Transportation & Mobility

An investigation into control mechanisms of driving performance: resource depletion and effort-regulation

Driver fatigue is a complex phenomenon that has a range of causal factors including sleep-related and task-related factors. These manifest as different safety and performance outcomes. Extensive research has been applied to linking these factors to performance impairment. However, little research focuses on the mechanisms by which this link exists. This research project therefore focuses on the processes underlying how driving performance is controlled and maintained during the development on non-sleep-related driver fatigue. The main aim was to establish whether progressive impairment of driving control over a prolonged drive could be attributed to a depletion of attentional resources, as proposed by Resource Theory, or to a withdrawal of effort, as proposed by Effort-Regulation Theory. As a multi-component skill, driving requires perception, cognition and motor output. The secondary aim of this research was therefore to assess whether a prolonged drive impairs stage-specific information processing. Participants (n=24) in three experimental groups performed a 90-minute simulated drive wherein they were expected to keep the bonnet of a car on a lane (tracking task). The three groups differed in terms of lane width: small, medium and large, corresponding to low, medium, and high task-demand, respectively. To assess the impacts of this task on stage-specific information processing, participants performed a set of resource specific tests before and after the prolonged drive. Each task had two difficulty variations to ensure that performance decrement was due not only to the task-characteristic, but specifically to resource depletion. The tests probing information processing were: a modified Fitts’ tapping task for motor programming, a digit recall task for perception, and an object recognition reading task for cognition. Performance was measured as lateral deviation of the car. Physiological measures included heart rate frequency (HR) and various time- and frequency-domain heart rate variability (HRV) parameters, eye blink frequency and duration. The Borg CR-10 scale was used to evaluate subjective effort and fatigue during the task. Driving control declined over time and was supplemented by HR, HRV, blink frequency and duration, indicating an increase in parasympathetic activity (or a reduction in arousal). An increase in blink frequency was considered as a sign of withdrawal of attentional resources over time. Driving control declined to a greater extent in the large road width group and reflected a lower parasympathetic activity, whereas the inverse was observed for the small road width group. Resource tests reveal a non-specific impairment of information processing following the prolonged drive. However, this was accompanied by an increase in parasympathetic activity. Overall, results indicate that Effort-Regulation Theory better accounts for the impairment of driving control in prolonged driving than does Resource Theory. This suggests that the impact of fatigue is guided more by task goals and intrinsic motivation than by the manner in which the fatigue state developed. Moreover, performance impairment by effort-regulation is dependant more on time on task than on task-demand.

Eye Tracking Glasses

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2 versions available

D9. 3-Requirements & Specification & first Modelling for the Automotive AdCoS and HF-RTP Requirements Definition Update (Feedback)

The main objective of WP9 is the development and qualification of AdCoS in Automotive (AUT) domain using the tailored HF-RTP and methodology from WP1, to demonstrate the added value for industrial engineering processes, in terms of reduced cost, fewer necessary development cycles and better functional performances. This report describes the requirements, specifications and the first modelling for the AdCoS applications in the Automotive (AUT) domain, with reference to the target-scenarios (TSs) and the Use-cases (UCs) described in the deliverable D9.1 “Requirements Definition for the HF-RTP, Methodology and Techniques and Tools from an Automotive Perspective”. In particular, we mainly refer to the two AdCoS applications implemented on the real test-vehicles (TVs): • Adapted Assistance, that is a Lane-Change Assistant (LCA) system, led by the CRF partner. • Adapted Automation, that is an automatic Intuitive Driving (ID) system, led by the IAS partner. In addition, this report includes the results of a first attempt to model the AdCoS using the HF-RTP and methodology utilising either pre-existing tools or new tools to be developed in the frame of the HoliDes project. Section §2 contains a list of tools definitely applied from WP1-5. Section §3 describes each AdCoS use case including AdCoS operational definitions, HMI for the AdCoS, tools applied from the HF-RTP, requirements and specifications, and the system architecture. Section §4 reports on feedback from WP 1-5. Section §5 presents some conclusions and the next steps.

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Dynamic simulation and prediction of drivers’ attention distribution

The distribution of driver’s attention is a crucial aspect for safe driving. The SEEV model by Wickens is a state of the art model that provides an easy but abstract way to estimate the distribution of attention for specific situations. The present paper presents an extension of the SEEV model, the Adaptive Information Expectancy (AIE) model. The AIE model is a sophisticated model of attention control, able to provide estimates based on a far more detailed simulation of human allocation of attention within a cognitive architecture. The AIE model relates attention directly to a task model, which is executed within the architecture. It is able to automatically measure task-dependent event frequencies and adapt its distribution of attention according to these frequencies. The AIE model was used to create a dynamic cognitive driver model. A driving simulator study with 21 participants has been conducted to evaluate the predictions of the driver model. Event rates for the primary driving task and an artificial secondary task have been varied, as well as the priorization of tasks. Both the SEEV and the AIE model provided estimates for percentage dwell times with similar quality, while the AIE model was able to provide estimates for further measure like gaze frequencies and link values.

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8 versions available

Free-hand pointing for identification and interaction with distant objects

In this paper, we investigate pointing as a lightweight form of gestural interaction in cars. In a pre-study, we show the technical feasibility of reliable pointing detection with a depth camera by achieving a recognition rate of 96% in the lab. In a subsequent in-situ study, we let drivers point to objects inside and outside of the car while driving through a city. In three usage scenarios, we studied how this influenced their driving objectively, as well as subjectively. Distraction from the driving task was compensated by a regulation of driving speed and did not have a negative influence on driving behaviour. Our participants considered pointing a desirable interaction technique in comparison to current controller-based interaction and identified a number of additional promising use cases for pointing in the car.

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7 versions available

Increasing complexity of driving situations and its impact on an ADAS for anticipatory assistance for the reduction of fuel consumption

This paper presents a study of the impact of different complex situations on an advanced driver assistance system (ADAS) for anticipatory assistance for the reduction of fuel consumption. Different studies showed that it is possible to reduce the individual fuel consumption of drivers by extending the driver's anticipation horizon through an ADAS. But the influences of the driving situation on the success of such a system has not been researched yet. Therefore the driving simulator study which is presented in this paper deals with the impact of different traffic situations and its complexity on an anticipatory ADAS for fuel reduction in deceleration scenarios. For this, different rural and urban deceleration scenarios where chosen and situations of different complexity were implemented by changing traffic and environmental conditions. As the main focus of the ADAS lies on the reduction of the fuel consumption, this was one of the main variables which was measured. Additionally the glance time on the HMI was analyzed as an indicator for the manner how the system was used. The results showed that the degree of complexity of the chosen road traffic situations has generally no impact on the fuel consumption if it was driven without any assistance system. The glance times on the HMI of the ADAS shorten if a situation is more complex. But this does not lead to differences in the reduction of the fuel consumptions by the ADAS in different complex situations. The overall fuel consumption was reduced by about 10%. These results lead to the assumption that a good designed anticipatory ADAS reduces the driver related fuel consumption independently from the degree of the complexity of a situation.

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3 versions available

Integrated simulation of attention distribution and driving behavior

A suitable distribution of attention to task demands is an essential component for efficient handling of multitasking situations. In most cases humans are not consciously aware of how they allocate attention to tasks. Yet they automatically weight their distribution to properties of the task like task value or the frequency of information events for a specific task. The Adaptive Information Expectancy (AIE) model was developed as a dynamic model of attention allocation and integrated into a cognitive architecture. It automatically derives the rate of information events for a task based on the interaction of a formal task model with the environment. The attention of the model is distributed according to these event rates and task priorities. Previous studies demonstrated that a dynamic driver model which uses the AIE model could reproduce many key characteristics of visual attention. In this paper it is shown, how changes in attention distribution are reflected in the task performance of the driver model for the three tasks of (1) keeping the car in the center of the lane, (2) keeping the speed close to 100 km/h and (3) solving a continuous in-vehicle secondary task. Driver model performance is compared to experimental data from a study on human drivers. Shortcomings of the driver model are discussed based on this comparison.

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1 version available:

The influence of a driving task on movement times of goal directed hand-arm movements

A model based approach for the prediction of interaction times is needed for the evaluation of display and control concepts during the early virtual phase of the vehicle design process. The main objective of the described experiment is to assess the influence of a driving task on movement times of goal directed hand-arm movements. Another objective was to create a dataset to empirically model motion times and to support the adaptability of Fitts’ law for goal directed hand-arm movements in vehicle conditions. The experiments were conducted with 31 subjects in a fixed based driving simulator. In Scenario A the subjects performed a pointing only task, and in Scenario B, a driving task and the pointing task were performed together. Motions were captured using a VICON MX system. The variables scenario F(1, 27)=14.25, p<.001 and target distance F(4.97, 134.21)=174.962, p=.001 had a significant influence on movement time. Movements were performed 6% faster in Scenario B than in Scenario A. A stepwise linear regression showed that the distance from the origin of the movement to the target is the best predictor for the calculation of movement time.

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3 versions available

Towards the automated recognition of assistance need for drivers with impaired visual field

Mobility enabled through driving is a crucial aspect of today’s social lives. It concerns young and elderly people and is critical for those among us suffering from visual field defects. Since driving primarily involves visual input, such people are often considered as unsafe drivers and banned from driving, although several recent studies, including our own, provide evidence that even severe visual field defects can be compensated through effective visual search strategies. In this context, this work pursues the challenging vision of adaptive driving assistance systems that take the visual deficits of the driver into account to enable a safer driving experience. The main challenges towards this vision are: (1) individual analysis and detection of visual field defects, (2) online analysis of visual search behavior, and (3) integrated analysis of visual deficits, search behavior, and traffic objects to identify and draw the driver’s attention towards potential hazards. Each of the above challenges is approached by customized methods. For (1), a mobile method for the assessment of the visual field and an algorithm for the recognition of the type of the visual field defect are proposed. For (2), an online probabilistic method is combined with algebraic analysis of the driver’s gaze. For (3), a detailed analysis of the driving scene is combined with the above methods to reliably detect hazardous traffic objects that might be overlooked by the driver. The methods were evaluated on real-world data from driving experiments with patients suffering from visual field defects. In combination, they improve over state-of-the-art techniques by being flexible, adaptive, and reliable. The feasibility of detecting objects that might be overlooked by the driver, and thus an adaptive assistance need, is demonstrated in different user studies. The methods developed in this work have a broad applicability that reaches beyond the driving context. Their application to a variety of tasks involving visual perception might help better understand its underlying mechanisms. Some of these tasks are already being investigated and will also be presented in this thesis.

Eye Tracking Glasses

Software

4 versions available

Traffic Light Assistant–Can take my eyes off of you

A traffic light assistant on a smart phone is assessed with an eye tracker in real traffic. The system is displayed on three different screen sizes on a nomadic device and gaze durations are measured. Another condition of the experiment includes an acoustic click when the display content changes to reduce glance frequency. The acoustic click as an auditory hint does not reduce the frequency of gazes, as expected. The gaze durations can get shorter as display size increases, but this does not necessarily reduce the percentage of time an in-vehicle information system (IVIS) is looked at. Subjective ratings indicate that display contents can even be shown too big. Overall, the gaze durations are in line with current limits, even when displayed on a small screen. A set of gaze histograms and calculated gaze metrics is provided to enable comparison with other experiments and IVIS.

Eye Tracking Glasses

Software

4 versions available

Comparison of in-car touchpads with adaptive haptic feedback

Two in-car touchpads with adaptive haptic feedback are specified in the context of this contribution. These innovative control elements enable an easy and intuitive handling of modern car infotainment systems. The current paper presents the results of a field experiment comparing a touchpad with realistic haptic feedback via sensible and operable elements to a touchpad with haptic feedback via vibration of the touchpad surface in a real driving situation.

Eye Tracking Glasses

Simulator

2 versions available

Publication Hub Archive

An investigation into control mechanisms of driving performance: resource depletion and effort-regulation

D9. 3-Requirements & Specification & first Modelling for the Automotive AdCoS and HF-RTP Requirements Definition Update (Feedback)

Dynamic simulation and prediction of drivers’ attention distribution

Free-hand pointing for identification and interaction with distant objects

Increasing complexity of driving situations and its impact on an ADAS for anticipatory assistance for the reduction of fuel consumption

Integrated simulation of attention distribution and driving behavior

The influence of a driving task on movement times of goal directed hand-arm movements

Towards the automated recognition of assistance need for drivers with impaired visual field

Traffic Light Assistant–Can take my eyes off of you

Comparison of in-car touchpads with adaptive haptic feedback