Optical information for car following: the driving by visual angle (DVA) model

OBJECTIVE: The present study developed and tested a model of car following by human drivers. BACKGROUND: Previous models of car following are based on 3-D parameters such as lead vehicle speed and distance information, which are not directly available to a driver. In the present paper we present the driving by visual angle (DVA) model, which is based on the visual information (visual angle and rate of change of visual angle) available to the driver. METHOD: Two experiments in a driving simulator examined car-following performance in response to speed variations of a lead vehicle defined by a sum of sine wave oscillations and ramp acceleration functions. In addition, the model was applied to six driving events using real world-driving data. RESULTS: The model provided a good fit to car-following performance in the driving simulation studies as well as in real-world driving performance. A comparison with the advanced interactive microscopic simulator for urban and nonurban networks (AIMSUN) model, which is based on 3-D parameters, suggests that the DVA was more predictive of driver behavior in matching lead vehicle speed and distance headway. CONCLUSION: Car-following behavior can be modeled using only visual information to the driver and can produce performance more predictive of driver performance than models based on 3-D (speed or distance) information. APPLICATION: The DVA model has applications to several traffic safety issues, including automated driving systems and traffic flow models.     

基于驾驶员转向模型的共享控制系统EI北大核心CSCD

针对车辆驾驶对于共享控制系统实用性的需求,提出了基于驾驶员转向模型的共享控制系统.基于驾驶员的视觉预瞄特性与神经肌肉特性建立了驾驶员转向模型,通过遗传算法辨识模型参数并分析其与车速和道路曲率之间的函数关系;采用模糊权重分配策略合理分配驾驶权重;本文利用基于所开发的CarMaker驾驶模拟实验平台,对系统进行在线测试和验证,结果表明该系统不仅能够更好地提升车辆的轨迹跟踪精度和安全性,辅助驾驶员转向,还能够极大地减轻驾驶员负荷.     

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.     

An investigation of train driver visual strategies

Train driving is primarily a visual task; train drivers are required to monitor the dynamic scene visually both outside and inside the train cab. Poor performance on this visual task may lead to errors, such as signals passed at danger. It is therefore important to understand the visual strategies that train drivers employ when monitoring and searching the visual scene for key items, such as signals. Prior to this investigation, a pilot study had already been carried out using an eye tracking technique to investigate train drivers’ visual behaviour and to collect data on driver monitoring of the visual environment, Groeger et al. (2003) Pilot study of train drivers’ eye movements, University of Surrey. However, a larger set of data was needed in order to understand more fully train driver visual behaviour and strategies. In light of this need, the Transport Research Laboratory produced a methodology for the assessment of UK train driver visual strategies, on behalf of the Rail Safety and Standards Board and applied this methodology to conduct a large-scale trial. The study collected a wealth of data on train drivers’ visual behaviour with the aim of providing a greater understanding of the strategies adopted. The corneal dark-eye tracking system chosen for these trials tracks human visual search and scanning patterns, and was fitted to 86 drivers whilst driving in-service trains. Data collected include the duration and frequency of glances made towards different elements of the visual scene. In addition, the train drivers were interviewed after driving the routes, to try and understand the thought processes behind the behaviour observed. Statistical analysis of over 600 signal approaches was conducted. This analysis revealed that signal aspect, preceding signal aspect, signal type and signal complexity are important factors, which affect the visual behaviour of train drivers. Train driver interview data revealed that driver expectation also plays a significant role in train driving. The findings of this study have implications for the rail industry in terms of infrastructure design, design of the driving task and driver training. However, train driving is extremely complex and the data from this study only begin to describe and explain train driver visual strategies in the specific context of signal approaches. This study has provided a wealth of data and further analysis of it is needed to investigate the role of other factors and the complex relationships between factors during signal approaches and other driving situations systematically. Finally, there are important aspects of visual behaviour that cannot be examined using these data or this method. Investigation of other aspects of visual behaviour, such as peripheral vision, will require other methods such as simulation.     

Modeling task completion time of in-vehicle information systems while driving with keystroke level modeling

The use of touchscreen-based in-vehicle information systems (IVIS) is increasing. To ensure safe driving, it is important to evaluate IVIS task performance during driving situations. Therefore, we proposed a model to assess the task completion time (TCT) of IVIS tasks while driving using a keystroke-level modeling (KLM) technique. The basic assumptions and heuristic rules of driver behaviors were considered. In addition, based on the characteristics of visual and manual IVIS interactions, we determined the basic unit operators (i.e., visual, manual, and mental operators). User experiments were conducted to determine the individual execution times of unit tasks and to measure the TCT of IVIS tasks while driving. Based on the heuristic rules for model development and individual task execution times, we derive a predictive model for the TCT of IVIS tasks. We used a regression analysis to validate the modeling procedure, showing that the observed TCT was found to have a strong positive correlation with the predicted time from the modeling process. The findings showed that the task completion time needed to perform a secondary task in a driving context can be predicted by KLM. This study provides meaningful insights into the design of touchscreen-based IVIS to enhance driving safety.     

Modelling the hare and the tortoise: predicting the range of in-vehicle task times using critical path analysis

Analytic models can enable predictions about important aspects of the usability of in-vehicle information systems (IVIS) to be made at an early stage of the product development process. Task times provide a quantitative measure of user performance and are therefore important in the evaluation of IVIS usability. In this study, critical path analysis (CPA) was used to model IVIS task times in a stationary vehicle, and the technique was extended to produce predictions for slowperson and fastperson performance, as well as average user (middleperson) performance. The CPA-predicted task times were compared to task times recorded in an empirical simulator study of IVIS interaction, and the predicted times were, on average, within acceptable precision limits. This work forms the foundation for extension of the CPA model to predict IVIS task times in a moving vehicle, to reflect the demands of the dual-task driving scenario.

Practitioner Summary: The CPA method was extended for the prediction of slowperson and fastperson IVIS task times. Comparison of the model predictions with empirical data demonstrated acceptable precision. The CPA model can be used in early IVIS evaluation; however, there is a need to extend it to represent the dual-task driving scenario.     

虚拟驾驶员视觉感知模型研究

视觉是驾驶员获得驾驶信息的主要通道,虚拟驾驶员的视觉感知模型是驾驶行为建模与仿真的重要内容,直接影响驾驶行为仿真的逼真度。介绍了四种视觉感知建模方法,根据视觉感知系统的反馈性和选择性,建立了视觉感知模型。在模型中,将视觉感知分为感觉和知觉两个过程,并引入了注意力、记忆力、驾驶疲劳和驾驶经验等因素,对每个因素进行分析。通过对虚拟驾驶员视觉感知模型的仿真实验,验证了该模型的可靠性和有效性,为驾驶行为研究提供了基础。     

On-line driver workload estimation. Effects of road situation and age on secondary task measures

In order to develop a driver-car interface that adapts the presentation of messages generated by in-vehicle information systems to driver workload, two experiments investigated potential determinants of driver visual and mental workload as indicated by performance on two secondary tasks. Experiment 1 suggested that road situation is a major determinant of visual and mental workload of the driver and that the processing resources of older drivers are somewhat more limited than those of younger and middle-aged drivers. Familiarity with the area of driving (when guided) and time of day (associated with traffic density) showed no secondary task effects. Experiment 2 showed that the categorization of road situations, proposed in Experiment 1, could underlie adaptation of visually loading messages to the workload incurred by driving. This was not found with respect to mentally loading messages.     

On-line driver workload estimation. Effects of road situation and age on secondary task measures

In order to develop a driver-car interface that adapts the presentation of messages generated by in-vehicle information systems to driver workload, two experiments investigated potential determinants of driver visual and mental workload as indicated by performance on two secondary tasks. Experiment 1 suggested that road situation is a major determinant of visual and mental workload of the driver and that the processing resources of older drivers are somewhat more limited than those of younger and middle-aged drivers. Familiarity with the area of driving (when guided) and time of day (associated with traffic density) showed no secondary task effects. Experiment 2 showed that the categorization of road situations, proposed in Experiment 1, could underlie adaptation of visually loading messages to the workload incurred by driving. This was not found with respect to mentally loading messages.     

Visual perception modelling for intelligent virtual driver agents in synthetic driving simulation

This paper introduces a vision model used within a synthetic (or purely software-based) driving simulation framework. This framework represents driver decision-making, individual vehicle movement and emergent traffic flow, and is intended to aid the integration of driver psychology, traffic management and vehicle engineering. The aims of developing the vision model discussed here are twofold: Firstly, to remove the unrealistic availability of ‘perfect knowledge’ concerning the positions and velocities of vehicles in a simulation and secondly, to provide a means of introducing deeper cognitive models of driver reasoning and behaviour. The paper presents the essential mechanisms of the vision model, along with the results of initial validation experiments conducted with Leicestershire Constabulary, Traffic Division in the UK. In these experiments, subjects' visual perception of positions and speeds of moving vehicles were measured and compared with estimations of the agent based driving simulator. The results have demonstrated the feasibility of modelling driver vision within an agent based traffic simulation using concepts derived from AI and ALife systems. The paper is completed by a short discussion on the future development of cognitive models enabled through more detailed vision modelling.     

Speech-based interaction in multitask conditions: impact of prompt modality

Speech-based interaction is often recognized as appropriate for hands-busy, eyes-busy multitask situations. The objective of this study was to explore prompt-guided speech-based interaction and the impact of prompt modality on overall performance in such situations. A dual-task paradigm was employed, with tracking as a primary task and speech-based data input as a secondary task. There were three tracking conditions: no tracking, basic, and difficult tracking. Two prompt modalities were used for the speech interaction: a dialogue with spoken prompts and a dialogue with visual prompts. Data entry duration was longer with the speech prompts than with the visual prompts, regardless of whether or not there was tracking or its level of difficulty. However, when tracking was difficult, data entry duration was similar for both spoken and visual prompts. Tracking performance was also affected by the prompt modality, with poorer performance obtained when the prompts were visual. The findings are discussed in terms of multiple resource theory and the possible implications for speech-based interactions in multitask situations. Actual or potential applications of this research include the design of speech-based dialogues for multitask situations such as driving and other hands-busy, eyes-busy situations.     

Effectiveness of auditory and tactile crossmodal cues in a dual-task visual and auditory scenario

Abstract

In this study, we examined how spatially informative auditory and tactile cues affected participants’ performance on a visual search task while they simultaneously performed a secondary auditory task. Visual search task performance was assessed via reaction time and accuracy. Tactile and auditory cues provided the approximate location of the visual target within the search display. The inclusion of tactile and auditory cues improved performance in comparison to the no-cue baseline conditions. In comparison to the no-cue conditions, both tactile and auditory cues resulted in faster response times in the visual search only (single task) and visual–auditory (dual-task) conditions. However, the effectiveness of auditory and tactile cueing for visual task accuracy was shown to be dependent on task-type condition. Crossmodal cueing remains a viable strategy for improving task performance without increasing attentional load within a singular sensory modality.

Practitioner Summary: Crossmodal cueing with dual-task performance has not been widely explored, yet has practical applications. We examined the effects of auditory and tactile crossmodal cues on visual search performance, with and without a secondary auditory task. Tactile cues aided visual search accuracy when also engaged in a secondary auditory task, whereas auditory cues did not.     

虚拟驾驶员视觉注意力模型研究

为提高驾驶员视觉感知行为仿真的逼真性,注意力模型成为驾驶行为研究的重点。介绍了注意力的认知模型,根据注意力的双加工理论,建立了虚拟驾驶员视觉注意力模型,将视觉注意过程分为预注意和集中注意两个阶段,在预注意阶段,基于图像的相位谱信息建立了图像的特征显著图,实现注意的自下而上过程。使用RBF网络实现了预注意阶段的自下而上和自上而下两个过程的结合。在集中注意阶段,实现了注意力的聚焦效应。对视觉注意力模型进行了仿真实验,验证了模型的有效性,为虚拟驾驶员视觉感知行为建模与仿真提供了基础。     

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.     

What's skill got to do with it? Vehicle automation and driver mental workload

Previous research has found that vehicle automation systems can reduce driver mental workload, with implications for attentional resources that can be detrimental to performance. The present paper considers how the development of automaticity within the driving task may influence performance in underload situations. Driver skill and vehicle automation were manipulated in a driving simulator, with four levels of each variable. Mental workload was assessed using a secondary task measure and eye movements were recorded to infer attentional capacity. The effects of automation on driver mental workload were quite robust across skill levels, but the most intriguing findings were from the eye movement data. It was found that, with little exception, attentional capacity and mental workload were directly related at all levels of driver skill, consistent with earlier studies. The results are discussed with reference to applied theories of cognition and the design of automation.     

An integrated measure of display clutter based on feature content,user knowledge and attention allocation factors

Existing measures of display clutter in the literature generally exhibit weak correlations with task performance, which limits their utility in safety-critical domains. A literature review led to formulation of an integrated display data- and user knowledge-driven measure of display clutter. A driving simulation experiment was conducted in which participants were asked to search ‘high’ and ‘low’ clutter displays for navigation information. Data-driven measures and subjective perceptions of clutter were collected along with patterns of visual attention allocation and driving performance responses during time periods in which participants searched the navigation display for information. The new integrated measure was more strongly correlated with driving performance than other, previously developed measures of clutter, particularly in the case of low-clutter displays. Integrating display data and user knowledge factors with patterns of visual attention allocation shows promise for measuring display clutter and correlation with task performance, particularly for low-clutter displays.

Practitioner Summary: A novel measure of display clutter was formulated, accounting for display data content, user knowledge states and patterns of visual attention allocation. The measure was evaluated in terms of correlations with driver performance in a safety-critical driving simulation study. The measure exhibited stronger correlations with task performance than previously defined measures.     

驾驶员多源信息融合协同仿真算法研究

交通仿真是交通控制与管理方案评价和优化的重要实验研究手段。传统的微观交通仿真模型,特别是刻画驾驶员行为的车辆跟驰模型,未能综合考虑交通环境中信息刺激的多源性和驾驶员任务集聚、协调反应的行为过程。文章利用Bayes方法描述驾驶员在复杂行驶环境中多源信息的融合过程,确定驾驶员任务集聚后对车辆应采取的驾驶行为。模型验证表明:交通仿真过程中,在车辆跟驰模型实施之前,利用Bayes算法模型化驾驶员在多源信息刺激下任务集聚、协同反应的过程是行之有效的。     

基于人类视觉皮层双通道模型的驾驶员眼动行为识别

驾驶员的危险行为会增加交通事故的发生率,目前对驾驶员行为的研究中大多通过面部识别等方法对异常行为如疲劳驾驶、接电话等进行识别.这种方法仅客观地对驾驶员行为进行分类,而忽略了他们在驾驶过程中的主观心理.眼动仪是记录和分析驾驶员眼动数据的有效工具,可以清晰地了解驾驶员的想法并总结其视觉认知模式.因为目前还没有针对驾驶员眼动...     

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.     

Comparative evaluation of performance measures for human driving skills

In this paper, we evaluate the adequacy of several performance measures for the evaluation of driving skills between different drivers. This work was motivated by the need for a training system that captures the driving skills of an expert driver and transfers the skills to novice drivers using a haptic-enabled driving simulator. The performance measures examined include traditional task performance measures, e.g., the mean position error, and a stochastic distance between a pair of hidden Markov models (HMMs), each of which is trained for an individual driver. The emphasis of the latter is on the differences between the stochastic somatosensory processes of human driving skills. For the evaluation, we developed a driving simulator and carried out an experiment that collected the driving data of an expert driver whose data were used as a reference for comparison and of many other subjects. The performance measures were computed from the experimental data, and they were compared to each other. We also collected the subjective judgement scores of the driver’s skills made by a highly-experienced external evaluator, and these subjective scores were compared with the objective performance measures. Analysis results showed that the HMM-based distance metric had a moderately high correlation between the subjective scores and it was also consistent with the other task performance measures, indicating the adequacy of the HMM-based metric as an objective performance measure for driving skill learning. The findings of this work can contribute to developing a driving simulator for training with an objective assessment function of driving skills.