How are distractibility and hazard prediction in driving related? Role of driving experience as moderating factor

Distraction constitute one of the 'five fatal' behaviours that contribute to road trauma, and some people may be more susceptible to it than others. It is also known that a greater ability to predict danger is related to a lower probability of suffering accidents. It could be hypothesised that drivers with a higher tendency to distraction are worse at predicting traffic hazards, but to what extent might driving experience serve to mitigate this tendency to distraction? The current study collected self-reported attentional errors from drivers by using the Attention-Related Driving Errors Scale (ARDES-Spain) in order to examine whether novice drivers suffered from inattention more than experienced drivers. The results demonstrated that novice drivers scored more highly on ARDES than experienced drivers. ARDES scores were then related to performance in a Hazard Prediction test, where participants had to report what hazard was about to happen in a series of video clips that occlude just as the hazard begins to develop. While experienced drivers were better at the Hazard Prediction test than novice drivers, those participants who reported fewer attention errors were also better able to detect the upcoming hazard following occlusion. In addition, our results demonstrate a relationship between self-reported attentional errors and the ability to predict upcoming hazards on the road, with driving experience having a moderating role. In the case of novice drivers, as their scores in the Manoeuvring Errors ARDES factor increase, their ability in Hazard Prediction diminishes, while for experienced drivers the increase is not significant. Guidance on how to improve training for drivers in order to mitigate the effects of inattention on driving safety can be addressed.

Drive aware training: A computerized training program for older drivers' detection of road hazards

OBJECTIVE

Cognitive training for reducing crash rate can be delivered to older drivers via in-person on-road sessions, a driving simulator, or using computer-based cognitive methods. Despite established effectiveness, in-person on-road assessment and rehabilitation are expensive, and limited professional service may not be readily accessible; simulator-based training may not fit every driver due to simulator sickness. In comparison, computerized training is easier to implement and could be delivered with little cost to older drivers with computer access. Based on the Drive Aware Task, a validated measure of attentional processes in hazard detection, we developed a computerized cognitive training method with a focus on hazard detection. In this study, we examined the effectiveness of this newly-developed interactive training program in improving older drivers' detection of road hazards.

METHODS

Using a matched-pair design, nine triads of three older drivers (aged 65 or above) with matched pretest performance and gender were formed. For each triad, each participant was randomly allocated to one of the three groups: 1) active training group to receive the two training sessions (1.5-2.5h per session); 2) passive training group to receive two video-watching sessions (i.e., watch the video of training session performance from the corresponding paired participant); 3) no-contact control group. Older drivers' performances on hazard detection and simulated driving were measured before and after training.

RESULTS

The active training group showed significant training effects on the computerized hazard detection task and simulated driving performance, while there were only marginal effects in the passive training group and no effects in the no-contact control group. A post-training survey suggested older drivers were receptive to the Drive Aware training program.

CONCLUSIONS

The computerized Drive Aware training program has the potential to be used as a prevention and intervention tool to improve older drivers' hazard detection performance. Future studies should examine the effectiveness of this tool in more diverse samples and in long-term outcomes.

Dual-target hazard perception: Could identifying one hazard hinder a driver's capacity to find a second?

Low-level cognitive processes like visual search are crucial for hazard detection. In dual-target searches, subsequent search misses (SSMs) are known to occur when the identification of one target impedes detection of another that is concurrently presented. Despite the high likelihood of concurrent hazards in busy driving environments, SSMs have not been empirically investigated in driving. In three studies, participants were asked to identify safety-related target(s) in simulated traffic scenes that contained zero, one, or two target(s) of low or high perceptual saliency. These targets were defined as objects or events that would have prevented safe travel in the direction indicated by an arrow preceding the traffic scene. Findings from the pilot study (n = 20) and Experiment 1 (n = 29) demonstrated that detecting one target hindered drivers' abilities to find a second from the same scene. In Experiment 2 (n = 30), explicit instructions regarding the level of risk were manipulated. It was found that search times were affected by the instructions, though SSMs persisted. Implications of SSMs in understanding the causes of some crashes are discussed, as well as future directions to improve ecological and criterion validity and to explore the roles of expertise and cognitive capabilities in multi-hazard detection.

Developing and testing a hazard prediction task for novice drivers: A novel application of naturalistic driving videos

INTRODUCTION

Novice drivers' inability to appropriately anticipate and respond to hazards has been implicated in their elevated crash risk. Our goal was to develop a driving hazard prediction task using naturalistic videos from the U.S. context that could distinguish between novice and experienced drivers.

METHOD

Using the query builder from the SHRP 2 InSight Data Access Website, we identified a sample of 1034 videos for further review. Task criteria reduced these to 30 videos of near-crash events that were split into event and non-event segments and were used to develop the driving hazard prediction task (task). Participants, aged 16-20 years-old (22 novice and 19 experienced drivers) completed the task during which they watched event and non-event videos and were asked, "How likely was the driver of this car to get into a crash?" after each video. Overall ratings for hazardousness were calculated for experienced and novice drivers as well as a group difference score for hazardousness.

RESULTS

All participants rated event videos as more hazardous than non-event videos, but there was no main effect of group. Rather, there was a significant EventbyGroup interaction in which there were no group differences in hazard ratings for non-event videos, but experienced drivers rated event videos as more hazardous than novice drivers. Specific characteristics of the event videos, such as the hazard development period, were related to differences between novice and experienced drivers' hazardousness ratings.

CONCLUSION

To the best of our knowledge, this is the first use of naturalistic driving videos from an existing database as experimental stimuli. We found that the task discriminated between novice and experienced drivers' ratings of hazardousness. This distinction suggests naturalistic driving videos may be viable stimuli for experimental studies. Practical Applications: The application of naturalistic driving video database for experimental research may hold promise.

Seeing the truck, but missing the cyclist: effects of blur on duration thresholds for road hazard detection

Drivers must respond promptly to a wide range of possible road hazards, from trucks veering into their lane to pedestrians stepping onto the road. While drivers' vision is tested at the point of licensure, visual function can degrade, and drivers may not notice how these changes impact their ability to notice and respond to events in the world in a timely fashion. To safely examine the potential consequences of visual degradation on hazard detection, we performed two experiments examining the impact of simulated optical blur on participants' viewing duration thresholds in a hazard detection task, as a proxy for eyes-on-road duration behind the wheel. Examining this question with older and younger participants, across two experiments, we found an overall increase in viewing duration thresholds under blurred conditions, such that younger and older adults were similarly impacted by blur. Critically, in both groups, we found that the increment in thresholds produced by blur was larger for non-vehicular road hazards (pedestrians, cyclists and animals) compared to vehicular road hazards (cars, trucks and buses). This work suggests that blur poses a particular problem for drivers detecting non-vehicular road users, a population considerably more vulnerable in a collision than vehicular road users. These results also highlight the importance of taking into account the type of hazard when considering the impacts of blur on road hazard detection.

A new methodological framework for hazard detection models in health information technology systems

The adoption of health information technology (HIT) has facilitated efforts to increase the quality and efficiency of health care services and decrease health care overhead while simultaneously generating massive amounts of digital information stored in electronic health records (EHRs). However, due to patient safety issues resulting from the use of HIT systems, there is an emerging need to develop and implement hazard detection tools to identify and mitigate risks to patients. This paper presents a new methodological framework to develop hazard detection models and to demonstrate its capability by using the US Department of Veterans Affairs' (VA) Corporate Data Warehouse, the data repository for the VA's EHR. The overall purpose of the framework is to provide structure for research and communication about research results. One objective is to decrease the communication barriers between interdisciplinary research stakeholders and to provide structure for detecting hazards and risks to patient safety introduced by HIT systems through errors in the collection, transmission, use, and processing of data in the EHR, as well as potential programming or configuration errors in these HIT systems. A nine-stage framework was created, which comprises programs about feature extraction, detector development, and detector optimization, as well as a support environment for evaluating detector models. The framework forms the foundation for developing hazard detection tools and the foundation for adapting methods to particular HIT systems.

Design and testing of a personalized noise monitoring system

Agricultural workers are more prone to noise-induced hearing loss than are many other workers. Hearing protection device use among agricultural workers is low, but training can increase hearing protection device use. This work proposes a system designed to automatically inform agricultural workers when they were exposed to noises that exceed the National Institute for Occupational Safety and Health (NIOSH) recommended exposure level. The smartphone-based system worn on the arm uses a noise dosimeter to measure noise exposures throughout the day to within ±2 A-weighted decibels of a Class 2 sound level meter. The device collects location and audio data, which are transferred to a server and presented to the worker on a locally hosted website. The website details noise exposure and helps the worker identify where exposure occurred and what specific tasks exceed NIOSH's recommended exposure limit, putting them at higher risk of noise-induced hearing loss. With this understanding, the worker is expected to adopt behavior changes and better hearing protection use at critical places and times. This pilot study evaluates the accuracy of the noise dosimeter and GPS relative to gold-standard instruments. The system was tested on a farm with outputs compared with gold-standard instruments. A-weighted, 1-sec averaged sound pressure levels and position data were collected while users were performing a variety of tasks indoors and outdoors. The smartphone's external noise dosimeter read within ±2 A-weighted decibels of the Class 2 reference dosimeter 59% of the time. The positioning devices had an average error of sub-4 m. While not perfectly matching gold-standard instruments, the device is capable of identifying and collecting information relative to loud noise events that promote noise-induced hearing loss.