Chris Dijksterhuis

The influence of music on mood and performance while driving

Mood can influence our everyday behaviour and people often seek to reinforce, or to alter their mood, for example by turning on music. Music listening while driving is a popular activity. However, little is known about the impact of music listening while driving on physiological state and driving performance. In the present experiment, it was investigated whether individually selected music can induce mood and maintain moods during a simulated drive. In addition, effects of positive, negative, and no music on driving behaviour and physiological measures were assessed for normal and high cognitive demanding rides. Subjective mood ratings indicated that music successfully maintained mood while driving. Narrow lane width drives increased task demand as shown in effort ratings and increased swerving. Furthermore, respiration rate was lower during music listening compared to rides without music, while no effects of music were found on heart rate. Overall, the current study demonstrates that music listening in car influences the experienced mood while driving, which in turn can impact driving behaviour. Practitioners Summary: Even though it is a popular activity, little is known about the impact of music while driving on physiological state and performance. We examined whether music can induce moods during high and low simulated drives. The current study demonstrates that in car music listening influences mood which in turn can impact driving behaviour. The current study shows that listening to music can positively impact mood while driving, which can be used to affect state and safe behaviour. Additionally, driving performance in high demand situations is not negatively affected by music.

Effects of steering demand on lane keeping behaviour, self-reports, and physiology. A simulator study.

In this study a driving simulator was used to determine changes in mental effort in response to manipulations of steering demand. Changes in mental effort were assessed by using subjective effort ratings, physiology, and the standard deviation of the lateral position. Steering demand was increased by exposure to narrow lane widths and high density oncoming traffic while speed was fixed in all conditions to prevent a compensatory reaction. Results indicated that both steering demand factors influence mental effort expenditure and using multiple measures contributes to effort assessment. Application of these outcomes for adaptive automation is envisaged.

Merging into heavy motorway traffic by young and elderly drivers

An increase in the number of Heavy Goods Vehicles on motorways may lead to additional problems in the interaction with an increased number of elderly drivers. Elderly drivers suffer from reduced information processing speed and capacity, and in general effectively compensate for this by taking more time. However, this strategy, regulating task demands by slowing down will make merging into motorway traffic actually more difficult. In an experiment performed in a driving simulator, young and elderly drivers merged into motorway traffic. Driver behaviour and mental workload were studied while the following factors were manipulated: type of traffic and density of Heavy Goods Vehicles on the main road, the length of the acceleration lane, presence of a slowly driving lead car, and presence of a driver support system that encouraged the drivers to speed up if their speed was too low. Results show that the effects of an increased number of Heavy Goods Vehicles on the main road were not more adverse for elderly than for the young participants, with the exception that elderly drivers merged at a lower speed. This lower speed could make the manoeuvre more risky in real traffic. The support system and an extended acceleration lane facilitated merging, while a slowly driving lead car impeded completion of the manoeuvre.

Cardiovascular state changes during performance of a simulated ambulance dispatchers' task: potential use for adaptive support

Adaptive support has the potential to keep the operator optimally motivated, involved, and able to perform a task. In order to use such support, the operators state has to be determined from physiological parameters and task performance measures. In an environment where the task of an ambulance dispatcher was simulated, two studies have been carried out to evaluate the feasibility of using cardiovascular measures for adaptive support. During performance of this 2-3h lasting planning task, a pattern of results is found that can be characterized by an initial increase of blood pressure and heart rate and a decrease of heart rate variability (defense reaction pattern) followed by an ongoing increase of blood pressure counteracted by a decrease in heart rate. This pattern can be explained by an augmented short-term blood pressure control (baroreflex), which is reflected in an increase of baroreflex sensitivity. Additionally, in this latter phase heart rate variability (HRV) increases as a function of time, while blood pressure variability decreases. In the two studies performed, the baroreflex pattern was consistent for all the relevant variables. In both studies there were periods with high and low workload. Effects of task load are mainly reflected in the variability measures, while in the second study, additionally, blood pressure level was higher during periods with high task demands. The conclusion of the studies is that consistent cardiovascular response patterns can be recognized during this semi-realistic planning task, where variability measures are most sensitive to task demand changes, while blood pressure and baroreflex sensitivity are most informative with respect to cardiovascular state changes. These findings can be seen as a great potential benefit for future use in adaptive support applications.

Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns

A passive Brain Computer Interface (BCI) is a system that responds to the spontaneously produced brain activity of its user and could be used to develop interactive task support. A human-machine system that could benefit from brain-based task support is the driver-car interaction system. To investigate the feasibility of such a system to detect changes in visuomotor workload, 34 drivers were exposed to several levels of driving demand in a driving simulator. Driving demand was manipulated by varying driving speed and by asking the drivers to comply to individually set lane keeping performance targets. Differences in the individual drivers workload levels were classified by applying the Common Spatial Pattern (CSP) and Fishers linear discriminant analysis to frequency filtered electroencephalogram (EEG) data during an off line classification study. Several frequency ranges, EEG cap configurations, and condition pairs were explored. It was found that classifications were most accurate when based on high frequencies, larger electrode sets, and the frontal electrodes. Depending on these factors, classification accuracies across participants reached about 95% on average. The association between high accuracies and high frequencies suggests that part of the underlying information did not originate directly from neuronal activity. Nonetheless, average classification accuracies up to 75–80% were obtained from the lower EEG ranges that are likely to reflect neuronal activity. For a system designer, this implies that a passive BCI system may use several frequency ranges for workload classifications.

An Adaptive Driver Support System: User Experiences and Driving Performance in a Simulator

Objective: The aim of this study was to test the implementation of an adaptive driver support system. Background: Providing support might not always be desirable from a safety perspective, as support may lead to problems related to a human operator being out of the loop. In contrast, adaptive support systems are designed to keep the operator in the loop as much as possible by providing support only when necessary. Method: A total of 31 experienced drivers were exposed to three modes of lane-keeping support: nonadaptive, adaptive, and no support. Support involved continuously updated lateral position feedback shown on a head-up display. When adaptive, support was triggered by performance-based indications of effort investment. Narrowing lane width and increasing density of oncoming traffic served to increase steering demand, and speed was fixed in all conditions to prevent any compensatory speed reactions. Results: Participants preferred the adaptive support mode mainly as a warning signal and tended to ignore nonadaptive feedback. Furthermore, driving behavior was improved by adaptive support in that participants drove more centrally, displayed less lateral variation and drove less outside the lane’s delineation when support was in the adaptive mode compared with both the no-support mode and the nonadaptive support mode. Conclusion: A human operator is likely to use machine-triggered adaptations as an indication that thresholds have been passed, regardless of the support that is initiated. Therefore supporting only the sensory processing stage of the human information processing system with adaptive automation may not feasible. Application: These conclusions are relevant for designing adaptive driver support systems.

Short-Term Cardiovascular Responses to Changing Task Demands

When measuring operator states the predictive power of cardiovascular and respiratory measures in relation to mental workload has been questioned. One of the main questions is to what extent do cardiovascular measures actually reflect mental workload. This question arises because good measures of mental workload should be sensitive to changes in mental effort alone and not to other influences or at least the changes associated with mental workload should be easy to isolate. In the case of cardiovascular measures, the physiological change brought on by the baroreflex is a compensatory control effect that can potentially overshadow changes in physiology due to mental effort and therefore reduce the usefulness of cardiovascular measures. However, this does not need to be the case. Despite the effects caused by the baroreflex differences in heart rate, heart rate variability and other cardiovascular measures associated with task related effort can still be found using short-term response patterns. The short-segment analysis approach described in this paper is based on a time-frequency method in which the spectral power of the cardiovascular measures in specified spectral bands is computed from small time segments, i.e. 30 s. To demonstrate the effectiveness of this technique two studies which made use of a simulation of an ambulance dispatchers task are described, both with easy and difficult task conditions. A short-lasting increase in task demand was found to be reflected in short-lasting increases in heart rate and blood pressure in combination with corresponding decreases in heart rate variability and blood pressure variability. These effects were larger in easy task conditions than in hard conditions, likely due to a higher overall effort-level during the hard task conditions. However, the developed measures are still very sensitive to mental effort and if this brief segmentation approach is used cardiovascular measures show promise as good candidates for reflecting mental effort during the assessment of operator state.

The impact of immediate or delayed feedback on driving behaviour in a simulated Pay-As-You-Drive system.

Pay-As-You-Drive (PAYD) insurance links an individuals driving behaviour to the insurance fee that they pay, making car insurance more actuarially accurate. The best known PAYD insurance format is purely mileage based and is estimated to reduce accidents by about 15% (Litman, 2011). However, these benefits could be further enhanced by incorporating a wider range of driving behaviours, such as lateral and longitudinal accelerations and speeding behaviour, thereby stimulating not only a safe but also an eco-friendly driving style. Currently, feedback on rewards and driver behaviour is mostly provided through a web-based interface, which is presented temporally separated from driving. However, providing immediate feedback within the vehicle itself could elicit more effect. To investigate this hypothesis, two groups of 20 participants drove with a behavioural based PAYD system in a driving simulator and were provided with either delayed feedback through a website, or immediate feedback through an in-car interface, allowing them to earn up to €6 extra. To be clear, every participant in the web group did actually view their feedback during the one week between sessions. Results indicate clear driving behaviour improvements for both PAYD groups as compared to baseline rides and an equal sized control group. After both PAYD groups had received feedback, the initial advantage of the in-car group was reduced substantially. Taken together with usability ratings and driving behaviours in specific situations these results show a moderate advantage of using immediate in-car feedback. However, the study also showed that under conditions of feedback certainty, the effectiveness of delayed feedback approaches that of immediate feedback as compared to a naïve control group.

In-car usage-based insurance feedback strategies. A comparative driving simulator study

Abstract Usage-Based Insurances (UBI) enable policyholders to actively reduce the impact of vehicle insurance costs by adopting a safer and more eco-friendly driving style. UBI is especially relevant for younger drivers, who are a high-risk population. The effectiveness of UBI should be enhanced by providing in-car feedback optimised for individual drivers. Thirty young novice drivers were therefore invited to complete six experimental drives with an in-car interface that provided real-time information on rewards gained, their driving behaviour and the speed limit. Reward size was either displayed directly in euro, indirectly as a relatively large amount of credits, or as a percentage of the maximum available bonus. Also, interfaces were investigated that provided partial information to reduce the potential for driver distraction. Compared to a control no-UBI condition, behaviour improved similarly across interfaces, suggesting that interface personalisation after an initial familiarisation period could be feasible without compromising feedback effectiveness. Practitioner Summary: User experiences and effects on driving behaviour of six in-car interfaces were compared. The interface provided information on driving behaviour and rewards in a UBI setting. Results suggest that some personalisation of interfaces may be an option after an initial familiarisation period as driving behaviour improved similarly across interfaces.

Adaptive Task Support Based on Dynamic Human State Estimation

Adaptive systems that provide task support to the human operator when needed can be a solution to the problems of traditional automation in complex dynamic systems. One way of initiating support is automatically: let the technical system assess operator functional state based on continuous monitored physiological, performance, and environmental factors and let it decide whether support is needed. For this concept to work in practice several human-related problems need to be solved. This is the focus of the REFLECT project which aims at developing adaptive systems that really support the human operator. Some of the accumulated knowledge gained in this and preceding projects are addressed in this paper. In addition, a design cycle for adaptive systems is presented that provides a functional overview of the design issues to be solved.