Changes in driving patterns of older Australians: Findings from the Candrive/Ozcandrive cohort study

Charlton, Judith Lynne; Koppel, Sjaan; D'Elia, Angelo Dominic; Hua, Phuong; Louis, Renée M. St.; Dārziņš, Pēteris; Stefano, Marilyn Di; Odell, Morris; Porter, Michelle M.; Myers, Anita M.; Tuokko, Holly

doi:10.1016/j.ssci.2018.11.008

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…Those questionnaires (DPPQ and DHQ scale) are self-reported measures but since the patient and the caregiver both answer about the patient driving, we may control for most of the bias induced by such measures. Moreover, authors such as Charlton et al showed that results with objective methods about self-regulation of driving are coherent with self reported measures ( 24 ) which supports our use of those questionnaires.…”

Section: Methodssupporting

confidence: 82%

“…The self-regulation strategy in driving will be considered beneficial if the score of “Self-Regulatory Practice” increases on average by two points ( 24 ) 2 months from inclusion. The number of subjects was computed by considering a Student t -test for equal variances between the two groups, with a two-sided alpha risk of 5% and a power of 80%.…”

Section: Methodsmentioning

confidence: 99%

“…These results are consistent with those of Raedt and Ponjaert-Kristoffersen, which showed that adaptation strategies such as avoidance of certain situations (e.g., night-time trips, peak-hour trips, on unfamiliar roads, or on roads with rough or damaged surfaces) may reduce accident risk in older adults with no major cognitive disorders ( 23 ). Charlton et al also confirms that older adults engaged in self-regulatory driving strategies like reducing driving exposure (driving distance and/or frequency) ( 24 ). However, only 1 in 5 elderly drivers whose driving performance is declining over time correctly detect this change ( 25 ), which may hinder implementation of regulatory strategies.…”

Section: Introductionmentioning

confidence: 90%

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An innovative therapeutic educational program to support older drivers with cognitive disorders: Description of a randomized controlled trial study protocol

et al. 2022

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Older drivers face the prospect of having to adjust their driving habits because of health problems, which can include neurocognitive disorders. Self-awareness of driving difficulties and the interaction between individual with neurocognitive disorders and natural caregiver seem to be important levers for the implementation of adaptation strategies and for the subsequent voluntary cessation of driving when the cognitive disorders become too severe. This study aims to evaluate an educational program for patient/natural caregiver dyads who wish to implement self-regulation strategies in driving activity, and to improve self-awareness of driving ability. The ACCOMPAGNE program is based on seven group workshops, which target the dyad. The workshops deal with the impact of cognitive, sensory and iatrogenic disorders on driving. They tackle questions about responsibility, and about autonomy and social life. They also provide alternative solutions aimed at maintaining outward-looking activities even if driving is reduced or stopped. A randomized controlled trial is planned to evaluate the effectiveness of the program 2 months and 6 months after inclusion, and to compare this to the effectiveness of conventional approaches. The main outcome of this trial (i.e., the implementation of self-regulated driving strategies), will be measured based on scores on the “Current Self-Regulatory Practices” subscale of the Driver Perception and Practices Questionnaire. The Driving Habits Questionnaire will be used to measure secondary outcomes (indicators of driving changes; indicators of changes in mood, quality of life and caregiver burden; and self-awareness of driving abilities). Indicators will be collected for both patients and natural caregivers. This cognitive, social and psychological program should allow older individuals with cognitive disorders to drive more safely, and help to maintain the quality of life and mood of both patient and natural caregiver despite driving limitations. The patient's care path would be optimized, as he/she would become an actor in the process of giving up driving, which will, most certainly, be needed at some point in the progress of neurocognitive disorders. This process ranges from becoming aware of driving difficulties, to implementing self-regulation strategies, through to complete cessation of driving when necessary.Clinical trial registration numberNCT04493957.

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Section: Methodssupporting

confidence: 82%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 90%

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An innovative therapeutic educational program to support older drivers with cognitive disorders: Description of a randomized controlled trial study protocol

et al. 2022

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“…The Canadian Driving Research Initiative for Vehicular Safety in the Elderly (Candrive) conducted a similar but much larger study, with over 256 drivers, 80.5 million vehicle kilometers driven and 5 million hours of video-a total of approximately 2 petabytes of compressed data. The primary objective of the study was to identify prospectively older drivers who were medically unfit to drive (Charlton et al 2019). In addition to recorded videos, the study monitored participants' driving patterns by recording location data from a GPS, vehicle's speed, the position of the gas pedal, the engine's speed, and the air temperature.…”

Section: Naturalistic Driving Studiesmentioning

confidence: 99%

“…Other studies have investigated car-following behaviors of drivers using NDS data. The NHTSA's 100-car NDS study (100-Car Naturalistic Driving Study, 2006) was the first of many Barnard et al 2018.;Charlton et al 2019;Fridman et al 2019;Larue et al 2018) to collect video, radar, and vehicle telemetry data from a wide range of drivers while driving naturally. The data collection devices installed in the vehicles cost hundreds of thousands of dollars.…”

Section: Chapter 1: Introduction 1backgroundmentioning

confidence: 99%

AI-based framework for automatically extracting high-low features from NDS data to understand driver behavior

Aboah¹

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Our ability to detect and characterize unsafe driving behaviors in naturalistic driving environments and associate them with road crashes will be a significant step toward developing effective crash countermeasures. Due to some limitations, researchers have not yet fully achieved the stated goal of characterizing unsafe driving behaviors. These limitations include, but are not limited to, the high cost of data collection and the manual processes required to extract information from NDS data. In light of this limitations, the primary objective of this study is to develop an artificial intelligence (AI) framework for automatically extracting high-low features from the NDS dataset to explain driver behavior using a low-cost data collection method. The author proposed three novel objectives for achieving the study's objective in light of the identified research gaps. Initially, the study develops a low-cost data acquisition system for gathering data on naturalistic driving. Second, the study develops a framework that automatically extracts high- to low-level features, such as vehicle density, turning movements, and lane changes, from the data collected by the developed data acquisition system. Thirdly, the study extracted information from the NDS data to gain a better understanding of people's car-following behavior and other driving behaviors in order to develop countermeasures for traffic safety through data collection and analysis. The first objective of this study is to develop a multifunctional smartphone application for collecting NDS data. Three major modules comprised the designed app: a front-end user interface module, a sensor module, and a backend module. The front-end, which is also the application's user interface, was created to provide a streamlined view that exposed the application's key features via a tab bar controller. This allows us to compartmentalize the application's critical components into separate views. The backend module provides computational resources that can be used to accelerate front-end query responses. Google Firebase powered the backend of the developed application. The sensor modules included CoreMotion, CoreLocation, and AVKit. CoreMotion collects motion and environmental data from the onboard hardware of iOS devices, including accelerometers, gyroscopes, pedometers, magnetometers, and barometers. In contrast, CoreLocation determines the altitude, orientation, and geographical location of a device, as well as its position relative to an adjacent iBeacon device. The AVKit finally provides a high-level interface for video content playback. To achieve objective two, we formulated the problem as both a classification and time-series segmentation problem. This is due to the fact that the majority of existing driver maneuver detection methods formulate the problem as a pure classification problem, assuming a discretized input signal with known start and end locations for each event or segment. In practice, however, vehicle telemetry data used for detecting driver maneuvers are continuous; thus, a fully automated driver maneuver detection system should incorporate solutions for both time series segmentation and classification. The five stages of our proposed methodology are as follows: 1) data preprocessing, 2) segmentation of events, 3) machine learning classification, 4) heuristics classification, and 5) frame-by-frame video annotation. The result of the study indicates that the gyroscope reading is an exceptional parameter for extracting driving events, as its accuracy was consistent across all four models developed. The study reveals that the Energy Maximization Algorithm's accuracy ranges from 56.80 percent (left lane change) to 85.20 percent (right lane change) (lane-keeping) All four models developed had comparable accuracies to studies that used similar models. The 1D-CNN model had the highest accuracy (98.99 percent), followed by the LSTM model (97.75 percent), the RF model (97.71 percent), and the SVM model (97.65 percent). To serve as a ground truth, continuous signal data was annotated. In addition, the proposed method outperformed the fixed time window approach. The study analyzed the overall pipeline's accuracy by penalizing the F1 scores of the ML models with the EMA's duration score. The pipeline's accuracy ranged between 56.8 percent and 85.0 percent overall. The ultimate goal of this study was to extract variables from naturalistic driving videos that would facilitate an understanding of driver behavior in a naturalistic driving environment. To achieve this objective, three sub-goals were established. First, we developed a framework for extracting features pertinent to comprehending the behavior of natural-environment drivers. Using the extracted features, we then analyzed the car-following behaviors of various demographic groups. Thirdly, using a machine learning algorithm, we modeled the acceleration of both the ego-vehicle and the leading vehicle. Younger drivers are more likely to be aggressive, according to the findings of this study. In addition, the study revealed that drivers tend to accelerate when the distance between them and the vehicle in front of them is substantial. Lastly, compared to younger drivers, elderly motorists maintain a significantly larger following distance. This study's results have numerous safety implications. First, the analysis of the driving behavior of different demographic groups will enable safety engineers to develop the most effective crash countermeasures by enhancing their understanding of the driving styles of different demographic groups and the causes of collisions. Second, the models developed to predict the acceleration of both the ego-vehicle and the leading vehicle will provide enough information to explain the behavior of the ego-driver.

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User-centered AI-based voice-assistants for safe mobility of older people in urban context

Jnr.

2024

AI & Soc

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Voice-assistants are becoming increasingly popular and can be deployed to offers a low-cost tool that can support and potentially reduce falls, injuries, and accidents faced by older people within the age of 65 and older. But, irrespective of the mobility and walkability challenges faced by the aging population, studies that employed Artificial Intelligence (AI)-based voice-assistants to reduce risks faced by older people when they use public transportation and walk in built environment are scarce. This is because the development of AI-based voice-assistants suitable for the mobility domain presents several techno–social challenges. Accordingly, this study aims to identify user-centered service design and functional requirements, techno–social factors, and further design an architectural model for an AI-based voice-assistants that provide personalized recommendation to reduce falls, injuries, and accidents faced by older people. Accordingly, a scoping review of the literature grounded on secondary data from 59 studies was conducted and descriptive analysis of the literature and content-related analysis of the literature was carried out. Findings from this study presents the perceived techno-socio factors that may influences older people use of AI-based voice-assistants. More importantly, this study presents user-centred service design and functional requirements needed to be considered in developing voice-assistants suitable for older people. Implications from this study provides AI techniques for implementing voice-assistants that provide safe mobility, walkability, and wayfinding for older people in urban areas.

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Changes in driving patterns of older Australians: Findings from the Candrive/Ozcandrive cohort study

Cited by 10 publications

References 40 publications

An innovative therapeutic educational program to support older drivers with cognitive disorders: Description of a randomized controlled trial study protocol

An innovative therapeutic educational program to support older drivers with cognitive disorders: Description of a randomized controlled trial study protocol

AI-based framework for automatically extracting high-low features from NDS data to understand driver behavior

User-centered AI-based voice-assistants for safe mobility of older people in urban context

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