This study investigates the effect of high cognitive load and drowsiness on driving performance (speed, lane position, steering wheel movement) and driver physiology (cardiac activity, skin conductance) and uses these measures in classifying high cognitive load, alert, and drowsy driver states. A within subject driving simulator experiment was conducted with twenty-seven participants (14 females, mean age: 36.7). High cognitive load was induced via the n-back task (1-back, 2-back), a commonly used auditory-verbal recall task. Drowsiness was induced by monotonous driving (i.e., extended periods of low cognitive load), and was rated by trained observers. Mixed linear models were used to analyze the differences between the driver states, while machine learning models were used for multi-class classification. Compared to alert driving with no additional cognitive load, high cognitive load was associated with greater physiological arousal and speed variation and reduced speed and standard deviation of lane position (SDLP). Drowsiness was associated with lower physiological arousal and increased speed, SDLP, and standard deviation of steering wheel angle. Tree-based ensemble models (i.e., random forest, XGBoost) performed the best in classification. With simple features such as the average and SD, high cognitive load, drowsiness, and alert states were classified with up to 76% average accuracy. These measures could differentiate high cognitive load states with around 85% AUC and drowsiness with around 79% AUC within one model. These findings can help in the selection of metrics for driver monitoring systems that can differentiate driver cognitive overload and underload and inform the design of real-time intervention systems.

A technology scan of in-vehicle touchscreens was conducted to identify trends for the use of touchscreen displays and controls in North American passenger vehicles, and relevant human factors literature was used to explore the safety implications of these trends. The technology scan focused on touchscreen devices that are installed as original equipment in new and recent passenger vehicles (i.e., from 2020-2024). Relevant data was extracted from owner's manuals, manufacturer websites, and online videos. Results indicate a trend towards larger touchscreens in the last five years and the moving of functions (e.g., climate control, audio entertainment, primary vehicle controls) from physical buttons to touchscreens. Based on findings from human factors literature, these trends may have a negative impact on road safety. For example, moving primary vehicle controls and infotainment tasks to touchscreens may result in increased distraction and impaired driving performance. Insights about consumer recalls related to touchscreens are also reported.

Providing personalized behavioral information as feedback to drivers can lead to safer practices. However, feedback efficacy is likely moderated by the driver's level of motivation towards behavioral change. Gamification of feedback, which is the incorporation of game design elements intended to motivate drivers toward safe behaviors, could potentially reduce unsafe behaviors in the long term. This article assesses a gamified driver feedback design in mitigating driver distraction and enhancing driving performance among younger drivers. A driving simulator study was conducted with 42 drivers, 21–30 years old, comparing: 1) no feedback; 2) real-time feedback; 3) real-time feedback + postdrive feedback; and 4) real-time feedback + postdrive feedback + game design elements to examine their impact on distraction engagement (manual-visual interactions with an in-vehicle display) and driving performance. Groups that received postdrive feedback, both with and without gamification elements, showed reduced distraction engagement and enhanced driving performance compared to no feedback. Between the two types of postdrive feedback, the nongamified feedback provided more benefits in reducing the 95th percentile glance duration to the in-vehicle display, and the one with gamification provided more benefits in reducing the rate of manual interactions with the in-vehicle display. Meanwhile, no benefits were observed with the real-time feedback only condition over no feedback. Despite minor differences in efficacy, both postdrive and gamification feedback appear to be effective countermeasures for distracted driving in the short term. Future research should investigate other game designs for driver feedback and assess the impact of feedback gamification over longer-term exposure.

Inappropriate speed choice increases crash risk. Emerging technologies, such as in-vehicle systems, can provide real-time and post-drive feedback to alert or educate drivers of their unsafe speed choices. Financial incentives, such as insurance reductions, can also be integrated with feedback. Although previous research supports the benefits of these approaches, research is limited in establishing their relative effectiveness and their long-term impact on speed limit compliance. A naturalistic driving study with 58 participants was conducted to investigate the effects of post-drive feedback versus financial incentives, both provided in conjunction with real-time feedback, on mitigating speeding behaviors. The study included a four-week long baseline data collection period (no feedback), a ten-week intervention period examining three feedback types in a between subjects design (real-time only; real-time and financial incentives; and real-time and post-drive feedback), and a two-week post-intervention period (feedback removed). Both real-time feedback alone and in conjunction with financial incentives were effective in raising speed limit compliance, but the effects did not sustain after the removal of feedback/incentives. Post-drive feedback, which was provided in the vehicle after a trip and as aggregated feedback on a website, does not appear to provide any benefits toward speed limit compliance; potential factors, including (voluntary) access frequency of post-drive feedback website, are discussed. Future research should continue to explore the feedback/incentive design space—carefully considering individual differences in driver characteristics and motivations—for supporting speed limit compliance and other risky driving behaviors.

This paper explores the influence of high cognitive load on vehicle driver's electroencephalography (EEG) signals collected from two channels (Fp1, Fp2) using a wireless consumer-grade system. Although EEG has been used in driving-related research to assess cognitive load, only a few studies focused on high load, and they used research-grade systems. Recent advancements allow for less intrusive and more affordable systems. As an exploration, we tested the feasibility of one such system to differentiate among three levels of cognitive taskload in a simulator study. Thirty-seven participants completed a baseline drive with no secondary task and two drives with a modified version of the n-back task (1-back and 2-back). The modification removed the verbal response required during task presentation to prevent EEG-signal degradation, with the 2-back task expected to impose higher load than that by the 1-back task. Another objective of this study is to validate that this modified task increased the cognitive load in the expected manner. The modified task led to significant trends from baseline to 1-back, and from 1-back to 2-back in participants' heart rate, galvanic skin response, respiration, horizontal gaze position variability, and pupil diameter, all in line with the previous driving-related studies on cognitive load. Furthermore, the EEG system was observed to be sensitive to the modified task, with the power of alpha band decreasing significantly with increasing n-back levels (baseline versus 1-back: 0.092 Bels on Fp1, 0.179 on Fp2; 1-back versus 2-back: 0.209 on Fp1, 0.147 on Fp2). Thus, a consumer-grade EEG system has the potential to capture high levels of cognitive load experienced by drivers.

In this paper, we focus on exploiting the vehicle kinematic signals from a naturalistic driving dataset to estimate motor control difficulty associated with the driving environment (i.e., curvature and poor surface condition), and detect whether the driver was engaged in a secondary task or not. Advanced driver assistance systems can exploit such driver behavior models to better support the driving task and improve safety. Hidden Markov models were built from sequential data; lateral (x-axis) and longitudinal (y-axis) acceleration were used to classify motor control difficulty (lower vs. higher), whereas GPS speed and steering wheel position were used to classify secondary task engagement (yes vs. no). The resulting accuracy for lower motor control difficulty classification was 72.03%, whereas 71.27% was achieved for higher motor control difficulty. Cases of engagement in secondary task vs. not were classified with 84.4% and 74.0% accuracy, respectively.

This study examines driver engagement with smartwatches and smartphones while driving. Twelve participants (7 novice and 5 experienced smartwatch users) drove in a high-fidelity simulator while receiving notifications from either a smartwatch (Pebble) or a smartphone (LG Nexus 5). It was found that participants had more glances, on average, per notification while using the smartwatch compared to the smartphone. Further, their brake response times were longer when they received notifications prior to a lead vehicle braking event on the smartwatch compared to when they did not receive any notifications and when they received notifications on the smartphone. Contrary to these glance and driving performance findings, participants perceived similar levels of risk for the two devices, and they largely reported that smartwatch use while driving should receive penalties equal to or less than smartphone use with respect to distracted driving legislation. Thus, there appears to be a disconnection between drivers' actual performance while using smartwatches and their perceptions.

Driver distraction research primarily focuses on voluntary distraction. Little known research explicitly evaluates driver susceptibility to involuntary distractions. This paper investigates the relationships between glance behavior in response to irrelevant stimuli in a driving simulator and measures of inhibitory control assessed through a modified flanker task. Overall, inhibitory control appears to be a mechanism that relates to number of glances and average glance duration. Data from 16 participants show that smaller flanker compatibility effects (i.e., better inhibitory control) are significantly associated with fewer glances and shorter average glance durations to irrelevant stimuli in the simulator. No significant relation was found between time fixation on the irrelevant stimulus after its onset and the size of the flanker compatibility effect.

The ability to anticipate future events in the traffic environment is an important competence in driving. This paper extends our prior work: 1) to show potential benefits resulting from anticipatory competence in driving, and 2) to collate characteristics of anticipatory competence from a theoretical point of view. The reviewed literature is foundational to our understanding of anticipation as a high-level cognitive competence, allowing for the prediction of future traffic situations on a tactical level. We conceptualize anticipation as relying on the identification of stereotypical traffic situations based on indicative cues, and stress that the impacts of this competence are dependent on the driver's individual goals. Thus, anticipation enables a number of potential benefits, such as safety and fuel-efficiency, but the realization of these potential benefits depends on the goals of the driver. Further, we argue that the superior anticipatory competence of experienced drivers observed in an earlier simulator study can be explained via their heightened ability both to identify indicative cues, and interpret those cues relative to similar, memorized situations. We then capture anticipatory driving in a model inspired by the classical theory of information processing to describe the various steps necessary to process indicative cues from the environment, anticipate a future traffic situation, and take appropriate action or achieve a state of cognitive readiness.

Patients undergoing interfacility transfers are at potentially greater risk of adverse or critical events than those in hospital, and efficient transfers play a significant role in reducing mortality and morbidity. Medical dispatchers rely on accurate estimations of transfer time in determining the most appropriate method of transportation, often either a helicopter and/or land ambulance, in situations that are characterized by high time pressure and uncertainty. In this paper, we propose the design of a data-driven decision support tool to improve dispatcher transport mode decision making. We studied the dispatch process of the air and land medical transport system in Ontario, Canada through onsite observations and developed a tool which generates transfer time estimates based on historical data. We found that dispatchers have large estimation errors, and are biased toward higher degrees of underestimation for air transfers compared with land transfers. In contrast, the proposed tool produced estimates that had significantly less error than dispatcher estimates. The estimation error for the tool was on the average 21 min less: a practically significant difference in urgent patient care. Through onsite observations and the relevant literature, we also identified factors that may influence the collaboration between the dispatcher and the tool. This research is a first attempt to study how decisions are made for interfacility medical transfers and for evaluating the accuracy of human operator estimates of these transfer times. It is also the first to demonstrate a tool's utility in comparison to existing procedures for estimating transfer times.

Anticipation of future events is recognized to be a significant element of driver competence. Surely, guiding one's behavior through the anticipation of future traffic states provides potential gains in recognition and reaction times. However, the role of anticipation in driving has not been systematically studied. In this paper, we identify the characteristics of anticipation in driving and provide a working definition. In particular, we distinguish it from driving goals such as eco or defensive driving and define it as a high-level competence for efficient positioning of the vehicle to facilitate these goals. We also present a driving simulator study assessing the relation between driver experience and anticipation. Thirty drivers from three different experience categories (low, medium, and high) completed five scenarios, each involving several pre-event cues designed to allow the anticipation of an event. The results showed that more experienced drivers demonstrated more pre-event actions compared with less experienced drivers. While pre-event actions resulted in improved safety on certain occasions, the effects were often not significant. Future research should further investigate the mechanisms underlying anticipation, particularly how drivers make use of temporal and spatial gains obtained through the recognition of pre-event cues.

Anticipation of future events is recognized to be a significant element of driver competence. Surely, guiding one's behavior through the anticipation of future traffic states provides potential gains in recognition and reaction times. However, the role of anticipation in driving and ways to support it have not been systematically studied. In this paper, we identify the characteristics of anticipatory driving and provide a working definition. In particular, we distinguish it from overall driving goals such as eco or defensive driving, but rather present it as a high-level competence for efficient positioning of the vehicle to ultimately facilitate these goals. We also argue that anticipation occurs within the context of stereotypical scenarios and provide an initial taxonomy for the identification of such scenarios. We suggest the Decision Ladder as a useful way of modeling anticipatory driving and finally discuss a potential approach for the facilitation of anticipatory driving through skill- and rule-based behavior, which can allow for shortcuts on the Decision Ladder.

This paper evaluates the tele-operation of a mobile sensor platform. In current operations, this land vehicle is controlled by a team consisting of a driver and a sensor operator. Our experiment is the first attempt, for this system, to assess the impact, if any, of using a single operator versus a team of two operators, in order to inform staffing decisions as well as the design of future automated functions. The experiment was conducted using a simulator and 24 participants: 8 single operators and 8 teams of two operators. Lower mission completion times arose for the two-operator condition in spite of any extra time and workload generated by communication required. Operators in teams also assessed the system as more usable. These findings give support to the team strategy for tele-operating mobile sensor platforms, and have implications for the staffing and design of similarly complex uninhabited vehicle systems.

Discrete-event simulations for futuristic unmanned vehicle (UV) systems enable a cost- and time-effective methodology for evaluating various autonomy and human-automation design parameters. Operator mental workload is an important factor to consider in such models. We suggest that the effects of operator workload on system performance can be modeled in such a simulation environment through a quantitative relation between operator attention and utilization, i.e., operator busy time used as a surrogate real-time workload measure. To validate our model, a heterogeneous UV simulation experiment was conducted with 74 participants. Performance-based measures of attention switching delays were incorporated in the discrete-event simulation model by UV wait times due to operator attention inefficiencies (WTAIs). Experimental results showed that WTAI is significantly associated with operator utilization (UT) such that high UT levels correspond to higher wait times. The inclusion of this empirical UT-WTAI relation in the discrete-event simulation model of multiple UV supervisory control resulted in more accurate replications of data, as well as more accurate predictions for alternative UV team structures. These results have implications for the design of future human-UV systems, as well as more general multiple task supervisory control models.