Effects of apparent image velocity and complexity on the dynamic visual field using a high-speed train driving simulator

Train driving is a highly visual task. The visual capabilities of the train driver affects driving safety and driving performance. Understanding the effects of train speed and background image complexity on the visual behavior of the high-speed train driver is essential for optimizing performance and safety. This study investigated the role of the apparent image velocity and complexity on the dynamic visual field of drivers. Participants in a repeated-measures experiment drove a train at nine different speeds in a state-of-the-art high-speed train simulator. Eye movement analysis indicated that the effect of image velocity on the dynamic visual field of high-speed train driver was significant while image complexity had no effect on it. The fixation range was increasingly concentrated on the middle of the track as the speed increased, meanwhile there was a logarithmic decline in fixation range for areas surrounding the track. The extent of the visual search field decreased gradually, both vertically and horizontally, as the speed of train increased, and the rate of decrease was more rapid in the vertical direction. A model is proposed that predicts the extent of this tunnel vision phenomenon as a function of the train speed.Relevance to industryThis finding can be used as a basis for the design of high-speed railway system and as a foundation for improving the operational procedures of high-speed train driver for safety.     

Effects of advertising billboards during simulated driving

There is currently a great deal of interest in the problem of driver distraction. Most research focuses on distractions from inside the vehicle, but drivers can also be distracted by objects outside the vehicle. Major roads are increasingly becoming sites for advertising billboards, and there is little research on the potential effects of this advertising on driving performance. The driving simulator experiment presented here examines the effects of billboards on drivers, including older and inexperienced drivers who may be more vulnerable to distractions. The presence of billboards changed drivers’ patterns of visual attention, increased the amount of time needed for drivers to respond to road signs, and increased the number of errors in this driving task.     

A Dynamic Travel Time Model for Spillback

In this paper we introduce travel time models that incorporate spillback and bottleneck phenomena. In particular, we study a model for determining the link travel times for drivers entering a link as well as drivers already in the link but whose travel times are affected by a significant change in traffic conditions (e.g. spillback or bottleneck phenomena). To achieve this goal, we extend the fluid dynamics travel time models proposed by Perakis (1997)and subsequently by Kachani (2002), and Kachani and Perakis (2001), to also incorporate such phenomena. These models utilize fluid dynamics laws for compressible flow to capture a variety of flow patterns such as the formation and dissipation of queues, drivers’ response to upstream congestion or decongestion and drivers’ reaction time. We propose variants of these models that explicitly account for spillback and bottleneck phenomena. Our investigation considers both separable and non-separable velocity functions.     

Driver detection and recognition of lineside signals and signs at different approach speeds

A study was carried out using simulation to investigate driver responses to lineside signals and signs at various approach speeds. The objectives of the study were: (1) to find out whether train speed would significantly affect signal/sign reading; (2) to examine at which point certain types of signs or signals could be detected or recognised, and (3) to determine a speed cut-off level above which certain types of signs or signals are no longer recognisable or detectable. Fifty-seven train drivers from 12 Train Operating Companies in the UK participated in the trials. Twenty different types of lineside signs and ten types of signals were tested under six different approach speeds ranging from 100 to 350 km/h (62–218 mph). Driver performance measures were ‘time remaining to the signal/sign’ at the point of detection or recognition, and reading error rate. The results showed a significant influence of train speed on driver responses to lineside signals/signs and demonstrated a non-linear relationship between driver responses to signals/signs and approach speed. This has been used to estimate a maximum approach speed limit within which a specific signal or sign can be correctly detected or recognised. The findings and implications of the study are discussed in the paper.     

Using Queuing Network and Logistic Regression to Model Driving with a Visual Distraction Task

Computational dual-task models of driving with a secondary task can help compute, simulate, and predict driving behavior in dual task situations. These models can thus help improve the process of developing in-vehicle devices by reducing or eliminating the need for conducting driver experiments in the early stage of the development. Further, these models can help improve traffic flow simulation. This article develops a dual-task model of driving with a visual distraction task using the Queuing Network model of driver lateral control and a logistic regression model. The comparison between the model simulation data and the human data from drivers in a driving simulator shows that this computational model can perform driving with a secondary visual task well and its performance is consistent with the driver data.     

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.     

Utilization of Visual Information Perception Characteristics to Improve Classification Accuracy of Driver’s Visual Search Intention for Intelligent Vehicle

Understanding and predicting a driver’s behaviors in a vehicle is a prospective function embedded in a smart car. Beyond the patterns of observable behaviors, driver’s intention could be identified based on goal-driven behaviors. A computational model to classify driver intention in visual search which is finding a target with one’s eyes as moving selective attention across a search field, could improve the level of intelligence that a smart car could demonstrate. To develop a computational cognitive that explains the underlying cognitive process and reproduces drivers’ behaviors, particular parameters in human cognitive process should be specified. In this study, 2 issues are considered as influential factors on a driver’s eye movements: a driver’s visual information processing characteristics (VIPCs) and the purpose of visual search. To assess an individual’s VIPC, 4 psychological experiments—Donders’s reaction time, mental rotation, signal detection, and Stroop experiments—were utilized. Upon applying k-means clustering method, 114 drivers were divided into 9 driver groups. To investigate the influence of task goal on a driver’s eye movement, driving simulation was conducted to collect a driver’s eye movement data under the given purpose of visual search (perceptual and cognitive tasks). The empirical data showed that there were significant differences in a driver’s oculomotor behavior, such as response time, average fixation time, and average glance duration between the driver groups and the purposes of visual search. The effectiveness of using VIPC for grouping drivers was tested with task goal classification model by comparing the models’ performance when drivers were grouped by typical demographic data such as gender. Results show that grouping based on VIPC improves accuracy and stability of prediction of the model on a driver’s intention underlying visual search behaviors. This study would benefit future studies focusing on personalization and adaptive interfaces in the development of smart car.     

Participatory design with train drivers — a process analysis

A participatory design process involving train drivers is analyzed and described in this paper. A group of six drivers were involved in the design process, and within a short period, four design iterations were completed. The present case study was the final part of a larger research project (TRAIN) investigating the train driving task including the drivers' information environment, number and nature of hours worked, work situation and work environment, and their effect on the drivers' behaviour and the train driver system safety.Although usability activities are widely used in IT development today, the users are not involved to the desired extent. This paper argues that to produce usable systems, quality time has to be spent initially to acquire knowledge of a work domain and establishing a common ground in terms of shared knowledge and a better understanding of the work context between the parties involved in system development. Our suggestions on participatory analysis and design that conclude the paper are based on the present case study including train drivers, as well as our experiences from previous case studies.     

A visual reasoning-based approach for driving experience improvement in the AR-assisted head-up displays

Enabled by advanced data analytics and intelligent computing, augmented reality head-up displays (AR-HUDs) are appraised with a certain degree of intelligence towards an in-car assistance system providing more convenience for drivers and ensuring safer traffic. Nevertheless, current AR-HUDs systems fail to analyze perceptual results with recommended driving strategies as the cognitive intelligence, while solely rely on driver’s own decision-makings. To pave the way, this work stepwise proposes a visual reasoning-based approach for presenting drivers with perceptual, predictive, and reasoning information onto AR-HUDs toward cognitive intelligence. Firstly, a Driving Scenario Knowledge Graph comprising many road elements and empirical knowledge is established appropriately. Then, by analyzing the video streams and images collected by an in-car visual camera, the driving scene can be perceived comprehensively, including 1) identifying road elements and 2) moving elements’ intention recognition. Afterwards, a graph-based driving scenario reasoning model, driving scenario-adaptive KAGNET, is built for achieving driving strategy recommendations. Moreover, the analyzed information is shown on the HUDs via pre-defined AR graphics to support drivers intuitively. A case study is given lastly to prove its feasibility. As an explorative study, some limitations and future work are emphasized to attract further study and open discussion in this area for pursuing the better implementation of AR-HUDs.     

The Deceleration Detection Flicker Test: A measure of experience?

A new driving-related test is described, which provides a simple procedure to investigate a wide range of distraction and visual attention issues in driving. It requires participants to divide attention between multiple sources of potential hazard within a driving scene. The primary task requires a response when the perceived headway to a car ahead diminishes across a series of static images. Two experiments used different secondary tasks to demonstrate that central task performance is sensitive to driver experience, with highly experienced drivers better able to notice a change in apparent headway to the lead vehicle. Furthermore, background visual complexity, such as visually cluttered urban roads compared to sparser rural roads, exacerbates the experiential differences. The results suggest that the Deceleration Detection Flicker Test taps into a real driving-related skill and may provide a useful methodology for future investigation of a wide range of visual processing issues in driving research.     

Bridging the gap between analysis and design: improving existing driver interfaces with tools from the framework of cognitive work analysis

Two studies of train driving are presented, both within the framework of cognitive work analysis. In the first study, the modelling tool abstraction–decomposition space is adapted to routine conditions, making the analysis more representative for normal procedures. A major contribution to these analyses was the use of the method for ‘collegial verbalisation’. One particular advantage with this method over other verbalisation methods is that it supplies the analysts with data that contain much more information, but not at the expense of being more subjective. On the contrary, this method produces think-aloud protocols from video-recordings that do not have to be interpreted by the researcher. From these analyses, it was possible to distinguish information that is an intrinsic part of the train driver task from information that is dependent on the configuration and design of the current support system. The analyses show that the driver works in three rather separate time intervals with a long-range, a short-term and an immediate sense of perspective. The driver switches between these while travelling between two stations. Based on these behaviour-shaping constraints, a prototype of a planning area of a driver interface was developed, making feed-forward planning possible for the driver. Four design iterations were completed, using a user-centred system design (UCSD) approach. Early tests show that the planning area of the interface supports the feed-forward decision strategy used by drivers who prefer an active driving style. However, the driver group also made substantial changes in the design, indicating that UCSD is an efficient tool in order to capture user competencies, and to bridge the gap between analysis and design.     

The design of next generation in-vehicle navigation systems for the older driver

It has been proposed that the current design of in-vehicle displays may not be appropriate for the older driver. This paper describes an empirical, road-based investigation of the benefits to older and younger drivers of providing landmarks within the instructions presented by an in-vehicle navigation system. Thirty two participants navigated a challenging urban route using either landmarks or distance information to identify the location of forthcoming manoeuvres. A range of driver behaviour measures were collected, including visual glance data, driving errors, driver workload, navigation errors, navigation confidence, and pre and post-trial driver attitudinal responses. Results show that, for older and younger drivers, landmarks reduced the time spent glancing to a visual display, reduced navigation and driving errors, and influenced driver confidence. There were some key differences between the older and younger drivers. The wider implications for the design of in-car interfaces for the older driver are discussed.     

The impact of distraction mitigation strategies on driving performance

OBJECTIVES: An experiment was conducted to assess the effects of distraction mitigation strategies on drivers' performance and productivity while engaged in an in-vehicle information system task. BACKGROUND: Previous studies show that in-vehicle tasks undermine driver safety and there is a need to mitigate driver distraction. METHOD: An advising strategy that alerts drivers to potential dangers and a locking strategy that prevents the driver from continuing the distracting task were presented to 16 middle-aged and 12 older drivers in a driving simulator in two modes (auditory, visual) and two road conditions (curves, braking events). RESULTS: Distraction was a problem for both age groups. Visual distractions were more detrimental than auditory ones for curve negotiation, as depicted by more erratic steering, F (6, 155) = 26.76, p < .05. Drivers did brake more abruptly under auditory distractions, but this effect was mitigated by both the advising, t (155) = 8.37, p < .05, and locking strategies, t (155) = 8.49, p < .05. The locking strategy also resulted in longer minimum time to collision for middle-aged drivers engaged in visual distractions, F (6, 138) = 2.43, p < .05. CONCLUSIONS: Adaptive interfaces can reduce abrupt braking on curve entries resulting from auditory distractions and can also improve the braking response for distracted drivers. APPLICATION: These strategies can be incorporated into existing in-vehicle systems, thus mitigating the effects of distraction and improving driver performance.     

基于VR的驾驶仿真及安全教育系统开发

针对全国道路交通事故高发现状及传统驾驶安全教育方式单一、培训效果差的缺点,基于虚拟现实技术（VR）,在引发交通事故人为因素理论基础上,开发驾驶仿真及安全教育系统。系统基于Unity3D引擎,构建了基于道路实景数据的虚拟场景,并联合SUMO实现了道路交通流仿真,通过VR技术仿真驾驶环境及驾驶行为;基于碰撞检测原理,建立了关卡违规触发机制,编码自定义屏幕空间渲染方式模拟驾驶员视觉效果,并构建了基于图像的交通事故现场三维全景,从认知、感知层面培训驾驶员安全驾驶。实用性测试结果表明,系统实现了不同道路场景、气象条件与交通状况下的驾驶模拟及安全培训,增强了使用者的学习兴趣,提高了使用者驾驶安全素养,具有较强的实用性。     

Personnel scheduling in a complex logistic system: a railway application case

In complex logistic systems, such as transportation systems, dealing with personnel scheduling is a non-trivial task. Duties have to be created and assigned to workers in a way to optimize a certain objective function. In this paper, in particular, we consider the case of scheduling train drivers on a railway subnetwork. Train driver scheduling involves the construction of feasible duties from a set of trips to be carried out by a number of train drivers. Each duty consists of a sequence of trips to be carried out by a single train driver on a single day. The duties should be such that: each trip is covered by at least one duty, each duty satisfies feasibility constraints, additional constraints involving the complete schedule are satisfied, one or several objectives are met. In this paper we focus on minimizing the number of duties and on maximizing the robustness of the obtained schedule for outside disruptions. We present an implicit column generation solution approach. We describe a heuristic procedure to find an initial feasible solution together with a heuristic branch-and-price algorithm based on a dynamic programming algorithm for the pricing-out of columns. We tested our approach on the timetable of the Intercity train series 500, 700, 1600 and 1700 of NS Reizigers, the largest Dutch operator of passengers trains.     

Elderly and young driver's reaction to an in-car enforcement and tutoring system

A system that contrasts driver behaviour with normative behaviour was tested in an advanced driving simulator. Drivers were provided with auditory and visual tutoring messages if deviations were detected from normative, i.e. legally allowed behaviour with respect to a selection of offences. Results showed that the system was very effective in increasing law-abiding behaviour, which has a major positive effect on traffic safety. However, driver mental effort, as indicated by self-reports and drivers' physiological states, was slightly increased in conditions where drivers received feedback. Opinion about the tutoring system was positive in terms of usefulness. Self-reports on satisfaction differed between age groups; young drivers rated it low, while elderly drivers held a positive attitude.     

Haptic gas pedal feedback

Active driver support systems either automate a control task or present warnings to drivers when their safety is seriously degraded. In a novel approach, utilising neither automation nor discrete warnings, a haptic gas pedal (accelerator) interface was developed that continuously presents car-following support information, keeping the driver in the loop. This interface was tested in a fixed-base driving simulator. Twenty-one drivers between the ages of 24 and 30 years participated in a driving experiment to investigate the effects of haptic gas pedal feedback on car-following behaviour. Results of the experiment indicate that when haptic feedback was presented to the drivers, some improvement in car-following performance was achieved, while control activity decreased. Further research is needed to investigate the effectiveness of the system in more varied driving conditions. Haptics is an under-used modality in the application of human support interfaces, which usually draw on vision or hearing. This study demonstrates how haptics can be used to create an effective driver support interface.     

Haptic gas pedal feedback

Active driver support systems either automate a control task or present warnings to drivers when their safety is seriously degraded. In a novel approach, utilising neither automation nor discrete warnings, a haptic gas pedal (accelerator) interface was developed that continuously presents car-following support information, keeping the driver in the loop. This interface was tested in a fixed-base driving simulator. Twenty-one drivers between the ages of 24 and 30 years participated in a driving experiment to investigate the effects of haptic gas pedal feedback on car-following behaviour. Results of the experiment indicate that when haptic feedback was presented to the drivers, some improvement in car-following performance was achieved, while control activity decreased. Further research is needed to investigate the effectiveness of the system in more varied driving conditions. Haptics is an under-used modality in the application of human support interfaces, which usually draw on vision or hearing. This study demonstrates how haptics can be used to create an effective driver support interface.     

Evaluating the effects of bilingual traffic signs on driver performance and safety

Variable message signs (VMS) can provide immediate and relevant information to road users and bilingual VMS can provide great flexibility in countries where a significant proportion of the population speak an alternative language to the majority. The study reported here evaluates the effect of various bilingual VMS configurations on driver behaviour and safety. The aim of the study was to determine whether or not the visual distraction associated with bilingual VMS signs of different configurations (length, complexity) impacted on driving performance. A driving simulator was used to allow full control over the scenarios, road environment and sign configuration and both longitudinal and lateral driver performance was assessed. Drivers were able to read one- and two-line monolingual signs and two-line bilingual signs without disruption to their driving behaviour. However, drivers significantly reduced their speed in order to read four-line monolingual and four-line bilingual signs, accompanied by an increase in headway to the vehicle in front. This implies that drivers are possibly reading the irrelevant text on the bilingual sign and various methods for reducing this effect are discussed.     

The use of expert knowledge in the development of simulations for train driver training

The current study was undertaken to inform the development of simulations for improving train driver’s decision making under degraded track conditions. Trains are sophisticated heavy machinery and their performance is ever increasing resulting in the driving task becoming more complex and progressively dominated by cognitive and perceptual skills. A critical part of reducing the potential for train driver error and of increasing performance lies in the appropriate design of simulation training. In the current study a cognitive task analysis, using the critical decision method (CDM) was undertaken using a focus group research design. The process resulted in increased knowledge of expert train driver decision-making processes. Across four major incidents analyzed 11 decision points, 17 cues, 30 essential responsive actions and 45 possible errors where identified. The use of these results for supporting the design of simulation training and associated performance measures is discussed.