Learning to make errors: evidence from a driving task simulation

Human errors represent a mismatch between the demands of an operational system and what the operator does. If they cannot be reversed, their consequences may be severe. Errors are frequently classified as design-or operator-induced. A third class of errors may also be identified, namely process-induced errors. Such errors arise out of on-going processes which typically extend over time. One such process is that of learning. In relation to the acquisition of skills, for example, learning frequently involves a trial-and-error component. Accidents by inexperienced drivers may represent a severe consequence of such errors. Errors may also arise out of particular learning experiences which provide a distorted underestimate of objective risk and/or motivate high risk behaviour. These phenomena are investigated in a computer simulation of the driving task. The relationship is discussed between various kinds of learning experience and the development of situations in which the possibility of error recovery declines. Some suggestions for reducing the frequency of irreversible errors and for increasing the data base for human error in vehicle driving are made.     

Drivers' margins of safety considered as a focus for research on error

Accident statistics alone cannot provide a sound understanding of driver error, although they can assist the evaluation of remedial measures against errors and accidents. Roadside observation of drivers' errors can provide a valid index of their relative riskiness and of overall accident frequency, but only in route-specific applications. Field testing of hypotheses developed from theories of driver error is seen to be a far more valid and arguably more cost-effective method of improving road safety than relying on post hoc subjective assessments of error contributions to accident statistics. The distinction between driving task and envronmental factors which contribute to error production and those which constrain error correction is not well-documented in road accident studies. Yet it seems essential to make this distinction if we are to reach a sound understanding of research requirements in this field and hence identify and evaluate cost effective countermeasures against driver error. The bias which certain drivers appear to have towards inadequate safety margins is seen to provide an instructive theoretical framework for field studies of error production and error correction as contributory factors in traffic accident causation.     

The limits of path error-neglecting in straight lane driving

Abstract

It is argued that driving cannot simply be considered as a permanent, closed-loop task. Time-to-line-crossing (TLC) is used as a measure to quantify the potential role of visual open-loop and path-error-neglecting strategies. Basically, TLC represents the time available for a driver to neglect path errors until the moment at which any part of the vehicle reaches one of the lane boundaries. The strategy adopted by drivers during error-neglecting should be represented in terms of decision rules, describing how drivers switch from error-neglecting to error-correcting when approaching the edge of a lane. The experiment to be presented in this paper was designed to provide these rules for a straight lane-keeping task. Drivers were instructed to neglect the vehicle path error and to switch to error-correcting only at that moment when the vehicle heading could still comfortably be corrected to prevent a crossing of the lane boundary. The results show that the lateral distance from the lane boundary at which drivers switch to error-correction increases about linearly with the lateral approach speed. This mechanism results in an approximately constant TLC (time) distance at the moment of decision: this result being consistent over a broad range of speeds.     

Speech-based E-mail and driver behavior: effects of an in-vehicle message system interface

As mobile office technology becomes more advanced, drivers have increased opportunity to process information "on the move." Although speech-based interfaces can minimize direct interference with driving, the cognitive demands associated with such systems may still cause distraction. We studied the effects on driving performance of an in-vehicle simulated "E-mail" message system; E-mails were either system controlled or driver controlled. A high-fidelity, fixed-base driving simulator was used to test 19 participants on a car-following task. Virtual traffic scenarios varying in driving demand. Drivers compensated for the secondary task by adopting longer headways but showed reduced anticipation of braking requirements and shorter time to collision. Drivers were also less reactive when processing E-mails, demonstrated by a reduction in steering wheel inputs. In most circumstances, there were advantages in providing drivers with control over when E-mails were opened. However, during periods without E-mail interaction in demanding traffic scenarios, drivers showed reduced braking anticipation. This may be a result of increased cognitive costs associated with the decision making process when using a driver-controlled interface when the task of scheduling E-mail acceptance is added to those of driving and E-mail response. Actual or potential applications of this research include the design of speech-based in-vehicle messaging systems.     

Errors in routine driving tasks: a model and proposed analysis technique

Abstract

Models of behaviour and cognitive errors are briefly reviewed. A model derived from the work of Reason and Rasmussen is applied to the analysis of accidents at a crossroad in Friesland where the bulk of accidents occurred to drivers familiar with the location. Detailed application of the predicted error mechanisms to the steps of the driving task allows testable hypotheses to be formulated to differentiate between a number of possible causes of the accidents. The implications for research into accidents during routine driving tasks are discussed.     

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.     

Adaptive strategy changes as a function of task demands: a study of car drivers

When drivers perform additional tasks while driving, research shows conflicting results: primary driving performance may deteriorate but adaptive changes such as reducing driving speed have also been noted. We hypothesized that the nature of the secondary task may be important: drivers may give more priority to tasks that serve goals of the driving task itself, for example route finding, than tasks not directly relevant for driving, for example tuning the radio. The main objective of the present driving simulator study was to test this hypothesis. Twenty subjects performed two different subsidiary tasks while driving through two levels of traffic density: a working memory (WM) task and a map reading (MAP) task. It was hypothesized that in high task demand situations, the WM task, irrelevant for the driving task, would be neglected more than the MAP task. The results confirmed the hypothesis: in MAP conditions, the WM task was indeed neglected, but map reading resulted in more swerving, indicating that the subjects looked at the map despite the high task demands. It is concluded that drivers will be highly motivated to get route information, and RG systems should therefore present their information in a readily understandable format.     

Adaptive strategy changes as a function of task demands: a study of car drivers

When drivers perform additional tasks while driving, research shows conflicting results: primary driving performance may deteriorate but adaptive changes such as reducing driving speed have also been noted. We hypothesized that the nature of the secondary task may be important: drivers may give more priority to tasks that serve goals of the driving task itself, for example route finding, than tasks not directly relevant for driving, for example tuning the radio. The main objective of the present driving simulator study was to test this hypothesis. Twenty subjects performed two different subsidiary tasks while driving through two levels of traffic density: a working memory (WM) task and a map reading (MAP) task. It was hypothesized that in high task demand situations, the WM task, irrelevant for the driving task, would be neglected more than the MAP task. The results confirmed the hypothesis: in MAP conditions, the WM task was indeed neglected, but map reading resulted in more swerving, indicating that the subjects looked at the map despite the high task demands. It is concluded that drivers will be highly motivated to get route information, and RG systems should therefore present their information in a readily understandable format.     

Effects of an in-vehicle collision avoidance warning system on short- and long-term driving performance

Many new in-vehicle systems focus on accident prevention by facilitating the driving task. One such driving aid is an in-vehicle collision avoidance warning system (IVCAWS), used to alert the driver to an impending collision. Our study evaluated the effects of an imperfect IVCAWS both on driver headway maintenance and on driver behavior in response to warning system errors. Our results showed that drivers tend to overestimate their headway and consequently drive with short and potentially dangerous headways, and that IVCAWSs are a useful tool for educating drivers to estimate headway more accurately. Moreover, our study showed that after a relatively short exposure to the system, drivers were able to maintain longer and safer headways for at least six months. The practical implications of these results are that the use of an IVCAWS should be considered for inclusion in driver education and training programs.     

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.     

ERRORS IN THE PERCEPTION OP RAILWAY SIGNALS

This paper is based upon observations of train drivers at work, and defines certain kinds of errors which may be made by drivers in perceiving railway signals. It discusses hypotheses relating incidence of error to various factors. It is suggested that the investigation of hypotheses of this kind, derived from the observation of the normal performance of the operative, is a valid method of accident study.     

Speed Estimation from a Moving Automobile

An experiment is described in which the role played by the senses of sight and hearing in the estimation of speed from a moving automobile was investigated. Subjects were driven in the front seat of a car whose speedometer was hidden from their view. The subjects' task was to estimate the speed of the car under the following four conditions of sensory awareness: (A) normal passenger; (B) unable to see— the subject wore a blindfold; (C) diminished hearing— the subject wore a sound excluder; (D) the subject wore both a blindfold and a sound excluder. Under all four conditions slow speeds (25 mph or less) were, on average, underestimated. For the two conditions which permitted hearing the mean estimates of normal driving speeds were without systematic error. However, for the two conditions involving diminished hearing the mean estimates were always lower than the set speeds, indicating that the sense of hearing is of great importance in the task of speed estimation. A subjective speed scale, which is not of the common power-law form, is derived for the normal passenger condition. This is used to predict how drivers would perform certain tasks which have previously been investigated experimentally, and the predictions agree satisfactorily with the observations.     

SocioTelematics: Harnessing social interaction-relationships in developing automotive applications

In a cooperative convoy, a vehicle interacts with other vehicles, service providers and infrastructure systems to make the travel safe and convenient. Through these interactions a vehicle can share its domain-specific information–acquired from service providers and infrastructure–with other vehicles in the convoy. Such interactions are subject to defined agreements and constraints between the entities (i.e., vehicle to vehicle, vehicle to service provider, and so on), which we refer as (social) interaction-relationships. Such relationships, however, may need to adapt with the changes of requirements. Also a driver may want to automate certain interactions to reduce distraction during driving. A cooperative convoy telematics system should support collaboration (i.e., allow drivers to share specific travel information) and coordination (i.e., allow drivers to automate interactions), and be able to adapt to cope with the changes of requirements.In this paper, we address these issues and demonstrate how our social interaction-relationships modelling technique can be exploited to develop a telematics system, called SocioTelematics, providing such functionalities. This system allows collaboration and coordination preferences explicitly specified and updated to cope with the changes. In particular, our service oriented implementation enhances adaptability of the system, making it easily deployable and changeable. We have implemented a prototype system based on a client–server architecture where the client application is developed for Android and the server is running on the Amazon cloud. The system’s performance and resource consumption were quantified using real life experiments that show the feasibility of our approach.     

How does people's trust in automated vehicles change after automation errors occur? An empirical study on dynamic trust in automated driving

When errors of automated vehicles (AVs) occur, drivers' trust can easily be destroyed, resulting in the reduction of the use of AVs. This study aims to examine how error of AVs declines driver's trust by impacting their subjective perceptions. A driving simulator experiment is conducted, in which 104 participants (male = 58; female = 46) experienced automated driving with automation errors and rated their trust. The results indicate that automation error will affect the driver's perceived predictability, perceived reliability, and perceived safety, which will lead to the decline of trust and abandonment of automated driving. With the occurrence of automation error of AVs, perceived safety plays a more critical role in drivers' trust. In addition, when automation errors occur in specific tasks with low risk, the trust of drivers will drop faster than that in high-risk tasks. This paper has explored the internal effects of the decline of driver's trust after automation errors of AVs, and further considers the influence of different external risks on these perception factors and trust. This study can help AVs manufacturers to formulate different degrees of trust repair strategies according to different driving tasks and accident severity.     

Situation awareness assessment in critical driving situations at intersections by task and human error analysis

The rapid development of sensor and tracking technology enables deployment of new Advanced Driver Assistance Systems (ADAS) that support the driver not just on highways but in urban areas as well. Intersections particularly present critical traffic scenarios where almost 35% of accidents occur, partially due to the present lack of in‐depth research about human errors and their determinants. The first step in ergonomic design of ADAS is to identify the specific situations in which drivers require support. To contribute to identification of such spots, situation awareness of 20 drivers in four critical intersection scenarios was explored. The study was conducted in the fixed‐base driving simulator. The applied approach consisted of assessing drivers' expectations and mental workload and of comparing theoretically correct cognitive behavior to experimentally collected data. Intersection scenarios were divided into five segments, and for each segment a task analysis was made. The study has shown that the driving simulator environment can be successfully deployed to provoke and explore various driver errors. The results have revealed that, in scenarios in which information is objectively missing, the majority of errors happened because the drivers had inaccurate mental models of particular scenarios. To the contrary, in the complex scenario the major cause of accidents was information overload. Furthermore, the task analysis disclosed applicable areas of intersection assistance. © 2010 Wiley Periodicals, Inc.     

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.     

Integration of task networks and cognitive user models using coloured Petri nets and its application to job design for safety and productivity

Changes of task demands due to unforeseen events and technological changes can cause variations in job design such as modifications to job procedures and task allocation. Failure to adapt to job design variations can lead to human errors that may have severe consequences for system safety. Existing techniques for task modelling cannot adequately model how task networks can be adapted to changing work conditions and task demands. Therefore, there is a need to integrate task networks with cognitive user models that indicate how operators process information, make decisions, or cope with suspended tasks and errors. The work described here presents a tool for integrating task and cognitive models using coloured Petri nets. The cognitive user model comprises two modules of attention management (selective and divided attention), a module of memory management of suspended tasks and a module of work organization. Performance Shaping Factors (e.g., workload, fatigue and mental-tracking load) are calculated at any point in time to take into account the context of work (e.g., competing activities, errors and suspended tasks). Different types of human error can be modelled for rule-based behaviours required in proceduralized work environments. Simulation analysis and formal analysis techniques can be applied to process control tasks to verify job procedures, workload management strategies and task allocation schemes in response to technological changes and unfamiliar events.     

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.     

Fuzzy hierarchical data fusion networks for terrain location identification problems.

The terrain location identification problem represents a very complicated learning task. Beside of learning from noisy and nondeterministic training data, the training task must learn from a very large size of training data, which may lead to lots of learning problems. A phenomenon called the fake convergence is observed in our implementation. In that case, the training process seemed to converge to a fixed error level, but the actual error is much higher than the converged one. In our study, a fuzzy hierarchical network is proposed to cope with the problem of large training data sets. With this fuzzy hierarchical structure, the learning process can become fast and errors are significantly reduced. Another issue is regarding about embedding domain knowledge into the learning structure of neural fuzzy networks. The idea is simple but effective. The proposed structure is called the fuzzy hierarchical data fusion network and its learning performance is significantly better than that of original fuzzy hierarchical networks. With the use of fuzzy hierarchical data fusion networks, errors indeed can converge and the system becomes practically applicable.     

Using human error information for error prevention

Developing error-free software requirements is of critical importance to the success of a software project. Problems that occur during requirements collection and specification, if not fixed early, are costly to fix later. Therefore, it is important to develop techniques that help requirements engineers detect and prevent requirements problems. As a human-centric activity, requirements engineering can be influenced by psychological research about human errors, which are the failings of human cognition during the process of planning and executinge a task. We have employed human error research to describe the types of problems that occur during requirements engineering. The goals of this research are: (1) to evaluate whether understanding human errors contributes to the prevention of errors and concomitant faults during requirements engineering and (2) to identify error prevention techniques used in industrial practice. We conducted a controlled classroom experiment to evaluate the benefits that knowledge of errors has on error prevention. We then analyzed data from two industrial surveys to identify specific prevention and mitigation approaches employed in practice. The classroom study showed that the better a requirements engineer understands human errors, the fewer errors and concomitant faults that engineer makes when developing a new requirements document. Furthermore, different types of Human Errors have different impacts on fault prevention. The industry study results identified prevention and mitigation mechanisms for each error type. Human error information is useful for fault prevention during requirements engineering. There are practices that requirements engineers can employ to prevent or mitigate specific human errors.