Increasing age and age-related health conditions can contribute to a higher risk of crash involvement and more severe physical injuries among older drivers. The current study uses in-vehicle sensing systems to analyze and cluster the overall driving behaviors of older drivers, precisely those aged 65 and older. The primary objective is to develop a framework that can help to observe patterns indicative of distinctive driving styles by clustering and interpreting similar patterns and leveraging Self-Organizing Maps (SOMs) and Deep Embedded Clustering (DEC) methods to reduce the complexity of the sensor data. The research uses two distinct groups of variables, including “speed”, “hard acceleration”, “hard braking” as constant variables, and “rpm”, “throttle positioning”, “fuel level”, “engine level”, and “ambient air temperature” as target variables from the in-vehicle sensor data in understanding driving behaviors and develops models in visualizing and interpreting complex driving patterns by classifying them. The results show that 5 × 5 grid SOMs have ability in visualizing multiple driving features concurrently, and DEC + K-means and DEC + agglomerative are the well-performed methods for determining the optimal number of clusters to analyze types of driving patterns. The clustering analysis identifies two distinct clusters as the optimal configuration, indicating that the predominant driving behavior among the targeted participants exhibits conservative patterns. The methodologies can be used for other driving features and demographics, making it relevant for broader applications in traffic analysis for understating and visualizing driving patterns.

Given a road network and a set of trajectory data, the anomalous behavior detection (ABD) problem is to identify drivers that show significant directional deviations, hardbrakings, and accelerations in their trips. The ABD problem is important in many societal applications, including Mild Cognitive Impairment (MCI) detection and safe route recommendations for older drivers. The ABD problem is computationally challenging due to the large size of temporally-detailed trajectories dataset. In this paper, we propose an Edge-Attributed Matrix that can represent the key properties of temporally-detailed trajectory datasets and identify abnormal driving behaviors. Experiments using real-world datasets demonstrated that our approach identifies abnormal driving behaviors.

Driving is a complex daily activity indicating age and disease related cognitive declines. Therefore, deficits in driving performance compared with ones without mild cognitive impairment (MCI) can reflect changes in cognitive functioning. There is increasing evidence that unobtrusive monitoring of older adults driving performance in a daily-life setting may allow us to detect subtle early changes in cognition. The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data, identifying important indicators for early changes in cognition, and detecting early-warning signs of cognitive impairment in a truly normal, day-to-day driving condition with machine learning approaches. Our statistical analysis comparing drivers with MCI to those without reveals that those with MCI exhibit smoother and safer driving patterns. This suggests that drivers with MCI are cognizant of their condition and tend to avoid erratic driving behaviors. Furthermore, our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation.

Background: Research to identify changes in driving behavior that occur with the onset of Pre-MCI and MCI is an emerging area with many gaps still to be addressed. These gaps include limited use of objective, continuous measurement of driver behavior in real-life traffic conditions and comprehensive, biomarker-validated, cognitive evaluation based upon both testing and clinical ratings. Using these strategies, the questions addressed in this exploratory study are whether or not differences in driving behavior are indicative of Pre-MCI/MCI and which behaviors are most predictive of Pre-MCI/MCI. Methods: As part of a naturalistic longitudinal study, older drivers with a Montreal Cognitive Assessment score ≥ 19 had telematic sensors installed in their vehicles and underwent comprehensive cognitive assessment quarterly for three years. Thirty-six participants were classified as Unimpaired (n = 23) or Pre-MCI/MCI (n = 10/3) based upon a neuropsychological battery and diagnostic algorithm. A penalized generalized linear mixed-effects model (GLMM) with a logistic link and LASSO regularization was used to model Pre-MCI/MCI group membership vs. unimpaired as a function of ten trip-level telematic features (trip distance, hard acceleration, hard braking, hard turns, speed average, maximum speed, RPM average, fuel level, throttle average, and throttle variability) at the end of their first 12 months in the study. Results: Higher RPM, shorter average trips, and greater throttle variability predicted higher odds of Pre-MCI/MCI, while more frequent hard braking, hard turns, higher mean speed, and lower average throttle (steadier pedal control) predicted lower odds of Pre-MCI/MCI. Conclusions: The model clearly distinguished unimpaired older drivers from those with MCI or Pre-MCI, suggesting that distinct patterns of driver behavior may be related to levels of cognitive function.