Elderly driver retraining using automatic evaluation system of safe driving skill

Tada, Minoru; Noma, Hiroyasu; Utsumi, Akira; Segawa, Makoto; Okada, Masaya; Renge, Kazumi

doi:10.1049/iet-its.2013.0027

Cited by 15 publications

(9 citation statements)

References 13 publications

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“…Following a complete review of the corresponding full-texts, 29 papers were then selected based on the previously mentioned inclusion criteria. Seven (7) papers were not considered due to different situations [61][62][63][64][65][66][67], for example: the objective of the paper by Joanisse et al [61] was to report the findings from an evaluability assessment of the 55 alive mature driver-refresher course offered by the Canada Safety Council. Another example is the study by Musselwhite [63] where different issues were addressed through an expert group opinion identifying age related physiological and cognitive changes that may be involved in collisions.…”

Section: Formentioning

confidence: 99%

Are interventions effective at improving driving in older drivers?: A systematic review

et al. 2020

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Background: With the aging of the population, the number of older drivers is on the rise. This poses significant challenges for public health initiatives, as older drivers have a relatively higher risk for collisions. While many studies focus on developing screening tools to identify medically at-risk drivers, little research has been done to develop training programs or interventions to promote, maintain or enhance driving-related abilities among healthy individuals. The purpose of this systematic review is to synopsize the current literature on interventions that are tailored to improve driving in older healthy individuals by working on components of safe driving such as: selfawareness, knowledge, behaviour, skills and/or reducing crash/collision rates in healthy older drivers. Methods: Relevant databases such as Scopus and PubMed databases were selected and searched for primary articles published in between January 2007 and December 2017. Articles were identified using MeSH search terms: ("safety" OR "education" OR "training" OR "driving" OR "simulator" OR "program" OR "countermeasures") AND ("older drivers" OR "senior drivers" OR "aged drivers" OR "elderly drivers"). All retrieved abstracts were reviewed, and full texts printed if deemed relevant. Results: Twenty-five (25) articles were classified according to: 1) Classroom settings; 2) Computer-based training for cognitive or visual processing; 3) Physical training; 4) In-simulator training; 5) On-road training; and 6) Mixed interventions. Results show that different types of approaches have been successful in improving specific driving skills and/or behaviours. However, there are clear discrepancies on how driving performance/behaviours are evaluated between studies, both in terms of methods or dependent variables, it is therefore difficult to make direct comparisons between these studies. Conclusions: This review identified strong study projects, effective at improving older drivers' performance and thus allowed to highlight potential interventions that can be used to maintain or improve older drivers' safety behind the wheel. There is a need to further test these interventions by combining them and determining their effectiveness at improving driving performance.

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Section: Formentioning

confidence: 99%

Are interventions effective at improving driving in older drivers?: A systematic review

et al. 2020

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“…Moreover, in [16], they used raw data from accelerometer, gyroscope and magnetometer sensors of smart glasses to estimate seven specific head postures based on rotation angles of yaw, pitch, and roll (e.g., 'yaw right', 'yaw left', 'pitch right', and 'pitch left'). In this direction, inertial sensors and GPS have been used to develop a system in Japan that automatically evaluates safedriving skills during a training program [17]. The system consists of inertial sensors that have to be worn on drivers' head and a shoe to measure their motions, and then, a support vector machine (SVM) classifier decides if the visual scanning was appropriate for safe driving [17].…”

Section: Sensing Techniques To Infer Driving Behaviorsmentioning

confidence: 99%

“…In this direction, inertial sensors and GPS have been used to develop a system in Japan that automatically evaluates safedriving skills during a training program [17]. The system consists of inertial sensors that have to be worn on drivers' head and a shoe to measure their motions, and then, a support vector machine (SVM) classifier decides if the visual scanning was appropriate for safe driving [17].…”

Section: Sensing Techniques To Infer Driving Behaviorsmentioning

confidence: 99%

A Sensing Architecture Based on Head-Worn Inertial Sensors to Study Drivers’ Visual Patterns

Armenta

Rodríguez

Andrade

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Most studies on driving behaviors use video-cameras and simulators. It involves human observers to code the video data to be later analyzed, which can be a demanding task. We propose a sensing architecture to conduct studies on driving behaviors under naturalistic conditions. It includes smart glasses and a classifier algorithm to infer the vehicle’s cockpit’s spot drawing drivers’ visual attention. Thus, our architecture facilitates annotating the collected datasets with codes corresponding to classes of the cockpit’s spots. We have collected data with the sensing architecture from 15 young drivers to study how glances duration and frequency to cockpit’s spots are correlated with driving speed. Our results suggest that the incidence of drivers’ glances at all spots is less on high-speed roads than in low-speed roads. And that even though participants limited their interaction with the audio system, this is the spot that most eye fixation demanded to interact with.

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“…The ER data of near-misses is best utilized pro-active education that targets safer driving. An example of safe-driving education is to have drivers watch actual ER footage of near-miss traffic incidents [17]. In addition, near-miss incidents in ER data are attracting the attention of fleet management companies that need to control scores of commercial motor vehicles such as vans and trucks.…”

Section: Introductionmentioning

confidence: 99%

Identifying Near-Miss Traffic Incidents in Event Recorder Data

Yamamoto

Kurashima

Toda

2020

Advances in Knowledge Discovery and Data Mining

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Front video and sensor data captured by vehicle-mounted event recorders are used for not only traffic accident evidence but also safe-driving education as near-miss traffic incident data. However, most event recorder (ER) data shows only regular driving events. To utilize near-miss data for safe-driving education, we need to be able to easily and rapidly locate the appropriate data from large amounts of ER data through labels attached to the scenes/events of interest. This paper proposes a method that can automatically identify near-misses with objects such as pedestrians and bicycles by processing the ER data. The proposed method extracts two deep feature representations that consider car status and the environment surrounding the car. The first feature representation is generated by considering the temporal transitions of car status. The second one can extract the positional relationship between the car and surrounding objects by processing object detection results. Experiments on actual ER data demonstrate that the proposed method can accurately identify and tag near-miss events.

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Elderly driver retraining using automatic evaluation system of safe driving skill

Cited by 15 publications

References 13 publications

Are interventions effective at improving driving in older drivers?: A systematic review

Are interventions effective at improving driving in older drivers?: A systematic review

A Sensing Architecture Based on Head-Worn Inertial Sensors to Study Drivers’ Visual Patterns

Identifying Near-Miss Traffic Incidents in Event Recorder Data

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