intersections. Two case studies using actual traffic counts are used to demonstrate the proposed intersection turning movement estimation procedure. Copyright © 2010 John Wiley & Sons, Ltd.     

Optimal recharging strategies for electric vehicle fleets with duration constraints

Electrical vehicles (EVs) have become a popular green transportation means recently because they have lower energy consumption costs and produce less pollution. The success of EVs relies on technologies to extend their driving range, which can be achieved by the good deployment of EV recharging stations. This paper considers a special EV network composed of fixed routes for an EV fleet, where each EV moves along its own cyclic tour of depots. By setting up a recharging station on a depot, an EV can recharge its battery for no longer than a pre-specified duration constraint. We seek an optimal deployment of recharging stations and an optimal recharging schedule for each EV such that all EVs can continue their tours in the planning horizon with minimum total costs. To solve this difficult location problem, we first propose a mixed integer program (MIP) formulation and then derive four new valid inequalities to shorten the solution time. Eight MIP models, which were created by adding different combinations of the four valid inequalities to the basic model, have been implemented to test their individual effectiveness and synergy over twelve randomly generated EV networks. Valuable managerial insights into the usage of valid inequalities and the relations between the battery capacity and the total costs, number of recharging facilities to be installed, and running time are analyzed.     

Determinants of subjective time estimates in simulated urban driving

An urban driving simulator is used to generate a data base for calibrating and testing a causal path model for subjective time estimates. The model specifies the determinants of subjective time through a web of direct and indirect interactions confirming a positive relationship between the density of urban environmental stimuli (e.g., traffic lights, turns) and time estimates. The results suggest that subjective time is predictable on the basis of time distance, physical distance, and obstacle-like variables. Some implications of the results of urban travel modeling are put forward.