729 research outputs found
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Associations Between Cognitive Distortions in Moral Reasoning and Self-Reported Traffic Violations and Crashes for Different Road User Groups
No full textThe use of self-serving cognitive distortions measured by traffic-role specific versions of the Cognitive Distortions in Driving (CDD) test was explored for three Dutch road user groups: cyclists beginning to learn to drive (LDs) who were enrolled in a pro-social driving program (n=138); young novice drivers enrolled in a safety awareness program (n=1660), and; experienced professional bus drivers enrolled in a post-licensing training program (871). Associations between cognitive distortions and self-reported traffic behavior, fines and crashes were analyzed. Results show that about 20 per cent of the young novice drivers used self-serving cognitive distortions, compared to 8 per cent of the LDs and 5 per cent of the bus drivers. In addition, use of cognitive distortions was significantly correlated with speed and traffic violations. Finally, a subgroup of cyclist LDs (n=38) who had been licensed for six months used fewer cognitive distortions when tested as drivers than the licensed young novice drivers without pro-social driver training. This shows that pro-social driver training can reduce cognitive distortions and may possibly increase safety
How Long Does It Take to Relax? Observation of Driver Behavior During Real-World Conditionally Automated Driving
No full textConditionally Automated Driving (CAD) may reduce drivers’ mental load and provide the driver opportunities to engage in non-driving related tasks (NDRTs). Such systems can be expected to enter the market within the next few years and effects of automated driving need to be better understood first to maximize their potential benefit. A road-traffic study with N = 41 subjects was conducted using a Wizard-of-Oz vehicle to simulate CAD. We observed driver behavior during the initial use of CAD and set out to answer the question: How long does it take to relax? Gaze behavior, seating position, NDRT and self-reported feedback helped in identifying the phases of initial contact and familiarization. The results showed that loose seating position, glance off the road, NDRT engagement and self-reports indicate a familiarization after 10 min of total CAD and correlated with gender and previous experience with advanced driver assistance systems (ADAS). No significant connection was found between subjective and objective data
Investigating Pedestrians' Crossing Behaviour During Car Deceleration Using Wireless Head Mounted Display: An Application Towards the Evaluation of eHMI of Automated Vehicles
No full textThis study investigated pedestrians’ crossing behaviour in a virtual reality environment. One aim was to develop a framework for evaluating external Human-Machine Interfaces (eHMI) used by automated vehicles for future studies. Pedestrians were provided with a series of two approaching cars, which were travelling at either 25mph, 30mph, or 35mph, with eight manipulated time gaps in between cars, where the second car either decelerated or kept pace. These stimuli were presented in 3 blocks. Pedestrians’ task was to cross (or not) naturally between the approaching cars. Data from decelerating trials were analysed. Results showed 51% of crossings happened before deceleration, 31% of crossings after the car had stopped and only 18% of the crossings during deceleration, leaving a great margin for evaluating the effect of eHMI and changing pedestrian crossing behaviour during deceleration. A learning effect was found, demonstrating a shift of decision making across blocks, whereby crossing increasingly occurred during the approaching vehicle’s deceleration, rather than after it had come to a full stop. Further analyses were conducted to investigate the effect of speed on initiation time, crossing time and safety margin. This study provides guidelines in choosing the appropriate time gaps and speeds that may influence pedestrians’ crossing decisions and behaviour while presenting different designs of eHMI in future studies. The results also provide guidance on how to evaluate safety, efficiency/receptivity and learning effects, when comparing different eHMI designs in VR experiments
Posing Questions and Policy Suggestions: Autonomous Vehicles & Climate Change
No full textThe introduction of autonomous vehicles (AVs) is projected to increase safety and efficiency of transportation. However, climate change poses a challenge to the smooth integration of AVs to current transportation infrastructure. Increased extreme weather events, higher precipitation and temperature, and damages to infrastructure are challenges AVs will face. Therefore, the authors advocate for Planned Adaptive Regulation, and raise questions that they feel policymakers and driving assessment should consider
The Effect of a Concussion on the Hazard Anticipation Ability in Teen Drivers
No full textDriving after a brain injury is controversial. Since diagnosing a concussion and tracking the healing trend is challenging, whether or not a patient is fit-to-drive after the injury is open to interpretation. The primary purpose of the present research was to investigate the effect of a concussion on teen drivers’ hazard anticipation skill. Twenty-four participants were recruited for this study in two groups: the concussed teen driver group and the non-concussed teen driver group. They were asked to wear an eye-tracker and drive with a driving simulator. The drive included several scenarios with potential latent hazards. While driving, the participants were expected to scan the latent hazards with their eyes and fixate at the hazardous area. The results show significant differences (p < 0.05) in the hazard anticipation skills between the two groups on two primary aspects: 1). The concussed group showed more random eye movements while the non-concussed participants had more deliberate eye fixations with less distractions and saccadic jumping. 2) The concussed patients showed a significantly poorer performance in anticipating the potential hazards. In conclusion, results indicate concussions can affect the hazard prediction skills of the teens, which in turn makes the driving task riskier for this group of drivers
What You See is What You Get? Correspondence of Video and Interview Data on Secondary Task Engagement While Driving-A Naturalistic Driving Study
No full textNumerous studies use questionnaires or interviews to investigate the prevalence of secondary task engagement while driving. This data may be subject to memory distortion. This study aims at investigating the extent to which interviews are valid instruments to assess secondary tasks. Therefore, we evaluated interviews and video data allowing the observation of secondary task engagement from a Naturalistic Driving Study. We equipped the vehicles of 94 subjects with cameras filming the driver's vehicle cabin. Video and interview data were collected twice within the study period of 3 days. We then determined hit rate, misses, false alarms, correct rejections, sensitivity, as well as specificity for 15 secondary tasks. We found 594 secondary tasks in the videos. In 10% of all comparisons (Nall=2.187 for 15 tasks) the interview correctly identified task engagement (hit). In 17% of the comparisons drivers missed to report a task. In 9% of the comparisons there was a false alarm and in 64% we found correct rejections. More conscious and longlasting tasks (hands-free phoning, smoking) were remembered best. The interview method seems to be a valuable and valid tool to assess rather consciously conducted and legally prohibited secondary tasks while driving
Design and Evaluation of Adaptive Collision Avoidance Systems
No full textTaking a human factors approach, the present study aims at improving driver interaction with automation by improving driver trust in and understanding of the system and enhancing system design. First, a driving experiment was conducted to investigate how driver understanding of the system capabilities effects driver performance and trust. The experiment compared two driver assistance systems for avoiding collisions during critical lane change: one was a haptic steering control that manipulates the steering wheel friction torque, and the other was an automatic steering control that decouples the driver during critical conditions. The results indicate that, especially in critical situations when driver expectation of the system and system capabilities were not aligned, the driversystem interaction was significantly affected by the way control is allocated between agents. To improve system design in terms of functional allocation and capabilities, the study proposes an enhanced adaptive collision avoidance system in which control is allocated dynamically depending on the situation. This system was assessed in a second driving experiment. While the diver-system interactions significantly improved compared to the haptic and automatic steering control systems, in terms of safety, it did not perform as well as expected. A third experiment, using long term simulator training, was conducted to enhance drivers’ understanding of and trust in the system. The training interaction revealed that drivers adapted more easily to the system, improving driver performance, system effectiveness, and safety. The findings highlight how user training can improve human-automation interaction
Dynamics of Pedestrian Crossing Decisions Based on Vehicle Trajectories in Large-Scale Simulated and Real-World Data
No full textHumans, as both pedestrians and drivers, generally skillfully navigate traffic intersections. Despite the uncertainty, danger, and the non-verbal nature of communication commonly found in these interactions, there are surprisingly few collisions considering the total number of interactions. As the role of automation technology in vehicles grows, it becomes increasingly critical to understand the relationship between pedestrian and driver behavior: how pedestrians perceive the actions of a vehicle/driver and how pedestrians make crossing decisions. The relationship between time-to-arrival (TTA) and pedestrian gap acceptance (i.e., whether a pedestrian chooses to cross under a given window of time to cross) has been extensively investigated. However, the dynamic nature of vehicle trajectories in the context of non-verbal communication has not been systematically explored. Our work provides evidence that trajectory dynamics, such as changes in TTA, can be powerful signals in the non-verbal communication between drivers and pedestrians. Moreover, we investigate these effects in both simulated and realworld datasets, both larger than have previously been considered in literature to the best of our knowledge
Spatially Biased Eye Movements in Older Drivers with Glaucoma and Visual Field Defects
No full textPatients with glaucoma are at greater driving safety risk due to visual field defects. These driving safety risks may be mitigated by compensatory eye movements. We measured spatial allocation of eye movements in a panoramic driving simulator in 8 drivers with glaucoma and 5 with suspected glaucoma. All completed a driving simulator visual field task under three separate conditions: (1) parked with a naturalistic background (Baseline condition); (2) driving on a rural highway (Driving condition); and (3) driving and completing a competing auditory attention task (PASAT condition). Results showed that: (1) drivers with larger binocular visual field defects showed more restricted, spatially biased eye movements, and (2) greater task load led to more spatially biased eye movements in drivers with larger binocular visual field defects. Findings provide preliminary evidence of eye movement patterns that may reflect compensatory behaviors in drivers with glaucomatous visual fields. Better understanding of the relationship between visual field deficits, eye movement patterns, and driving in glaucoma can help inform countermeasures to improve safety and mobility in drivers with visual impairments
Are Driving Simulators Suitable to Measure the Driving Competence of Elderly Drivers?
No full textoai:driving:id:28330The present project aimed to develop and validate a methodology for driving simulators to assess and diagnose driving ability of elderly drivers. A driving simulation course has been developed which covered a representative selection of driving tasks of moderate difficulty as well as scenarios which are particularly difficult for elderly drivers. Driving errors were semi-automatically registered and classified by a tablet PC application. Based on the registered driving errors, the driving competence of each driver was rated on an 11-point fitness-to-drive (FtD) rating scale by specifically trained raters. The driving course was validated on the basis of a 60-minute standardized driving test in real traffic. By including similar driving tasks, it was ensured that it was structurally comparable to the simulated course. 30 elderly drivers (> 70 years) and 30 control drivers (25-50 years) were assessed in the simulation and in real traffic. During the driving tests, more driving errors were registered for the elderly drivers than for the controls both in the simulator and in real traffic. FtD-ratings and total number of driving errors during the driving tests in the simulation correlated up to r = .80 with the FtD-ratings of the driving tests in real traffic. ROC-Analyses revealed at Sensitivity-Specificity Ratio of 85.71 : 82.61 at best. Overall, driving simulation was well accepted by the subjects. The findings of the study confirm the validity of driving simulation as a tool to diagnose driving ability and argue for its introduction as a diagnostic tool