729 research outputs found
Sort by
Assessing Moral Reasoning, Cognitive Distortions and Driving Style in the Context of Post-License Young Driver Coaching
No full textAs part of the Dutch post-license young driver coaching program, Drive Xperience (DX), the level of moral reasoning was explored in relation to self-reported violating driving behaviors. Drawing from literature in the field of juvenile crime, three online assessments were developed to measure: a) social driving behavior; b) moral justification for rule compliance, and: c) cognitive distortions in relation to socially undesirable driving behavior. The assessments were administered between fall 2014 and fall 2016 to1660 participants in the DXprogram. The results show that immature levels of moral reasoning and prevalence of cognitive distortions are strongly associated with self-reported speed choice, space competition and traffic law violations
Driving Performance and Driver State in Obstructive Sleep Apnea: What Changes with Positive Airway Pressure?
No full textWe evaluated naturalistic driving in 65 drivers with obstructive sleep apnea (OSA) before and after positive airway pressure (PAP) therapy and in 43 comparison drivers. Driving performance metrics included speed (mean, variability), and lateral, and longitudinal acceleration (g’s). Driver state measures included sleepiness and attention to the driving task based on sampled trigger and baseline video clips. OSA drivers showed less variability in speed and lateral g’s compared to control drivers before and after PAP treatment when vehicle speed wa
Can Information About an Approaching Bicycle’s Characteristics Influence Drivers’ Gap Acceptance and TTA Estimates?
No full textE-bikes, which have the potential to reach higher speed levels than conventional bicycles, but look basically the same, are suspected to be at a higher crash risk than such conventional bicycles. Other road users might misjudge the time remaining before the approaching bicycle arrives (time to arrival, TTA) and accept unsafe gaps (e.g. for turning manoeuvres) as a result of this combination of higher speed and well-known looks. Researchers have therefore suggested to make drivers aware of the higher speed of e-bikes, and give e-bikes a distinct appearance. Goal of this experiment was to investigate the effects of such a unique appearance, coupled with clear instructions about the capabilities of ebikes, on gap acceptance and TTA estimates. Participants were presented with video sequences of approaching cyclists clearly identifiable as either riding a conventional bicycle or e-bike, and were required to either indicate the smallest acceptable gap for a left turn in front of the cyclist, or to estimate TTA in two different experimental blocks. The results showed no difference in accepted gap size between the two appearances of the cyclist, whereas there was a minor effect on TTA estimates. Overall, the results imply that simply informing other road users about e-bikes (in conjunction with a re-design that gives them a unique appearance), might not be sufficient to elicit a more conservative behavior
Time to Arrival Estimates, (Pedestrian) Gap Acceptance and the Size Arrival Effect
No full textVarious studies have found that road users’ acceptance of gaps to cross in front of another vehicle is dependent on the approaching vehicle’s size, with smaller accepted gaps in front of smaller vehicles. At the same time, the so called size arrival effect is well known from research on time to collision / time to arrival estimates, where larger objects / vehicles tend to be judged as arriving earlier than smaller objects / vehicles. However, so far there has been no attempt to connect these two approaches in a single experiment to investigate whether the size arrival effect that is prevalent in time to arrival estimates can explain the variations in gap acceptance. In this experiment, twenty-seven participants observed video clips of approaching virtual vehicles of varying size (truck, bus, van, two different cars and a motorcycle) from a pedestrian’s perspective, and were either required to indicate a crossing decision, or to estimate time to arrival. While, overall, the effect of vehicle size was clearly visible for both crossing decision and time to arrival estimates, there was also a clear exception in form of the motorcycle, which went with larger accepted gaps than some of the larger vehicles. This exception might be explained by the participants’ subjective rating of perceived threat, which was rather high for the motorcycle. As (with the exception of the motorcycle), vehicle size and perceived threat correlated substantially, it is unclear at this stage to what degree these two factors contribute to perceived time to arrival and crossing decisions
Am I Driving or Are You or Are We Both? A Taxonomy for Handover and Handback in Automated Driving
No full textIn this paper, a taxonomy of handover and handback (i.e., from manual to automatic control and vice versa) is proposed to be used by practitioners and researchers to help assure the duration of those periods are clearly defined, and accordingly, studies examining them are comparable and have repeatable results. Furthermore, use of this framework will help assure that those implementing automation will do so in a comprehensive manner. The taxonomy is more detailed than that in SAE Standard J3114. Handover includes the phases preparation, perception (of the handover signal), suspension (of in-vehicle tasks) and the actual process of taking over, which can be subdivided into sufficient (to steer and control speed) and full (where situation awareness is complete) control. Furthermore, handover can be imminent, scheduled, or user-initiated. For handback, the phases are initialization, the actual handback, and re-engagement (of the driver). Handback may be optional or mandatory and user- or system initiated. For both handover and handback processes, the duration and change of the control transfer (as a function of time) needs to be precisely described/specified
Considering Self-Report in the Interpretation of Objective Performance Data in the Comparison of HMI Systems
No full textDriver interaction with two production voice-command interfaces representing differing user interface design approaches were compared under onroad highway driving conditions. A sample of 80 drivers was randomly assigned to drive each vehicle (40 per vehicle). During voice-based phone contact calling and destination address entry, participants in one vehicle showed, on average, statistically significant “better” performance in terms of task completion time, mean glance duration, total off-road glance time, and total number of glances. However, these objective measures do not fully characterize the overall experience of participants. An analysis of error rates and subjective report of attitudes, effects on driving behavior, and behavioral intentions relative to their exposure to the two systems provided important, complementary and sometimes contrasting data about the relative advantages of each implementation
Evaluation of a Training Intervention to Improve Novice Drivers’ Hazard Mitigation Behavior on Curves
No full textNewly licensed teenage drivers experience a higher risk of crashing compared to other age cohorts. Literature reveals that novice drivers exhibit poor hazard mitigation skills. The current study assesses the effectiveness of a training program at improving novice divers’ hazard mitigation and speed selection behaviors on curves. In this study, drivers are randomly assigned to two training cohorts (ACT and placebo), and were exposed to 2 different scenarios of interest, one scenario contained a moderate curve left and the other included a tightening curve right. ACT trained drivers made more glances to the far extent of the curve, than the placebo-trained drivers. ACT (Anticipate, Control, and Terminate) trained drivers were also significantly more likely to slow to the target speed before the curve, when compared to the placebo trained drivers. The results indicate the effectiveness of ACT as a countermeasure, at training novice drivers to select better glancing and speed management strategies
How Common In-Car Distractions Affect Driving Performance in Simple and Complex Road Environments
No full textDistracted driving (driving while performing a secondary task) is the cause of many collisions. Although the research has stressed the deleterious effects of distraction, there may be situations where distraction improves driving performance. Boredom is associated with collision risk, and it is possible that some types of secondary task may combat boredom on simple monotonous drives. In this study, licensed drivers were tested in a driving simulator (a car body surrounded by screens) that simulated simple or complex roads. Road complexity was manipulated by increasing traffic, scenery, and the number of curves in the drive. Participants either drove (single task), or they drove while listening to an audiobook or having a hands-free cellular phone conversation. Driving performance was measured in terms of speed, standard deviation of speed, standard deviation of lateral position (SDLP), and hazard response times. Task condition and road complexity had no significant effect on driving speed or standard deviation of speed. There was a trend to greater SDLP on the simple drives, where there was little oncoming traffic, though this was only statistically significant in the Audiobook condition. However, there was also evidence that audiobooks could be beneficial. On simple roads, drivers listening to audiobooks had significantly faster hazard response times that those that were driving (single task) or driving while having a hands-free conversation, though this pattern of response was not evident on complex drives. These results suggest that audiobooks could play a role in helping drivers stay focused on monotonous drives
Adjusted Crash Odds Ratio Estimates of Driver Behavior Errors: A Re-Analysis of the SHRP2 Naturalistic Driving Study Data
No full textDingus and colleagues recently estimated crash odds ratios (ORs) for “driver behavior errors” (hereafter, “Behaviors”) in the Strategic Highway Research Program Phase 2 naturalistic driving study. Behaviors are illegal, improper, aggressive, and/or reckless driving maneuvers. For example, the Dingus study OR estimate for “Speeding over limit and too fast for conditions,” (hereafter, “Speeding”), was 12.8, with a 95% confidence interval (CI) from 10.1 to 16.2. The current study identified four issues in the Dingus study. First, heterogeneous Behaviors were pooled; e.g., “Exceeded speed limit,” and “Exceeded safe speed but not speed limit” were apparently improperly pooled to form Speeding. Second, exposed drivers often had other Behaviors in the same time window, but unexposed drivers had none, a selection bias that inflated Behavior ORs by 30%. Third, impairments were not filtered out. Fourth, secondary tasks were not filtered out, creating a confounding bias that deflated Behavior OR estimates by 50%. To correct these issues, the current study stratified the heterogeneous categories, then filtered out other Behaviors, impairments, and secondary tasks. “Pure Behavior” (no other Behaviors, secondary tasks, or impairments) was thus compared to “Pure Driving” (no Behaviors, secondary tasks, or impairments). The Pure OR estimate for “Exceeded speed limit” was 5.4 (CI 2.7-10.1), and for “Exceeded safe speed but not speed limit” was 71.5 (CI 36.0-136.2), both substantially different than the Dingus study Speeding estimate. All Behavior OR estimates in the Dingus study should be similarly corrected and adjusted to improve their validity
Modeling of Stimulus-Response Secondary Tasks with Different Modalities while Driving in a Computational Cognitive Architecture
No full textThis paper introduces a computational human performance model based upon the queueing network cognitive architecture to predict driver’s eye glances and workload for four stimulus-response secondary tasks (i.e., auditorymanual, auditory-speech, visual-manual, and visual-speech types) while driving. The model was evaluated with the empirical data from 24 subjects, and the percentage of eyes-off-road time and driver workload generated by the model were similar to the human subject data. Future studies aim to extend the types of voice announcements/commands to enable Human-Machine-Interface (HMI) evaluations with a wider range of usability test for in-vehicle infotainment system developments