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.

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 1.5. days. 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.

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.