Big data analytics to identify deceleration characteristics of an older driver

Wallace, Bruce; Puli, Akshay; Goubran, Rafik; Knoefel, Frank; Porter, Michelle M.; Smith, Andrew

doi:10.1109/memea.2015.7145178

Cited by 15 publications

(12 citation statements)

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“…Driving is a very complex cognitive task including executive function tasks such as trip and route planning through to highly trained and learned tasks such as turn signal use. The measurement of driving is presented in three sections with an initial focus of measurement of driving behaviours that are unique to the driver, the measurement of navigational performance as an indication of cognitive ability and the measurement of turn signal use as one measure of vehicle operation (refereed journals [10] and conferences [12][13][14][15][16][17][18][19][20][21]).…”

Section: Driving Cognition Measurementmentioning

confidence: 99%

“…Blue -"Healthy" score and Yellow -"MCI" score with participant classified into the class with the larger score. 15 MCI subjects on the left and 17 HC subjects on the right. ........... 75 Figure 6-5: Decision scores for Weighted KNN using best 2 features from Table 6-5 for each of the 32 participants.…”

mentioning

confidence: 99%

“…Blue -"Healthy" score and Yellow -"MCI" score with participant classified into the class with the positive score. 15…”

mentioning

confidence: 99%

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An Integrated Approach for Episodic Cognition Assessment

Wallace¹

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Dementia and Mild Cognitive Impairment (MCI) are significant health issues that are a rising cost to society. They are monitored with cognitive tests during clinical appointments that are limited by the healthcare system capacity and patient's ability/willingness to attend. Current cognitive tests use behavioural measures and not direct measures of underlying cellular change. This causes delay in This work could never have been possible without the inspiration, support and acceptance of the long hours of work from my family including

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Section: Driving Cognition Measurementmentioning

confidence: 99%

mentioning

confidence: 99%

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An Integrated Approach for Episodic Cognition Assessment

Wallace¹

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“…A focused study of acceleration [19] for drivers with stable general, physical and cognitive health [20] that are sole drivers of their vehicles showed drivers have differing acceleration and deceleration behaviors [1,21,22]. This paper explores the trip level calculated features individually and in combination for their ability to allow two different drivers to be distinguished.…”

Section: Problem Statementmentioning

confidence: 99%

“…This includes the tendency a driver has to make trips at similar times of day, and measures of the percentage of a trip that occurs in each of the 24 hours of the day and also for each of the days of the week [18]. Drivers may also adjust their driving patterns based on other influences, such as avoiding high traffic that occurs at rush hour [22] or avoiding driving when it is dark [24]. The northern latitude location of Ottawa results in a large variation of the solar day over the year.…”

Section: Trip Features Measuredmentioning

confidence: 99%

Features that Distinguish Drivers: Big Data Analytics of Naturalistic Driving Data

2017

STBD

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The unique behaviours of drivers have many emerging applications. These include the personalization of automated/selfdriving vehicles so the owners are more comfortable with them, and the identification of changing driving behaviours that may be associated with aging or disease. This paper explores measures of driving behaviours that might allow for the differentiation of drivers based on their individual driving characteristics. An emerging challenge within longitudinal studies of drivers is to distinguish between different drivers of a shared vehicle. It also has application in the insurance industry where insurance risk and associated owner premium depends on the diversity or lack thereof of drivers for a vehicle such as a vehicle driven/never driven by secondary drivers that have higher risk driving behaviours. In this paper, a big data set of driving data for 14 older drivers is analyzed -a single year of data includes over 250,000 km and almost 5000 hours of driving for the 14 drivers. A set of 162 trip level calculated features are analyzed to determine their ability to be used to distinguish between two drivers of a vehicle. The results show that features based on road choice and driver chosen velocity provide the best performance individually and in feature pairs with 2 features providing error rates less than 5% for some driver pairs. The set of features that provided the best performance differed for each driver pair and was found to include features from measures of a driver's road choice, velocity and velocity ratio in addition to the features measuring trip similarity to two phase acceleration and deceleration relationships for the driver. The best error rate obtained was 1.5% for a driver pair. On the other hand, the results suggest that a number of features and feature groups do not allow for older driver differentiation. For instance, overnight driving and high rates of acceleration are not sufficiently exhibited by these drivers to be useful.

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