Monitoring Driver Behaviour with BackPocketDriver

Warren, Ian; Meads, Andrew; Wang, Chong; Whittaker, Robyn

doi:10.1007/978-3-030-27192-3_5

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…The application [11] uses unsupervised learning to create a driver profile which can be used later to provide recommendations and incentives to encourage the adoption of better driving style. Back-PocketDriver [20] is a similar application that confirms the accuracy of onboard sensors for monitoring driver behaviour. While [10] compares the performance various algorithms to identify aggressive driving behaviour.…”

Section: Introductionmentioning

confidence: 87%

“…Recent works on the design of ITS safety applications for smartphones are mostly aimed at monitoring of driver behaviour, road conditions, and pedestrian activity to avoid accidents. Examples of applications that studied driver behaviour include [11], [20], [10], [21], [7], and [22]. The application [11] uses unsupervised learning to create a driver profile which can be used later to provide recommendations and incentives to encourage the adoption of better driving style.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Vehicles’ Relative Position on Two-Lane Highways Through a Smartphone-Based Video Overtaking Aid Application

et al. 2020

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Here we present a smartphone-based realtime video overtaking aid for vehicular networks. The developed application aims to prevent head-on collisions that might occur due to attempts to overtake when the view of the driver is obstructed by the presence of a larger vehicle ahead. In such cases, the driver does not have a clear view of the road ahead and of any vehicles ahead that might be coming from the opposite direction, resulting in a high probability of accident occurrence. Our application relies on the use of dashboard mounted smartphones with the back camera facing the windshield and the screen towards the driver. A video is streamed from the vehicle ahead to the vehicle behind automatically, where it is displayed so that the driver can decide if it is safe to overtake. One of the major challenges is the way to elect the video source and destination among the vehicles in close proximity, depending on their relative position on road. For this purpose, we have focused on two different methods: the first relying solely on GPS data, which is faster but prone to errors due to GPS inaccuracies; the second method involving use of the camera and vehicle heading information, is accurate over short distances but is more computationally intensive.

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Section: Introductionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Detecting Vehicles’ Relative Position on Two-Lane Highways Through a Smartphone-Based Video Overtaking Aid Application

et al. 2020

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Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study

hafidy

Rachad

Idri

et al. 2021

Mobile Information Systems

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Many research works and official reports approve that irresponsible driving behavior on the road is the main cause of accidents. Consequently, responsible driving behavior can significantly reduce accidents’ number and severity. Therefore, in the research area as well as in the industrial area, mobile technologies are widely exploited in assisting drivers in reducing accident rates and preventing accidents. For instance, several mobile apps are provided to assist drivers in improving their driving behavior. Recently and thanks to mobile cloud computing, smartphones can benefit from the computing power of servers in the cloud for executing machine learning algorithms. Therefore, many mobile applications of driving assistance and control are based on machine learning techniques to adjust their functioning automatically to driver history, context, and profile. Additionally, gamification is a key element in the design of these mobile applications that allow drivers to develop their engagement and motivation to improve their driving behavior. To have an overview concerning existing mobile apps that improve driving behavior, we have chosen to conduct a systematic mapping study about driving behavior mobile apps that exist in the most common mobile apps repositories or that were published as research works in digital libraries. In particular, we should explore their functionalities, the kinds of collected data, the used gamification elements, and the used machine learning techniques and algorithms. We have successfully identified 220 mobile apps that help to improve driving behavior. In this work, we will extract all the data that seem to be useful for the classification and analysis of the functionalities offered by these applications.

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Monitoring Driver Behaviour with BackPocketDriver

Cited by 2 publications

References 22 publications

Detecting Vehicles’ Relative Position on Two-Lane Highways Through a Smartphone-Based Video Overtaking Aid Application

Detecting Vehicles’ Relative Position on Two-Lane Highways Through a Smartphone-Based Video Overtaking Aid Application

Gamified Mobile Applications for Improving Driving Behavior: A Systematic Mapping Study

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