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.

Driver Maneuver Detection and Analysis Using Time Series Segmentation and Classification.

The current paper implements a methodology for automatically detecting vehicle maneuvers from vehicle telemetry data under naturalistic driving settings. Previous approaches have treated vehicle maneuver detection as a classification problem, although both time series segmentation and classification are required since input telemetry data are continuous. Our objective is to develop an end-to-end pipeline for the frame-by-frame annotation of naturalistic driving studies videos into various driving events including stop and lane-keeping events, lane changes, left-right turning movements, and horizontal curve maneuvers.

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.

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.

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.

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.

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.

Detecting and Tracking Unsafe Lane Departure Events for Predicting Driver Safety in Challenging Naturalistic Driving Data.

Our goal is to improve driver safety predictions in at-risk medical or aging populations from naturalistic driving video data. To meet this goal, we developed a novel model capable of detecting and tracking unsafe lane departure events (e.g.

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.

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

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.

Quantifying vehicle control from physiology in type 1 diabetes.

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.

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.

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).

Reducing Accelerometer Data from Instrumented Vehicles.

In on-road driving behavior studies, vehicle acceleration is sampled at high frequencies and then reduced to meaningful metrics over short driving segments. We examined road test data from 65 subjects driving over a common route, as well as driving in naturalistic situations using their own vehicle. We isolated 24-second segments, then reduced the accelerometer data via two methods: 1) standard deviation (SD) within a segment, and 2) re-centering parameter from a time series model previously developed for driving simulator data.

Neuromagnetic evidence for a featural distinction of English consonants: sensor- and source-space data.

Speech sounds can be classified on the basis of their underlying articulators or on the basis of the acoustic characteristics resulting from particular articulatory positions. Research in speech perception suggests that distinctive features are based on both articulatory and acoustic information. In recent years, neuroelectric and neuromagnetic investigations provided evidence for the brain's early sensitivity to distinctive features and their acoustic consequences, particularly for place of articulation distinctions.