Evaluating governance in a clinical and translational research organization.

Institutional Development Awards for Clinical and Translational Research (IDeA-CTR) networks, funded by NIH/NIGMS, aim to advance CTR infrastructure to address historically unmet state and regional health needs. Success depends on the response to actionable feedback to IDeA-CTR leadership from network partners and governance groups through annual surveys, interviews, and governance body recommendations. The Great Plains IDeA-CTR applied internal formative meta-evaluation to evaluate dispositions of 172 governance recommendations from 2017 to 2021. Results provided insights to improve the classification and quality of recommendations, credibility of evaluation processes, responsiveness to recommendations, and communications and governance in a complex CTR network comprising multiple coalitions.

Digital driving data can track driving exposure and quality of life in Parkinson's disease.

OBJECTIVE: Parkinson's disease (PD) impairs motor and non-motor functions. Driver strategies to compensate for impairments, like avoiding driving in risky environments, may reduce on-road risk at the cost of decreasing driver mobility, independence, and quality of life (QoL). It is unclear how PD symptoms link to driving risk exposure, strategies, and QoL. We assessed associations between PD symptoms and driving exposure (1) overall, (2) in risky driving environments, and (3) in relationship to QoL. METHODS: Twenty-eight drivers with idiopathic PD were assessed using the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and RAND 36-Item Short Form Health Survey (SF-36). Real-world driving was monitored for 1 month. Overall driving exposure (miles driven) and risky driving exposure (miles driven in higher risk driving environments) were assessed across PD symptom severity. High traffic, night, and interstate roads were considered risky environments. RESULTS: 18,642 miles (30,001 km) driven were collected. Drivers with PD with worse motor symptoms (MDS-UPDRS Part III) drove more overall (b = 0.17, P < .001) but less in risky environments (night: b = -0.35, P < .001; interstate roads: b = -0.23, P < .001; high traffic: b = -0.14, P < .001). Worse non-motor daily activities symptoms (MDS-UPDRS Part I) did not affect overall driving exposure (b = -0.05, P = .43) but did affect risky driving exposure. Worse non-motor daily activities increased risk exposure to interstate (b = 0.36, P < .001) and high traffic (b = 0.09, P = .03) roads while reducing nighttime risk exposure (b = -0.15, P = .01). Daily activity impacts from motor symptoms (MDS-UPDRS Part II) did not affect distance driven. Reduced driving exposure (number of drives per day) was associated with worse physical health-related QoL (b = 2.87, P = .04). CONCLUSIONS: Results provide pilot data revealing specific PD symptom impacts on driving risk exposure and QoL. Drivers with worse non-motor impairments may have greater risk exposure. In contrast, drivers with worse motor impairments may have reduced driver risk exposure. Reduced driving exposure may worsen physical health-related QoL. Results show promise for using driving to inform clinical care.

Real-World Driving Data Indexes Dopaminergic Treatment Effects in Parkinson's Disease.

Background: Driving is a complex, everyday task that impacts patient agency, safety, mobility, social connections, and quality of life. Digital tools can provide comprehensive real-world (RW) data on driver behavior in patients with Parkinson's disease (PD), providing critical data on disease status and treatment efficacy in the patient's own environment. Objective: This pilot study examined the use of driving data as a RW digital biomarker of PD symptom severity and dopaminergic therapy effectiveness. Methods: Naturalistic driving data (3974 drives) were collected for 1 month from 30 idiopathic PD drivers treated with dopaminergic medications. Prescriptions data were used to calculate levodopa equivalent daily dose (LEDD). The association between LEDD and driver mobility (number of drives) was assessed across PD severity, measured by the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Results: PD drivers with worse motor symptoms based on self-report (Part II: P = 0.02) and clinical examination (Part III: P < 0.001) showed greater decrements in driver mobility. LEDD levels >400 mg/day were associated with higher driver mobility than those with worse PD symptoms (Part I: P = 0.02, Part II: P < 0.001, Part III: P < 0.001). Conclusions: Results suggest that comprehensive RW driving data on PD patients may index disease status and treatment effectiveness to improve patient symptoms, safety, mobility, and independence. Higher dopaminergic treatment may enhance safe driver mobility in PD patients with worse symptom severity.

Vehicle Control as a Measure of Real-World Driving Performance in Patients With Rheumatoid Arthritis.

OBJECTIVE: To quantify vehicle control as a metric of automobile driving performance in patients with rheumatoid arthritis (RA). METHODS: Naturalistic driving assessments were completed in patients with active RA and controls without disease. Data were collected using in-car, sensor-based instrumentation installed in the participants' own vehicles to observe typical driving habits. RA disease status, disease activity, and functional status were associated with vehicle control (lateral [steering] and longitudinal [braking/accelerating] acceleration variability) using mixed-effect linear regression models stratified by road type (defined by roadway speed limit). RESULTS: Across 1,292 driving hours, RA drivers (n = 33) demonstrated differences in vehicle control compared to controls (n = 23), with evidence of significant statistical interaction between disease status and road type (P < 0.001). On residential roads, participants with RA demonstrated overall lower braking/accelerating variability than controls (P ≤ 0.004) and, when disease activity was low, lower steering variability (P = 0.03). On interstates/highways, RA was associated with increased steering variability among those with moderate/high Clinical Disease Activity Index scores (P = 0.04). In models limited to RA, increases in disease activity and physical disability over 12 weeks of observation were associated with a significant increase in braking/accelerating variability on interstate/highways (both P < 0.05). CONCLUSION: Using novel naturalistic assessments, we linked RA and worsening RA disease severity with aberrant vehicle control. These findings support the need for further research to map these observed patterns in vehicle control to metrics of driver risk and, in turn, to link patterns of real-world driving behavior to diagnosis and disease activity.

Strategy and safety at stop intersections in older adults with mild cognitive impairment and visual decline.

This study assessed the impact of age-related cognitive and visual declines on stop-controlled intersection stopping and scanning behaviors across varying roadway, traffic, and environmental challenges. Real-world driver data, collected from drivers' personal vehicles using in-vehicle sensor systems, was analyzed in 68 older adults (65-90 years old) with and without mild cognitive impairment (MCI) and with a range of age-related visual declines. Driver behavior, environmental characteristics, and traffic characteristic were examined across 2,596 approaches at 173 stop-controlled intersections. A mixed-effects logistic regression modeled stopping behavior as a binary response (full stop or rolling/no-stop). Overall, drivers who scanned more on intersection approaches (OR = 0.77) or had more visual decline (OR = 2.28) were more likely to make full stops at a stop-controlled approach. Drivers with a contrast sensitivity logMAR score > 0.8 showed the greatest probability of making a full stop compared across all drivers. Drivers without MCI were ~ 5 times more likely to come to a full stop when they scanned more (23 % versus 5 % when they scanned less) compared to drivers with MCI, who were only twice as likely to stop (14 % versus 6 % when they scanned less). Drivers were more likely to fully stop on two-lane roadways (1.5 %), during night (2.0 %), and at intersections with opposing vehicles (10.4 %). Findings illuminate how driver strategies interact with underlying impairment. While drivers with visual decline adopt strategies that may improve safety, when drivers with MCI adopt strategies it did not result in the same degree of improvement in stopping which may result in greater risk.

Sugar and stops in drivers with insulin-dependent type 1 diabetes.

BACKGROUND: Diabetes is a major public health challenge, affecting millions of people worldwide. Abnormal physiology in diabetes, particularly hypoglycemia, can cause driver impairments that affect safe driving. While diabetes driver safety has been previously researched, few studies link real-time physiologic changes in drivers with diabetes to objective real-world driver safety, particularly at high-risk areas like intersections. To address this, we investigated the role of acute physiologic changes in drivers with type 1 diabetes mellitus (T1DM) on safe stopping at stop intersections. METHODS: 18 T1DM drivers (21-52 years, µ = 31.2 years) and 14 controls (21-55 years, µ = 33.4 years) participated in a 4-week naturalistic driving study. At induction, each participant's personal vehicle was instrumented with a camera and sensor system to collect driving data (e.g., GPS, video, speed). Video was processed with computer vision algorithms detecting traffic elements (e.g., traffic signals, stop signs). Stop intersections were geolocated with clustering methods, state intersection databases, and manual review. Videos showing driver stop intersection approaches were extracted and manually reviewed to classify stopping behavior (full, rolling, and no stop) and intersection traffic characteristics. RESULTS: Mixed-effects logistic regression models determined how diabetes driver stopping safety (safe vs. unsafe stop) was affected by 1) disease and 2) at-risk, acute physiology (hypo- and hyperglycemia). Diabetes drivers who were acutely hyperglycemic (≥ 300 mg/dL) had 2.37 increased odds of unsafe stopping (95% CI: 1.26-4.47, p = 0.008) compared to those with normal physiology. Acute hypoglycemia did not associate with unsafe stopping (p = 0.537), however the lower frequency of hypoglycemia (vs. hyperglycemia) warrants a larger sample of drivers to investigate this effect. Critically, presence of diabetes alone did not associate with unsafe stopping, underscoring the need to evaluate driver physiology in licensing guidelines. CONCLUSION: This study links acute, abnormal physiologic fluctuations in drivers with diabetes to driver safety based on unsafe stopping at stop-controlled intersections, providing recommendations for clinicians aimed at improving patient safety, fair licensing guidelines, and targets for developing advanced driver assistance systems.

Vision, attention, and driving.

Safe driving demands the coordination of multiple sensory and cognitive functions, such as vision and attention. Patients with neurologic or ophthalmic disease are exposed to selective pathophysiologic insults to driving-critical systems, placing them at a higher risk for unsafe driving and restricted driving privileges. Here, we evaluate how vision and attention contribute to unsafe driving across different patient populations. In ophthalmic disease, we focus on macular degeneration, glaucoma, diabetic retinopathy, and cataract; in neurologic disease, we focus on Alzheimer's disease, Parkinson's disease, and multiple sclerosis. Unsafe driving is generally associated with impaired vision and attention in ophthalmic and neurologic patients, respectively. Furthermore, patients with ophthalmic disease experience some degree of impairment in attention. Similarly, patients with neurologic disease experience some degree of impairment in vision. While numerous studies have demonstrated a relationship between impaired vision and unsafe driving in neurologic disease, there remains a dearth of knowledge regarding the relationship between impaired attention and unsafe driving in ophthalmic disease. In summary, this chapter confirms-and offers opportunities for future research into-the contribution of vision and attention to safe driving.

Visual and Cognitive Impairments Differentially Affect Speed Limit Compliance in Older Drivers.

OBJECTIVES: We test the hypothesis that clinical measures of age-related cognitive, visual, and mobility impairments negatively impact older driver speed limit compliance to advance method developments that improve older patient care and screen, identify, and advise at-risk older drivers. DESIGN: Real-world driver behaviors of older adults who had a range of cognitive, visual, and mobility abilities (measured with standardized, clinical tests) were assessed in environmental context (e.g., speed limit, traffic density, roadway type). Older driver speed limit compliance was measured in constant speed limit zones and at transition zones, where speed limits changed. SETTING: A naturalistic driving study of older adults living around Omaha, Nebraska. PARTICIPANTS: Seventy-seven, legally licensed, active, and typically aging older drivers (65-90 years) who had a range of cognitive and visual abilities. MEASUREMENTS: Drivers typical, daily driving was continuously monitored for 3 months using sensor instrumentation installed in their own vehicles. At study start, each participant completed a comprehensive, standardized, clinical assessment of cognitive, visual, and mobility abilities relevant to aging and driving. RESULTS: Older drivers with greater cognitive impairment (P = .10) drove slower than drivers with less cognitive impairment, linking cognitive impairment to speed control. Drivers with greater visual impairment overall complied less with speed limit changes at transition zones (P = .01) and were more likely to comply with speed limit transitions when they occurred concurrently with changes in roadway features (P < .01). CONCLUSION: Results link clinical measures of age-related cognitive and visual impairment to impaired driver safety in real-world contexts. Real-world sensor data coupled with detailed, personalized older driver profiles can inform patients, caregivers, interventions, policy, and the design of supportive in-vehicle technology for at-risk older drivers.

Driving Ability and Safety in Rheumatoid Arthritis: A Systematic Review.

OBJECTIVE: To identify whether rheumatoid arthritis (RA) is associated with driving ability and/or the use of assistive devices or modifications to improve driving ability. METHODS: We conducted a systematic literature review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines of RA and driving ability/adaptations by searching multiple databases from inception to April 2018. Eligible studies were original articles in the English language that had quantitative data regarding the study objective and at least 5 RA patients. Similar outcomes were extracted across studies and grouped into categories for review. RESULTS: Our search yielded 1,935 potential reports, of which 22 fulfilled eligibility criteria, totaling 6,285 RA patients. The prevalence of driving issues in RA was highly variable among the studies. Some of the shared themes addressed in these publications included RA in association with rates of motor vehicle crashes, self-reported driving difficulty, inability to drive, use of driving adaptations, use of assistance by other people for transport, and difficulty with general transportation. CONCLUSION: Despite variability among individual reports, driving difficulties and the use of driving adaptations are relatively common in individuals with RA. Given the central importance of automobile driving for the quality of life of RA patients, further investigations of driving ability and potential driving adaptations that can help overcome barriers to safe driving are needed.

Glaucomatous visual fields and neurocognitive function are independently associated with poor lane maintenance during driving simulation.

BACKGROUND: Driving simulators are a safe alternative to on-road vehicles for studying driving behavior in glaucoma drivers. Visual field (VF) loss severity is associated with higher driving simulator crash risk, though mechanisms explaining this relationship remain unknown. Furthermore, associations between driving behavior and neurocognitive performance in glaucoma are unexplored. Here, we evaluated the hypothesis that VF loss severity and neurocognitive performance interact to influence simulated vehicle control in glaucoma drivers. METHODS: Glaucoma patients (n = 25) and suspects (n = 18) were recruited into the study. All had > 20/40 corrected visual acuity in each eye and were experienced field takers with at least three stable (reliability > 20%) fields over the last 2 years. Diagnosis of neurological disorder or cognitive impairment were exclusion criteria. Binocular VFs were derived from monocular Humphrey VFs to estimate a binocular VF index (OU-VFI). Montreal Cognitive Assessment (MoCA) was administered to assess global and sub-domain neurocognitive performance. National Eye Institute Visual Function Questionnaire (NEI-VFQ) was administered to assess peripheral vision and driving difficulties sub-scores. Driving performance was evaluated using a driving simulator with a 290° panoramic field of view constructed around a full-sized automotive cab. Vehicle control metrics, such as lateral acceleration variability and steering wheel variability, were calculated from vehicle sensor data while patients drove on a straight two-lane rural road. Linear mixed models were constructed to evaluate associations between driving performance and clinical characteristics. RESULTS: Patients were 9.5 years older than suspects (p = 0.015). OU-VFI in the glaucoma group ranged from 24 to 98% (85.6 ± 18.3; M ± SD). OU-VFI (p = .0066) was associated with MoCA total (p = .0066) and visuo-spatial and executive function sub-domain scores (p = .012). During driving simulation, patients showed greater steering wheel variability (p = 0.0001) and lateral acceleration variability (p < .0001) relative to suspects. Greater steering wheel variability was independently associated with OU-VFI (p = .0069), MoCA total scores (p = 0.028), and VFQ driving sub-scores (p = 0.0087), but not age (p = 0.61). CONCLUSIONS: Poor vehicle control was independently associated with greater VF loss and worse neurocognitive performance, suggesting both factors contribute to information processing models of driving performance in glaucoma. Future research must demonstrate the external validity of current findings to on-road performance in glaucoma.

Neurophysiologic and ophthalmic markers of chemotherapy-related cognitive impairment in patients diagnosed with hematologic cancer: A feasibility study.

BACKGROUND: Biomarkers of chemotherapy-related cognitive impairment (CRCI) in hematologic cancer are understudied and underdeveloped. We evaluated the feasibility of using ophthalmic and neurophysiologic markers to assess CRCI in hematologic cancer. METHODS: Hematologic cancer patients either receiving (Ctx+) or not receiving (Ctx-) chemotherapy were recruited from a tertiary medical center. Demographically-matched healthy controls (HC) were also recruited. Ctx+ participants completed the following study visits: (1) after diagnosis but prior to chemotherapy (baseline); (2) after one treatment cycle (one-month post-baseline); and (3) after three treatment cycles (three-months post-baseline). Comparison subjects completed assessments at similar intervals. Participants completed: (1) neuropsychological assessments of attention and executive function; (2) neurophysiologic assessments of control over spatial attention and working memory; and (3) ophthalmic assessments of contrast sensitivity and optical coherence tomography (OCT). RESULTS: We enrolled 45 participants (15 per group), and 30 participants (Ctx+ = 8; Ctx- = 10; HC = 12) completed all study visits. Ctx+ participants performed worse than HC participants on neuropsychological measures of attention and executive function. Both Ctx+ and Ctx- participants showed changes in neurophysiologic measures of control over spatial attention that differed from HC participants. Ctx+ participants showed chemotherapy-related declines in contrast sensitivity that were predicted by OCT retinal nerve fiber layer thickness (RNFL) changes. Changes in neurophysiologic measures of control over spatial attention were also predicted by OCT RNFL changes. CONCLUSION: We demonstrated the feasibility of using ophthalmic and neurophysiologic markers as rapid and non-invasive measures that may be useful for tracking CRCI in hematologic cancer.

Participatory needs assessment and action planning for a clinical and translational research network.

The goal of this study was to assess the utility of participatory needs assessment processes for continuous improvement of developing clinical and translational research (CTR) networks. Our approach expanded on evaluation strategies for CTR networks, centers, and institutes, which often survey stakeholders to identify infrastructure or resource needs, using the case example of the Great Plains IDeA-CTR Network. Our 4-stage approach (i.e., pre-assessment, data collection, implementation of needs assessment derived actions, monitoring of action plan) included a member survey (n = 357) and five subsequent small group sessions (n = 75 participants) to better characterize needs identified in the survey and to provide actionable recommendations. This participatory, mixed-methods needs assessment and strategic action planning process yielded 11 inter-related recommendations. These recommendations were presented to the CTR steering committee as inputs to develop detailed, prioritized action plans. Preliminary evaluation shows progress towards improved program capacity and effectiveness of the network to respond to member needs. The participatory, mixed-methods needs assessment and strategic planning process allowed a wide range of stakeholders to contribute to the development of actionable recommendations for network improvement, in line with the principles of team science.

Real-world risk exposure in older drivers with cognitive and visual dysfunction.

Objective: This study addresses the need to measure and monitor objective, real-world driver safety behavior in at-risk drivers with age-related dysfunction. Older drivers are at risk for age-related cognitive and visual dysfunction, which may reduce mobility and increase errors that lead to crashes. Understanding patterns of real-world behavior, exposure, and cognitive-perceptual processes underlying risk in environmental context and in older drivers requires new approaches.Methods: We assessed patterns of objective, real-world driver risk exposure and vehicle control related to steering, braking, and accelerating in older adults with a range of cognitive and visual functional abilities. Real-world driver behavior was collected from passive-monitoring systems installed in 77 drivers' vehicles and analyzed across 242,153 km (150,467 miles) driven. Driver behavior was assessed cross-sectionally in relationship to driver functional abilities and safety-critical environmental contexts (roadway type and visibility condition).Results: Results suggest that cognitive dysfunction impairs vehicle control across wide-ranging environments. Drivers with greater cognitive dysfunction showed more erratic braking and accelerating during daytime commercial and interstate driving. Drivers with less cognitive dysfunction showed more erratic braking and accelerating on residential roadways regardless of visibility condition. Greater cognitive dysfunction predicted more erratic steering on commercial and interstate roadways and less erratic steering on residential roadways. Greater visual dysfunction impaired braking and accelerating during nighttime and interstate driving, but not on residential or commercial roadways. Steering behavior was unaffected by visual abilities. Drivers with greater cognitive dysfunction did not appear to reduce driving frequency in higher-risk environments. Visually impaired drivers drove more on residential roadways and less on commercial roadways, but did not reduce driving on interstates, where they showed the greatest risk per mile driven.Conclusions: Results successfully mapped driver cognitive and visual profiles onto contemporaneous, real-world behavior and risk loci. Results link age-related dysfunction to real-world vehicle control and show that drivers may not sufficiently reduce exposure to higher-risk driving environments. Employing naturalistic observation to monitor and measure patterns of driver behavior can inform methods for early detection of age-related risk, fitness-to-drive assessments, and interventions to preserve safety, mobility, and quality of life in aging or other at-risk populations.

Quantifying vehicle control from physiology in type 1 diabetes.

Objective: Our goal is to measure real-world effects of at-risk driver physiology on safety-critical tasks like driving by monitoring driver behavior and physiology in real-time. Drivers with type 1 diabetes (T1D) have an elevated crash risk that is linked to abnormal blood glucose, particularly hypoglycemia. We tested the hypotheses that (1) T1D drivers would have overall impaired vehicle control behavior relative to control drivers without diabetes, (2) At-risk patterns of vehicle control in T1D drivers would be linked to at-risk, in-vehicle physiology, and (3) T1D drivers would show impaired vehicle control with more recent hypoglycemia prior to driving.Methods: Drivers (18 T1D, 14 control) were monitored continuously (4 weeks) using in-vehicle sensors (e.g., video, accelerometer, speed) and wearable continuous glucose monitors (CGMs) that measured each T1D driver's real-time blood glucose. Driver vehicle control was measured by vehicle acceleration variability (AV) across lateral (AVY, steering) and longitudinal (AVX, braking/accelerating) axes in 45-second segments (N = 61,635). Average vehicle speed for each segment was modeled as a covariate of AV and mixed-effects linear regression models were used.Results: We analyzed 3,687 drives (21,231 miles). T1D drivers had significantly higher overall AVX, Y compared to control drivers (BX = 2.5 × 10-2BY = 1.6 × 10-2, p < 0.01)-which is linked to erratic steering or swerving and harsh braking/accelerating. At-risk vehicle control patterns were particularly associated with at-risk physiology, namely hypo- and hyperglycemia (higher overall AVX,Y). Impairments from hypoglycemia persisted for hours after hypoglycemia resolved, with drivers who had hypoglycemia within 2-3 h of driving showing higher AVX and AVY. State Department of Motor Vehicle records for the 3 years preceding the study showed that at-risk T1D drivers accounted for all crashes (N = 3) and 85% of citations (N = 13) observed.Conclusions: Our results show that T1D driver risk can be linked to real-time patterns of at-risk driver physiology, particularly hypoglycemia, and driver risk can be detected during and prior to driving. Such naturalistic studies monitoring driver vehicle controls can inform methods for early detection of hypoglycemia-related driving risks, fitness to drive assessments, thereby helping to preserve safety in at-risk drivers with diabetes.

Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health.

This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.

Driving Safety and Real-Time Glucose Monitoring in Insulin-Dependent Diabetes.

Our goal is to address the need for driver-state detection using wearable and in-vehicle sensor measurements of driver physiology and health. To address this goal, we deployed in-vehicle systems, wearable sensors, and procedures capable of quantifying real-world driving behavior and performance in at-risk drivers with insulin-dependent type 1 diabetes mellitus (DM). We applied these methodologies over 4 weeks of continuous observation to quantify differences in real-world driver behavior profiles associated with physiologic changes in drivers with DM (N=19) and without DM (N=14). Results showed that DM driver behavior changed as a function of glycemic state, particularly hypoglycemia. DM drivers often drive during at-risk physiologic states, possibly due to unawareness of impairment, which in turn may relate to blunted physiologic responses (measurable heart rate) to hypoglycemia after repeated episodes of hypoglycemia. We found that this DM driver cohort has an elevated risk of crashes and citations, which our results suggest is linked to the DM driver's own momentary physiology. Overall, our findings demonstrate a clear link between at-risk driver physiology and real-world driving. By discovering key relationships between naturalistic driving and parameters of contemporaneous physiologic changes, like glucose control, this study directly advances the goal of driver-state detection through wearable physiologic sensors as well as efforts to develop "gold standard" metrics of driver safety and an individualized approach to driver health and wellness.

Road Weather Condition Estimation Using Fixed and Mobile Based Cameras.

Automated interpretation and understanding of the driving environment using image processing is a challenging task, as most current vision-based systems are not designed to work in dynamically-changing and naturalistic real-world settings. For instance, road weather condition classification using a camera is a challenge due to high variance in weather, road layout, and illumination conditions. Most transportation agencies, within the U.S., have deployed some cameras for operational awareness. Given that weather related crashes constitute 22% of all vehicle crashes and 16% of crash fatalities, this study proposes using these same cameras as a source for estimating roadway surface condition. The developed model is focused on three road surface conditions resulting from weather including: Clear (clear/dry), Rainy-Wet (rainy/slushy/wet), and Snow (snow-covered/partially snow-covered). The camera sources evaluated are both fixed Closed-circuit Television (CCTV) and mobile (snow plow dash-cam). The results are promising; with an achieved 98.57% and 77.32% road weather classification accuracy for CCTV and mobile cameras, respectively. Proposed classification method is suitable for autonomous selection of snow plow routes and verification of extreme road conditions on roadways.

Using Topic Modeling to Develop Multi-level Descriptions of Naturalistic Driving Data from Drivers with and without Sleep Apnea.

One challenge in using naturalistic driving data is producing a holistic analysis of these highly variable datasets. Typical analyses focus on isolated events, such as large g-force accelerations indicating a possible near-crash. Examining isolated events is ill-suited for identifying patterns in continuous activities such as maintaining vehicle control. We present an alternative approach that converts driving data into a text representation and uses topic modeling to identify patterns across the dataset. This approach enables the discovery of non-linear patterns, reduces the dimensionality of the data, and captures subtle variations in driver behavior. In this study topic models are used to concisely described patterns in trips from drivers with and without untreated obstructive sleep apnea (OSA). The analysis included 5000 trips (50 trips from 100 drivers; 66 drivers with OSA; 34 comparison drivers). Trips were treated as documents, and speed and acceleration data from the trips were converted to "driving words." The identified patterns, called topics, were determined based on regularities in the co-occurrence of the driving words within the trips. This representation was used in random forest models to predict the driver condition (i.e., OSA or comparison) for each trip. Models with 10, 15 and 20 topics had better accuracy in predicting the driver condition, with a maximum AUC of 0.73 for a model with 20 topics. Trips from drivers with OSA were more likely to be defined by topics for smaller lateral accelerations at low speeds. The results demonstrate topic modeling as a useful tool for extracting meaningful information from naturalistic driving datasets.