Effects of executive load on crashes and near-crashes for young versus older drivers

With the increasing use of infotainment systems in vehicles, secondary tasks requiring executive demand may increase crash risk, especially for young drivers. Naturalistic driving data were examined to determine if secondary tasks with increasing executive demand would result in increasing crash risk. Data were extracted from the Second Strategic Highway Research Program Naturalistic Driving Study, where vehicles were instrumented to record driving behavior and crash/near-crash data. executive and visual-manual tasks paired with a second executive task (also referred to as dual executive tasks) were compared to the executive and visual-manual tasks performed alone. Crash/near-crash odds ratios were computed by comparing each task condition to driving without the presence of any secondary task. Dual executive tasks resulted in greater odds ratios than those for single executive tasks. The dual visual-manual task odds ratios did not increase from single task odds ratios. These effects were only found in young drivers. The study shows that dual executive secondary task load increases crash/near-crash risk in dual task situations for young drivers. Future research should be conducted to minimize task load associated with vehicle infotainment systems that use such technologies as voice commands.

Impact of non-driving related tasks while operating automated driving systems (ADS): A systematic review

Automated Driving Systems (ADS) (SAE, 2021), promise improved safety and comfort for drivers. Current technological advances have resulted in increased automation capabilities. However, with the increase in automation capabilities, there is a shift in how drivers interact with their vehicles. Drivers can now temporarily hand over the control of the driving task to ADS under certain conditions. However, with ADS in temporary control of the vehicle, drivers may choose to engage in non-driving related tasks (NDRT). The current capabilities of ADS do not allow drivers to hand over control of the driving task indefinitely. Drivers must remain aware and be ready to take back control if necessary. There is a need to better understand drivers' performance and behaviors when driving with ADS, especially when engaged in NDRTs. This literature review, therefore, aims to understand the state of knowledge on automated vehicle systems and driver distraction. This review was conducted as per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies found a significant increase in takeover times while engaging in NDRTs and driving with automation active. Studies also discuss a change in driver's visual attention, with more focus given to NDRTs as compared to the front roadway. The concerning effects of increasing reaction times and decreases in visual attention can be mitigated by using interventions and studies have had success in redirecting drivers attention and reorient them to the task of driving. The review, therefore, includes a discussion of ADS and NDRT engagement and its impact on driving behaviors such as take-over times, visual attention, trust, and workload. Implications on driver safety and performance are discussed in light of this synthesis.

The Impact of driver distraction and secondary tasks with and without other co-occurring driving behaviors on the level of road traffic crashes

Driving safety is typically affected by concurrent non-driving tasks. These activities might negatively impact the trips' outcome and cause near-crash or crash incidents and accidents. The crashes impose a tremendous social and economic cost to society and might affect the involving individuals' quality of life. As it stands, road injuries are ranked among top-ten leading causes of death by the World Health Organization. Distracted driving is defined as an attention diversion of the driver toward a competing activity. It was shown in numerous studies that distracted driving increase the probability of near-crash or crash events. By leveraging the statistical power of the large SHRP2 naturalistic data, we are able to quantify the preponderance of specific distractions during daily trips and confirm the causality factor of an ubiquitous non-driving task in the crash event. We show that, except for phone usage which happens more frequently in near-crash and crash categories than in baseline trips, both distracted driving and secondary tasks occur almost uniformly in different types of trips. In this study, we investigate the impact of the co-occurrence of distracted driving with other driving behaviors and secondary tasks. It is found that the co-occurrence of distracted driving with other driving behaviors or secondary tasks increase the chance of near-crash and crash events. This study's findings can inform the design and development of more precise and reliable driving assistance and warning systems.

Do drivers self-regulate their engagement in secondary tasks at intersections? An examination based on naturalistic driving data

Using naturalistic driving data, this study explored the prevalence of engagement in secondary tasks whilst driving through intersections, and investigated whether drivers manage and self-regulate such behaviour in response to variations in roadway and environmental conditions. Video recordings of in-vehicle and external scenes were coded for precisely defined categories of secondary tasks and related contextual variables. The findings indicated that nearly one-quarter of the total driving time at intersections was spent on secondary activities and that lower engagement occurred within intersections compared to phases immediately upstream or downstream. Drivers were less likely to occupy themselves with secondary tasks when their vehicles were moving than when they were stationary. Elderly drivers showed less inclination to perform secondary tasks than did younger drivers. Lastly, drivers tended to perform secondary tasks less frequently at intersections managed by traffic signs than those controlled by traffic lights, when they did not have priority compared to when they had priority, and in adverse weather conditions compared to fine weather conditions. In conclusion, drivers appeared to self-regulate secondary task engagement in response to roadway and environmental conditions. Specifically, they exercised self-regulation by reducing their secondary task engagement when the driving task was more challenging. The findings from this study provide preliminary evidence for targeting the education and training of drivers and media campaigns related to safe driving strategies and managing distractions.

A hierarchical machine learning classification approach for secondary task identification from observed driving behavior data

According to NHTSA, more than 3477 people (including 551 non-occupants) were killed and 391,000 were injured due to distraction-related crashes in 2015. The distracted driving epidemic has long been under research to identify its impact on driving behavior. There have been a few attempts to detect drivers' engagement in secondary tasks from observed driving behavior. Yet, to the authors' knowledge, not much effort has been directed to identify the types of secondary tasks from driving behavior parameters. This study proposes a bi-level hierarchical classification methodology using machine learning to identify the different types of secondary tasks drivers are engaged in using their driving behavior parameters. At the first level, drivers' engagement in secondary tasks is detected, while at the second level, the distinct types of secondary tasks are identified. Comparative evaluation is performed between nine ensemble tree classification methods to identify three types of secondary tasks (hand-held cellphone calling, cellphone texting, and interaction with an adjacent passenger). The inputs to the models are five driving behavior parameters (speed, longitudinal acceleration, lateral acceleration, pedal position, and yaw rate) along with their standard deviations. The results showed that the overall secondary task detection accuracy ranged from 66% to 96%, except for the Decision Tree that was able to detect engagement in secondary tasks with a high accuracy of 99.8%. For the identification of secondary tasks types, the overall accuracy ranged from 55% to 79%, with the highest accuracy of 82.2% achieved by the Random Forest method. The findings of the paper show the proposed methodology promising to (1) characterize drivers' engagement in unlawful secondary tasks (such as texting) as a counter measure to prevent crashes, and (2) alert drivers to pay attention back to the main driving task when risky changes to their driving behavior take place.

An exploratory study of long-haul truck drivers' secondary tasks and reasons for performing them

Research on drivers has shown how certain visual-manual secondary tasks, unrelated to driving, increase the risk of being involved in crashes. The purpose of the study was to investigate (1) if long-haul truck drivers in Sweden engage in secondary tasks while driving, what tasks are performed and how frequently, (2) the drivers' self-perceived reason/s for performing them, and (3) if psychological factors might reveal reasons for their engaging in secondary tasks. The study comprised 13 long-haul truck drivers and was conducted through observations, interviews, and questionnaires. The drivers performed secondary tasks, such as work environment related "necessities" (e.g., getting food and/or beverages from the refrigerator/bag, eating, drinking, removing a jacket, face rubbing, and adjusting the seat), interacting with a mobile phone/in-truck technology, and doing administrative tasks. The long-haul truck drivers feel bored and use secondary tasks as a coping strategy to alleviate boredom/drowsiness, and for social interaction. The higher number of performed secondary tasks could be explained by lower age, shorter driver experience, less openness to experience, lower honesty-humility, lower perceived stress, lower workload, and by higher health-related quality of life. These explanatory results may serve as a starting point for further studies on large samples to develop a safer and healthier environment for long-haul truck drivers.

Evaluating distraction of in-vehicle information systems while driving by predicting total eyes-off-road times with keystroke level modeling

Increasingly complex in-vehicle information systems (IVIS) have become available in the automotive vehicle interior. To ensure usability and safety of use while driving, the distraction potential of system-associated tasks is most often analyzed during the development process, either by employing empirical or analytical methods, with both families of methods offering certain advantages and disadvantages. The present paper introduces a method that combines the predictive precision of empirical methods with the economic advantages of analytical methods. Keystroke level modeling (KLM) was extended to a task-dependent modeling procedure for total eyes-off-road times (TEORT) resulting from system use while driving and demonstrated by conducting two subsequent simulator studies. The first study involved the operation of an IVIS by N = 18 participants. The results suggest a good model fit (R(2)Adj. = 0.67) for predicting the TEORT, relying on regressors from KLM and participant age. Using the parameter estimates from study 1, the predictive validity of the model was successfully tested during a second study with N = 14 participants using a version of the IVIS prototype with a revised design and task structure (rPred.-Obs. = 0.58). Possible applications and shortcomings of the approach are discussed.

Effects of secondary tasks on auditory detection and crossing thresholds in relation to approaching vehicle noises

Research suggests an association between distracting environmental sound stimuli and poorer performance in detecting and localizing approaching vehicles using auditory cues. However, no studies have investigated the distractive potential posed by intrapersonal distractors in the context of pedestrian auditory perception. We examined the effects of holding naturalistic vocal and texting cell phone conversations on participants' auditory detection of approaching vehicles and crossing thresholds in a non-visual simulated setting. Ninety-nine adults were randomly assigned to conditions of vocal conversation, texting conversation, or a control group and completed an auditory vehicle detection task. Participants in the vocal cell phone conversation group detected vehicles at significantly shorter distances than participants in the control group. The concurrence of a secondary task did not affect the distances at which participants deemed vehicles noise too close for them to safely cross (i.e., crossing thresholds). Implications for future research and injury prevention are discussed.

Identifying cognitive distraction using steering wheel reversal rates

The influence of driver distraction on driving performance is not yet well understood, but it can have detrimental effects on road safety. In this study, we examined the effects of visual and non-visual distractions during driving, using a high-fidelity driving simulator. The visual task was presented either at an offset angle on an in-vehicle screen, or on the back of a moving lead vehicle. Similar to results from previous studies in this area, non-visual (cognitive) distraction resulted in improved lane keeping performance and increased gaze concentration towards the centre of the road, compared to baseline driving, and further examination of the steering control metrics indicated an increase in steering wheel reversal rates, steering wheel acceleration, and steering entropy. We show, for the first time, that when the visual task is presented centrally, drivers' lane deviation reduces (similar to non-visual distraction), whilst measures of steering control, overall, indicated more steering activity, compared to baseline. When using a visual task that required the diversion of gaze to an in-vehicle display, but without a manual element, lane keeping performance was similar to baseline driving. Steering wheel reversal rates were found to adequately tease apart the effects of non-visual distraction (increase of 0.5° reversals) and visual distraction with offset gaze direction (increase of 2.5° reversals). These findings are discussed in terms of steering control during different types of in-vehicle distraction, and the possible role of manual interference by distracting secondary tasks.

An observational study of secondary task engagement while driving on urban streets in Iranian Safe Communities

In Iran the prevalence of traffic injuries and death from vehicle collisions are high. Driver engagement in non-driving-related tasks has been previously identified as an important contributing factor to crashes. Therefore, the objective of the present study was to investigate the prevalence of drivers' engagement in potentially distracting activities in Kashmar, Khalilabad and Bardaskan, which are three Iranian International Safe Communities. Observations took place at 12 randomly selected roadside locations in each city, which were comprised of six main streets and six side streets. In total 7979 drivers were observed. The prevalence rates of potentially distracting activities in Kashmar, Khalilabad and Bardaskan were 24.3%, 26% and 24.9%, respectively. In both Kashmar and Khalilabad the most frequently observed secondary tasks were drivers talking to passengers (10.6% and 11.5%, respectively) followed by mobile phone use (3.4% and 4.0%, respectively). Although in Bardaskan the most commonly observed secondary task was also talking to passengers (12.7%), the second most common was reaching for an object (3.2%). In all three cities younger drivers were significantly more likely to be observed engaged in a secondary task while driving. Furthermore, involvement in secondary tasks while driving was significantly higher amongst females and those driving on a working day. The percentage of drivers identified as potentially distracted in these three Safe Communities was worryingly high. Thus, interventions should be integrated into the WHO Safe Community network in these cities, including: education regarding the risks associated with engaging in secondary activities while driving, law enforcement, tougher legislation, periodic assessment, raising public awareness, as well as attracting political and social support.