Ben Mulder

MEASURING DRIVING PERFORMANCE BY CAR-FOLLOWING IN TRAFFIC

The measurement of impairing effects on driving performance by such external factors as alcohol, medicinal drugs, or mobile telephoning, etc., is extended with a new test. Most existing methods of measuring impairing effects in the actual driving environment have the drawback that, irrespective of high sensitivity, they measure driving skills that are involved in only a very low percentage of accident causes, i.e., accidents after motor-response or eye-hand co-ordination errors. Since in accident causation, attention and perception errors predominate over response errors, on-road studies should examine specifically deterioration in attention and perception. The ability to follow a car in front, as measured by coherence and reaction time to speed variations, offers such a measure of attention and perception performance.

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.

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.

Cardiovascular state changes in simulated work environments

The usefulness of cardiovascular measures as indicators of changes in cognitive workload has been addressed in several studies. In this paper the question is explored whether cardiovascular patterns in heart rate, blood pressure, baroreflex sensitivity and HRV that are found are consistent within and between two simulated working environments. Two studies, were performed, both with 21 participants: one in an ambulance dispatch simulation and one in a driving simulator. In the ambulance dispatcher task an initial strong increase in blood pressure is followed by a moderate on-going increase in blood pressure during the next hour of task performance. This pattern is accompanied by a strong increase in baroreflex sensitivity while heart rate decreases. In the driving simulator study, blood pressure initially increases but decreases almost to baseline level in the next hour. This pattern is accompanied by a decrease in baroreflex sensitivity, while heart rate decreases. Results of both studies are interpreted in terms of autonomic control (related to both sympathetic and para-sympathetic effects), using a simplified simulation of a baroreflex regulation model. Interpretation of the results leads to the conclusion that the cardiovascular response patterns in both tasks are a combination of an initial defensive reaction, in combination with compensatory blood pressure control. The level of compensatory blood pressure control, however, is quite different for the two tasks. This helps to understand the differences in response patterns between the two studies in this paper and may be helpful as well for understanding differences in cardiovascular response patterns in general. A substantial part of the effects observed during task performance are regulatory effects and are not always directly related to workload manipulations. Making this distinction may also contribute to the understanding of differences in cardiovascular response patterns during cognitive workload.

National culture moderates the influence of mental effort on subjective and cardiovascular measures

Subjective measures of mental effort have been shown to be relatively insensitive in Indonesian participants. An open question is whether this insensitivity reflects how mental effort is experienced or how it is reported. We compared the performance, subjective workload ratings, heart rate and heart-rate variability (HRV) of 31 Dutch and 30 Indonesian participants under single- and dual-task conditions. Indonesians performed faster but less accurately and used a narrower range of subjective workload ratings than did the Dutch. Dutch participants showed a decrease in HRV both in the mid-frequency (MF) and high-frequency bands and an increase in heart rate during task performance compared with the resting period. Indonesians showed this pattern in the MF band only. The decrease of HRV in the MF band in both groups suggests that the relative insensitivity of subjective mental effort scales among Indonesians has to do with how workload is reported rather than with how it is experienced. Practitioner summary: The sensitivity of the subjective measures of mental workload has been shown to depend on culture. Here, we show that heart-rate variability reacts similarly to workload in Eastern as in Western participants. This suggests that culture influences more how invested mental effort is reported than how it is experienced psychophysiologically.

Artefact-free real-time computation of cardiovascular measures

Cardiovascular measures can be used as indices of physiological and affective state. The theoretical and practical issues encountered in the sequence of data-acquisition, artefact handling and data (pre-) processing are described in this paper. The results of these processes are used to formulate suggestions how to develop an operator status model, a generic system for operator assessment.

Using Physiological Measures For Task Adaptation

Current Man Machine Interfaces (MMI) present information to the operator when it becomes available and when it is convenient to the computer; such automated systems do not act as a ‘team player’. Computers lack insight in the actual status, intentions and occupations of the operator. An approach is presented (COMPANION) in which better co-operation between user and computer is stimulated. In this approach, information about the users’ (physiological) state is used to adapt the MMI of the task at moments or in time periods where this is helpful for optimal task performance. Also task performance measures can be used in this feedback structure. COMPANION is about improving the Human Machine relation by: not interrupting the operator with non-urgent messages when he or she is completing an important task, and supporting the operator when task demands are high. In order to prevent specific solutions for each new task or for each additional information source an Operator Status Model (OSM) has been developed.