The effect of steering-system linearity, simulator motion, and truck driving experience on steering of an articulated tractor-semitrailer combination.

Steering systems of trucks consist of many linkages, which introduce nonlinearities that may negatively affect steering performance. Nowadays, it is possible to equip steering systems with actuators that provide artificial steering characteristics. However, before new steering systems are deployed in real vehicles, evaluation in a safe and controlled simulator environment is recommended. A much-debated question is whether experiments need to be performed in a motion-base simulator or whether a fixed-base simulator suffices. Furthermore, it is unknown whether simulator-based tests can be validly conducted with a convenience sample of university participants who have not driven a truck before. We investigated the effect of steering characteristic (i.e., nonlinear vs. linear) on drivers' subjective opinions about the ride and the steering system, and on their objective driving performance in an articulated tractor-semitrailer combination. Thirty-two participants (12 truck drivers and 20 university drivers) each completed eight 5.5-min drives in which the simulator's motion system was either turned on or off and the steering model either resembled a linear (i.e., artificial) or nonlinear (i.e., realistic) system. Per drive, participants performed a lane-keeping task, merged onto the highway, and completed four overtaking manoeuvers. Results showed that the linear steering system yielded less subjective and objective steering effort, and better lane-keeping performance, than the nonlinear system. Consistent with prior research, participants drove a wider path through curves when motion was on compared to when motion was off. Truck drivers exhibited higher steering activity than university drivers, but there were no significant differences between the two groups in lane keeping performance and steering effort. We conclude that for future truck steering systems, a linear system may be valuable for improving performance. Furthermore, the results suggest that on-centre evaluations of steering systems do not require a motion base, and should not be performed using a convenience sample of university students.

Validity and reliability of naturalistic driving scene categorization Judgments from crowdsourcing.

A common challenge with processing naturalistic driving data is that humans may need to categorize great volumes of recorded visual information. By means of the online platform CrowdFlower, we investigated the potential of crowdsourcing to categorize driving scene features (i.e., presence of other road users, straight road segments, etc.) at greater scale than a single person or a small team of researchers would be capable of. In total, 200 workers from 46 different countries participated in 1.5days. Validity and reliability were examined, both with and without embedding researcher generated control questions via the CrowdFlower mechanism known as Gold Test Questions (GTQs). By employing GTQs, we found significantly more valid (accurate) and reliable (consistent) identification of driving scene items from external workers. Specifically, at a small scale CrowdFlower Job of 48 three-second video segments, an accuracy (i.e., relative to the ratings of a confederate researcher) of 91% on items was found with GTQs compared to 78% without. A difference in bias was found, where without GTQs, external workers returned more false positives than with GTQs. At a larger scale CrowdFlower Job making exclusive use of GTQs, 12,862 three-second video segments were released for annotation. Infeasible (and self-defeating) to check the accuracy of each at this scale, a random subset of 1012 categorizations was validated and returned similar levels of accuracy (95%). In the small scale Job, where full video segments were repeated in triplicate, the percentage of unanimous agreement on the items was found significantly more consistent when using GTQs (90%) than without them (65%). Additionally, in the larger scale Job (where a single second of a video segment was overlapped by ratings of three sequentially neighboring segments), a mean unanimity of 94% was obtained with validated-as-correct ratings and 91% with non-validated ratings. Because the video segments overlapped in full for the small scale Job, and in part for the larger scale Job, it should be noted that such reliability reported here may not be directly comparable. Nonetheless, such results are both indicative of high levels of obtained rating reliability. Overall, our results provide compelling evidence for CrowdFlower, via use of GTQs, being able to yield more accurate and consistent crowdsourced categorizations of naturalistic driving scene contents than when used without such a control mechanism. Such annotations in such short periods of time present a potentially powerful resource in driving research and driving automation development.

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

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.

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 individual's 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.

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 driver's workload levels were classified by applying the Common Spatial Pattern (CSP) and Fisher's 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.

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.