Kinematic cues in driver-pedestrian communication to support safe road crossing.

OBJECTIVE: Right-of-way negotiation between drivers and pedestrians often relies on explicit (e.g., waving) and implicit (e.g., kinematic) cues that signal intent. Since effective driver-pedestrian communication is important for reducing safety-relevant conflicts, this study uses information theory to identify vehicle kinematic behaviors that provide the greatest information gain and serve as cues for pedestrians to cross safely. DATA SOURCES: A driver-pedestrian dataset with 348 interactions was extracted from a large naturalistic driving data collection effort. It includes 325 instances of a pedestrian crossing the vehicle's path and 23 instances in which the vehicle did not yield to a pedestrian. Kinematic data were collected from the vehicle's CAN. Pedestrian behaviors, driver cues, and contextual information were manually annotated from a forward-facing video. METHODS: We used kernel density estimation to quantify the probabilities of vehicle acceleration, speed, and standard deviation of speed, for a given vehicle position and pedestrian behavior. Mutual information was then calculated between the estimated distributions given a pedestrian behavior (crossing/not crossing; walking/pausing) across intersection types (protected, e.g., stop signs; designated, e.g., crosswalks; and undesignated, e.g., jaywalking). RESULTS: The patterns mutual information conveyed by vehicle kinematics differed across measures (acceleration, speed, and standard deviation of speed) reaching peak values (in bits of information) at different distances from the pedestrian path. The mutual information conveyed by vehicle acceleration and pedestrian crossing behaviors peaked the farthest from the pedestrian path in the designated crossings, about 18 m away from the pedestrian path, with a difference in median deceleration of 1.01 m/s2 (p < 0.001) between pedestrian pausing and walking epochs. For protected crossings, the peak in mutual information occurred closer (10 m) to the pedestrian path, where median vehicle deceleration was significantly lower (0.55 m/s2; p < 0.05) in pausing epochs compared to walking epochs. For undesignated crossings, the peak in mutual information was the closest to the pedestrian crossing path, around 5 m, and was associated with a stronger deceleration behavior in pedestrian crossing epochs (-0.33 m/s2; p < 0.1). Vehicle speed demonstrated a similar sensitivity to distance from the pedestrian path across intersection types. Lastly, looking at the outcome of pedestrian behavior (i.e., crossing/not crossing), we find that the mutual information conveyed by acceleration, speed, and standard deviation of speed, peaked when the vehicle was at 30 m (stronger braking -0.37 m/s2; p < 0.1) and 10 m away, with greater acceleration (0.81 m/s2; p < 0.001) and faster speeds (2.41 m/s; p < 0.001) in pedestrian crossing epochs. SIGNIFICANCE OF RESULTS: This study examined driver-pedestrian information exchange using vehicle kinematic behavioral cues. We find that the differences in mutual information are shaped by multiple factors including the intersection type. In general, there was less mutual information gain in protected crossings which may be explained by unambiguous right-of-way rules guiding driver and pedestrian behavior, reducing the need for negotiation. Driver-pedestrian interactions in designated crossings seem to take place over a larger distance range compared to undesignated or protected crossings. These findings may support the design of automated driving and pedestrian safety systems that are able to consider the type, strength, and timing of kinematic cues to optimize driver-pedestrian negotiation. Eventually, such systems may enhance safe, efficient, and social interactions with pedestrians.

Driver response and recovery following automation initiated disengagement in real-world hands-free driving.

OBJECTIVE: Advanced driver assistance systems are increasingly available in consumer vehicles, making the study of drivers' behavioral adaptation and the impact of automation beneficial for driving safety. Concerns over driver's being out-of-the-loop, coupled with known limitations of automation, has led research to focus on time-critical, system-initiated disengagements. This study used real-world data to assess drivers' response to, and recovery from, automation-initiated disengagements by quantifying changes in visual attention, vehicle control, and time to steady-state behaviors. METHODS: Fourteen drivers drove for one month each a Cadillac CT6 equipped with Super Cruise (SC), a partial automation system that, when engaged, enables hands-free driving. The vehicles were instrumented with data acquisition systems recording driving kinematics, automation use, GPS, and video. The dataset included 265 SC-initiated disengagements identified across 5,514 miles driven with SC. RESULTS: Linear quantile mixed-effects models of glance behavior indicated that following SC-initiated disengagement, the proportions of glances to the Road decreased (Q50Before=0.91, Q50After=0.69; Q85Before=1.0, Q85After=0.79), the proportions of glances to the Instrument Cluster increased (Q50Before=0.14, Q50After=0.25; Q85Before=0.34, Q85After=0.45), and mean glance duration to the Road decreased by 4.86 sec in Q85. Multinomial logistic regression mixed-models of glance distributions indicated that the number of transitions between glance locations following disengagement increased by 43% and that glances were distributed across fewer locations. When driving hands-free, take over time was significantly longer (2.4 sec) compared to when driving with at least one hand on the steering wheel (1.8 sec). Analysis of moment-to-moment distributional properties of visual attention and steering wheel control following disengagement indicated that on average it took drivers 6.1 sec to start the recovery of glance behavior to the Road and 1.5 sec for trend-stationary proportions of at least one hand on the steering wheel. CONCLUSIONS: Automation-initiated disengagements triggered substantial changes in driver glance behavior including shorter on-road glances and frequent transitions between Road and Instrument Cluster glance locations. This information seeking behavior may capture drivers' search for information related to the disengagement or the automation state and is likely shaped by the automation design. The study findings can inform the design of more effective driver-centric information displays for smoother transitions and faster recovery.

Interdependence of driver and pedestrian behavior in naturalistic roadway negotiations.

OBJECTIVE: This paper characterizes the actions of pedestrian-driver dyads by examining their interdependence across intersection types (e.g., zebra crossings, stop signs). Additionally, the analysis of interdependence captures other external factors, such as other vehicles or pedestrians, that may influence the interaction. METHODS: A 228 epoch vehicle-pedestrian interaction dataset was extracted from a large naturalistic driving data collection effort, which included vehicle, pedestrian, and contextual information (e.g., intersection type, jaywalking, vehicle maneuver, and lead vehicle presence). An expanded Actor-Partner Interdependence Model (APIM) was used to analyze driver-pedestrian dyads using driver and pedestrian standard deviations of velocity as the independent variables and wait times as dependent variables. APIM structural equation models were augmented to include driver effects (i.e., lead vehicle and maneuver type) and pedestrian effects (i.e., lead pedestrian, crossing group size, crossing direction). RESULTS: The level of protection afforded by an intersection had an effect on the extent of driver-pedestrian dyadic behavior. Interactions in undesignated crossings (i.e., jaywalking) were associated with interdependent behavior whereas interactions in designated crossings (i.e., crosswalks and parking lots) showed a partner effect on the driver's wait time but no significant corresponding partner effect on the pedestrian. Finally, protected intersection interactions (i.e., traffic lights and stop signs) demonstrated no significant partner effects. CONCLUSIONS: The difference in behavior patterns associated with the intersection type and level of protection shows that context can mediate the level of negotiation required between drivers and pedestrians. These findings inform how context and driver-pedestrian interactions should be incorporated in future modeling efforts which may, ultimately, support design of automated systems that are able to interact more safely, efficiently, and socially.

Characterizing driver speeding behavior when using partial-automation in real-world driving.

Objective: Speeding is a prevalent and complex risky behavior that can be affected by many factors. Understanding how drivers speed is important for developing countermeasures, especially as new automation features emerge. The current study seeks to identify and describe types of real-world speeding behaviors with and without the use of partial-automation.Methods: This study used a combination of supervised and unsupervised data analysis techniques to assess relevant factors in real-world speeding epochs, extracted from the MIT Advanced Vehicle Technology Naturalistic Driving Study, and classified them into distinct speeding behaviors. Speeding epochs were defined as traveling at least 5 mph over the speed limit for a minimum duration of 3 s. Vehicle speed-exceedance profiles were characterized over time using Dynamic Time Warping and included in multivariate models that evaluated the associations between different features of the speeding epochs, such as speeding duration and magnitude. Finally, the identified features were used to cluster speeding behaviors using the Gower dissimilarity measure.Results: The analysis yielded four types of behaviors in both partially-automated and manual driving: (i) Incidental speeding (low duration, low magnitude), (ii) Moderate speeding (low duration, moderate magnitude), (iii) Elevated speeding (moderate duration, high magnitude), and (iv) Extended speeding (long duration, high magnitude). When comparing the behaviors with and without partial-automation use, both Incidental and Moderate speeding were found to have significantly longer durations with partial-automation than manual driving. Elevated speeding was found to be more prevalent and associated with higher magnitudes during manual than with partially-automated driving. Finally, although Extended speeding was more prevalent during automation use, it was associated with a lower mean and maximum speed magnitude compared to Extended speeding during manual driving.Conclusions: This work highlights the variability in speeding behavior between and within partially-automated and manual driving. The design of systems that mitigate risky speeding behaviors should consider targeting divergent behaviors observed between manual and automated driving as a mechanism to mitigate the prevalence of the different behaviors associated with each state.

Speeding behavior while using adaptive cruise control and lane centering in free flow traffic.

OBJECTIVE: Adaptive cruise control (ACC) and lane centering are usually marketed as convenience features but may also serve a safety purpose. However, given that speeding is associated with increased crash risk and worse crash outcomes, the extent to which driver's speed using ACC may reduce the maximum safety benefit they can obtain from this system. The current study was conducted to characterize speeding behavior among drivers using adaptive cruise control and a similar system with added lane centering. METHODS: We recruited 40 licensed adult drivers from the Boston, Massachusetts, metro area. These drivers were given either a 2017 Volvo S90 or a 2016 Range Rover Evoque to use for about 4 weeks. RESULTS: Drivers were significantly more likely to speed while they used ACC (95%) relative to periods of manual control (77%). A similar pattern arose for drivers using ACC with added lane centering (96% vs. 77%). Drivers who traveled over the posted limit with these systems engaged also sped slightly faster than drivers controlling their vehicle manually. Finally, we found that these differences were the most pronounced on limited-access roads with a lower speed limit (55 mph). CONCLUSIONS: These findings point to a possible obstacle to obtaining the full safety potential from this advanced vehicle technology. Any consideration of the net safety effect of ACC and lane centering should account for the effects of more frequent and elevated speeding.

A model for naturalistic glance behavior around Tesla Autopilot disengagements.

OBJECTIVE: We present a model for visual behavior that can simulate the glance pattern observed around driver-initiated, non-critical disengagements of Tesla's Autopilot (AP) in naturalistic highway driving. BACKGROUND: Drivers may become inattentive when using partially-automated driving systems. The safety effects associated with inattention are unknown until we have a quantitative reference on how visual behavior changes with automation. METHODS: The model is based on glance data from 290 human initiated AP disengagement epochs. Glance duration and transition were modelled with Bayesian Generalized Linear Mixed models. RESULTS: The model replicates the observed glance pattern across drivers. The model's components show that off-road glances were longer with AP active than without and that their frequency characteristics changed. Driving-related off-road glances were less frequent with AP active than in manual driving, while non-driving related glances to the down/center-stack areas were the most frequent and the longest (22% of the glances exceeded 2 s). Little difference was found in on-road glance duration. CONCLUSION: Visual behavior patterns change before and after AP disengagement. Before disengagement, drivers looked less on road and focused more on non-driving related areas compared to after the transition to manual driving. The higher proportion of off-road glances before disengagement to manual driving were not compensated by longer glances ahead. APPLICATION: The model can be used as a reference for safety assessment or to formulate design targets for driver management systems.

Driver behavior and the use of automation in real-world driving.

BACKGROUND: The emergence of partial-automation in consumer vehicles is reshaping the driving task, the driver role, and subsequent driver behavior. When using partial-automation, drivers delegate the operational control of the dynamic driving task to the automation system, while remaining responsible for monitoring, object/event detection, response selection, and execution. Hence, driving has become a collaboration between driver and automation systems that is characterized by dynamic Transfers of Control (TOC). OBJECTIVE: This study aimed to assess how drivers leverage automation in real-world driving, identify driver and system-initiated TOCs, and provide a taxonomy to capture the underlying driver behaviors associated with automation disengagement. METHODS: Fourteen participants drove instrumented Cadillac CT6 vehicles for one-month each, yielding 1690 trips (22,108 miles), with a total of 5343 TOCs between manual driving, SAE Level 1 Adaptive Cruise Control (ACC), and SAE Level 2 Super Cruise (SC). RESULTS: The use of automation on limited access highways was prevalent (40 % of the miles driven were with SC and 10 % with ACC) yet not continuous. Drivers frequently initiated transitions between automation levels (mean = 9.98, SD = 8.32, transitions per trip), temporarily taking over the longitudinal and/or lateral vehicle control. These transitions were not necessarily related to immediate risk mitigation, but rather to the execution of functions beyond the automation system's capabilities or representing preferences in task execution. Driver-initiated TOCs from SC to manual driving followed the structure and temporal aspects of the hierarchical model of driver behavior. Strategic, Maneuver, and Control TOCs were associated with significantly different patterns of vehicle kinematics, automation disengagement modality, and TOC duration. System-initiated automation disengagements from SC to manual driving were rare (1%). CONCLUSIONS: Generalizing from objective, real-world driving data, this study provides an ecologically valid taxonomy for transfer of control building upon the hierarchical model of driver behavior. We show that driver-automation interactions can occur in each level of the hierarchical model and that TOCs are part of the driver's strategic, maneuver, and control levels of decision making. Thus, TOCs are not isolated or rare events, but rather an integral part of an ongoing, continuous and dynamic collaboration. This taxonomy contextualizes TOCs, paving the way for greater understanding of when and why drivers will takeover control, exposes the underlying motivations for TOCs, and characterizes how these are reflected in the driver's actions. The findings can inform the development of driver-centered automation systems as well as policies and guidelines for current and future automation levels.

Patterns in transitions of visual attention during baseline driving and during interaction with visual-manual and voice-based interfaces.

Voice interfaces reduce visual demand compared with visual-manual interfaces, but the extent depends on design. This study compared visual demand during baseline driving with driving while using voice or manual inputs to place calls with Chevrolet MyLink, Volvo Sensus, or a smartphone. Mean glance duration and total eyes-off-road-time increased when using manual input compared with baseline driving; only eyes off road time increased with voice input. Confusion matrices developed with hidden Markov modelling characterise the similarity of glance sequences during baseline driving and while making phone calls. Glance sequences with the MyLink voice interface were misclassified as baseline driving more frequently than the other voice interfaces. Conversely, glance sequences with the Sensus and smartphone voice interfaces were more often misclassified as manual phone calling. Thus, the MyLink voice interface not only reduced the overall visual demand of placing calls, but produced glance patterns more similar to driving without another task. Practitioner Summary: The attention map and confusion matrix methodologies provide ways of characterising similarities and differences in glance behaviour across secondary task conditions, complementing traditional temporally based metrics (e.g. mean glance duration, long duration glances) while addressing some of the limitations of total-eyes-off-road-time (TEORT) for comparing secondary task behaviour to baseline driving.

Unsupervised fNIRS feature extraction with CAE and ESN autoencoder for driver cognitive load classification.

Objective. Understanding the cognitive load of drivers is crucial for road safety. Brain sensing has the potential to provide an objective measure of driver cognitive load. We aim to develop an advanced machine learning framework for classifying driver cognitive load using functional near-infrared spectroscopy (fNIRS).Approach. We conducted a study using fNIRS in a driving simulator with theN-back task used as a secondary task to impart structured cognitive load on drivers. To classify different driver cognitive load levels, we examined the application of convolutional autoencoder (CAE) and Echo State Network (ESN) autoencoder for extracting features from fNIRS.Main results. By using CAE, the accuracies for classifying two and four levels of driver cognitive load with the 30 s window were 73.25% and 47.21%, respectively. The proposed ESN autoencoder achieved state-of-art classification results for group-level models without window selection, with accuracies of 80.61% and 52.45% for classifying two and four levels of driver cognitive load.Significance. This work builds a foundation for using fNIRS to measure driver cognitive load in real-world applications. Also, the results suggest that the proposed ESN autoencoder can effectively extract temporal information from fNIRS data and can be useful for other fNIRS data classification tasks.

Glanceable, legible typography over complex backgrounds.

Modern digital interfaces display typeface in ways new to the 500 year old art of typography, driving a shift in reading from primarily long-form to increasingly short-form. In safety-critical settings, such at-a-glance reading competes with the need to understand the environment. To keep both type and the environment legible, a variety of 'middle layer' approaches are employed. But what is the best approach to presenting type over complex backgrounds so as to preserve legibility? This work tests and ranks middle layers in three studies. In the first study, Gaussian blur and semi-transparent 'scrim' middle layer techniques best maximise legibility. In the second, an optimal combination of the two is identified. In the third, letter-localised middle layers are tested, with results favouring drop-shadows. These results, discussed in mixed reality (MR) including overlays, virtual reality (VR), and augmented reality (AR), considers a future in which glanceable reading amidst complex backgrounds is common. Practitioner summary: Typography over complex backgrounds, meant to be read and understood at a glance, was once niche but today is a growing design challenge for graphical user interface HCI. We provide a technique, evidence-based strategies, and illuminating results for maximising legibility of glanceable typography over complex backgrounds. Abbreviations: AR: augmented reality; VR: virtual reality; HUD: head-up display; OLED: organic light-emitting diode; UX: user experience; MS: millisecond; CM: centimeter.

The great typography bake-off: comparing legibility at-a-glance.

Typography plays an increasingly important role in today's dynamic digital interfaces. Graphic designers and interface engineers have more typographic options than ever before. Sorting through this maze of design choices can be a daunting task. Here we present the results of an experiment comparing differences in glance-based legibility between eight popular sans-serif typefaces. The results show typography to be more than a matter of taste, especially in safety critical contexts such as in-vehicle interfaces. Our work provides both a method and rationale for using glanceable typefaces, as well as actionable information to guide design decisions for optimised usability in the fast-paced mobile world in which information is increasingly consumed in a few short glances. Practitioner summary: There is presently no accepted scientific method for comparing font legibility under time-pressure, in 'glanceable' interfaces such as automotive displays and smartphone notifications. A 'bake-off' method is demonstrated with eight popular sans-serif typefaces. The results produce actionable information to guide design decisions when information must be consumed at-a-glance. Abbreviations: DOT: department of transportation; FAA: Federal Aviation Administration; GHz: gigahertz; Hz: hertz; IEC: International Electrotechnical Commission; ISO: International Organization for Standardization; LCD: liquid crystal display; MIT: Massachusetts Institute of Technology; ms: milliseconds; OS: operating system.

Rapid holistic perception and evasion of road hazards.

How quickly can a driver perceive a critical hazard on or near the road? Evidence from vision research suggests that static scene perception is fast and holistic, but does this apply in dynamic road environments? Understanding how quickly drivers can perceive hazards in moving scenes is essential because it improves driver safety now, and will enable autonomous vehicles to work safely with drivers in the future. This paper describes a new, publicly available set of videos, the Road Hazard Stimuli, and a study assessing how quickly participants in the laboratory can detect and correctly respond to briefly presented hazards in them. We performed this laboratory experiment with a group of younger (20-25 years) and older (55-69 years) drivers, and found that while both groups only required brief views of the scene, older drivers required significantly longer to both detect (220 ms, younger; 403 ms, older) and correctly respond to hazards (388 ms younger; 605 ms older). Our results indicate that participants can perceive the scene and detect hazards holistically, without serially searching the scene, and can understand the scene and hazard sufficiently well to respond adequately with only slightly longer viewing durations. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Detection of brake lights while distracted: Separating peripheral vision from cognitive load.

Drivers rarely focus exclusively on driving, even with the best of intentions. They are distracted by passengers, navigation systems, smartphones, and driver assistance systems. Driving itself requires performing simultaneous tasks, including lane keeping, looking for signs, and avoiding pedestrians. The dangers of multitasking while driving, and efforts to combat it, often focus on the distraction itself, rather than on how a distracting task can change what the driver can perceive. Critically, some distracting tasks require the driver to look away from the road, which forces the driver to use peripheral vision to detect driving-relevant events. As a consequence, both looking away and being distracted may degrade driving performance. To assess the relative contributions of these factors, we conducted a laboratory experiment in which we separately varied cognitive load and point of gaze. Subjects performed a visual 0-back or 1-back task at one of four fixation locations superimposed on a real-world driving video, while simultaneously monitoring for brake lights in their lane of travel. Subjects were able to detect brake lights in all conditions, but once the eccentricity of the brake lights increased, they responded more slowly and missed more braking events. However, our cognitive load manipulation had minimal effects on detection performance, reaction times, or miss rates for brake lights. These results suggest that, for tasks that require the driver to look off-road, the decrements observed may be due to the need to use peripheral vision to monitor the road, rather than due to the distraction itself.

Predicting road scenes from brief views of driving video.

If a vehicle is driving itself and asks the driver to take over, how much time does the driver need to comprehend the scene and respond appropriately? Previous work on natural-scene perception suggests that observers quickly acquire the gist, but gist-level understanding may not be sufficient to enable action. The moving road environment cannot be studied with static images alone, and safe driving requires anticipating future events. We performed two experiments to examine how quickly subjects could perceive the road scenes they viewed and make predictions based on their mental representations of the scenes. In both experiments, subjects performed a temporal-order prediction task, in which they viewed brief segments of road video and indicated which of two still frames would come next after the end of the video. By varying the duration of the previewed video clip, we determined the viewing duration required for accurate prediction of recorded road scenes. We performed an initial experiment on Mechanical Turk to explore the space, and a follow-up experiment in the lab to address questions of road type and stimulus discriminability. Our results suggest that representations which enable prediction can be developed from brief views of a road scene, and that different road environments (e.g., city versus highway driving) have a significant impact on the viewing durations drivers require to make accurate predictions of upcoming scenes.

The relative impact of smartwatch and smartphone use while driving on workload, attention, and driving performance.

The impact of using a smartwatch to initiate phone calls on driver workload, attention, and performance was compared to smartphone visual-manual (VM) and auditory-vocal (AV) interfaces. In a driving simulator, 36 participants placed calls using each method. While task time and number of glances were greater for AV calling on the smartwatch vs. smartphone, remote detection task (R-DRT) responsiveness, mean single glance duration, percentage of long duration off-road glances, total off-road glance time, and percent time looking off-road were similar; the later metrics were all significantly higher for the VM interface vs. AV methods. Heart rate and skin conductance were higher during phone calling tasks than "just driving", but did not consistently differentiate calling method. Participants exhibited more erratic driving behavior (lane position and major steering wheel reversals) for smartphone VM calling compared to both AV methods. Workload ratings were lower for AV calling on both devices vs. VM calling.

The effects of visual crowding, text size, and positional uncertainty on text legibility at a glance.

Reading at a glance, once a relatively infrequent mode of reading, is becoming common. Mobile interaction paradigms increasingly dominate the way in which users obtain information about the world, which often requires reading at a glance, whether from a smartphone, wearable device, or in-vehicle interface. Recent research in these areas has shown that a number of factors can affect text legibility when words are briefly presented in isolation. Here we expand upon this work by examining how legibility is affected by more crowded presentations. Word arrays were combined with a lexical decision task, in which the size of the text elements and the inter-line spacing (leading) between individual items were manipulated to gauge their relative impacts on text legibility. In addition, a single-word presentation condition that randomized the location of presentation was compared with previous work that held position constant. Results show that larger text was more legible than smaller text. Wider leading significantly enhanced legibility as well, but contrary to expectations, wider leading did not fully counteract decrements in legibility at smaller text sizes. Single-word stimuli presented with random positioning were more difficult to read than stationary counterparts from earlier studies. Finally, crowded displays required much greater processing time compared to single-word displays. These results have implications for modern interface design, which often present interactions in the form of scrollable and/or selectable lists. The present findings are of practical interest to the wide community of graphic designers and interface engineers responsible for developing our interfaces of daily use.

Investigating the correspondence between driver head position and glance location.

The relationship between a driver's glance orientation and corresponding head rotation is highly complex due to its nonlinear dependence on the individual, task, and driving context. This paper presents expanded analytic detail and findings from an effort that explored the ability of head pose to serve as an estimator for driver gaze by connecting head rotation data with manually coded gaze region data using both a statistical analysis approach and a predictive (i.e., machine learning) approach. For the latter, classification accuracy increased as visual angles between two glance locations increased. In other words, the greater the shift in gaze, the higher the accuracy of classification. This is an intuitive but important concept that we make explicit through our analysis. The highest accuracy achieved was 83% using the method of Hidden Markov Models (HMM) for the binary gaze classification problem of (a) glances to the forward roadway versus (b) glances to the center stack. Results suggest that although there are individual differences in head-glance correspondence while driving, classifier models based on head-rotation data may be robust to these differences and therefore can serve as reasonable estimators for glance location. The results suggest that driver head pose can be used as a surrogate for eye gaze in several key conditions including the identification of high-eccentricity glances. Inexpensive driver head pose tracking may be a key element in detection systems developed to mitigate driver distraction and inattention.

Relationships Between Older Drivers' Cognitive Abilities as Assessed on the MoCA and Glance Patterns During Visual-Manual Radio Tuning While Driving.

Objective: Research has established that long off-road glances increase crash risk, and other work has shown increased off-road glance behavior in older drivers. This study investigated the relationship between older drivers' (M = 66.3, range 61-69 years) cognitive abilities and the duration of off-road glances while engaged in secondary visual-manual activities. Method: Twenty-two drivers completed the Montreal Cognitive Assessment (MoCA) prior to driving an instrumented vehicle and completing a set of radio-tuning tasks. Glance behavior was recorded and manually coded into 7 glance regions (toward the forward roadway, instrument cluster, center stack, rearview mirror, left, right, and other). Results: On average, older drivers with higher MoCA scores used shorter glances and glanced away from the forward roadway for less total time when manually tuning the radio. Discussion: These findings suggest that lower MoCA scores may represent a driving force behind the "age" differences reported in earlier studies of off-road glance behavior. Questions are raised concerning the identification of MoCA scores that might be used as inclusion cut-points in driving research and in identifying individuals needing further evaluation related to suitability for continuance of driving.

How safe is tuning a radio?: using the radio tuning task as a benchmark for distracted driving.

Drivers engage in non-driving tasks while driving, such as interactions entertainment systems. Studies have identified glance patterns related to such interactions, and manual radio tuning has been used as a reference task to set an upper bound on the acceptable demand of interactions. Consequently, some view the risk associated with radio tuning as defining the upper limit of glance measures associated with visual-manual in-vehicle activities. However, we have little knowledge about the actual degree of crash risk that radio tuning poses and, by extension, the risk of tasks that have similar glance patterns as the radio tuning task. In the current study, we use counterfactual simulation to take the glance patterns for manual radio tuning tasks from an on-road experiment and apply these patterns to lead-vehicle events observed in naturalistic driving studies. We then quantify how often the glance patterns from radio tuning are associated with rear-end crashes, compared to driving only situations. We used the pre-crash kinematics from 34 crash events from the SHRP2 naturalistic driving study to investigate the effect of radio tuning in crash-imminent situations, and we also investigated the effect of radio tuning on 2,475 routine braking events from the Safety Pilot project. The counterfactual simulation showed that off-road glances transform some near-crashes that could have been avoided into crashes, and glance patterns observed in on-road radio tuning experiment produced 2.85-5.00 times more crashes than baseline driving.