MobiDriveScore &amp;#x2014; A system for mobile sensor based driving analysis: A risk assessment model for improving one's driving

Chakravarty, Tapas; Ghose, Avik; Bhaumik, Chirabrata; Chowdhury, Arijit

doi:10.1109/icsenst.2013.6727671

Cited by 28 publications

(15 citation statements)

References 8 publications

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“…An abnormal session is defined as a session of accelerometer readings, which contains more than five sequential peaks (greater than 0.38g m/s 2 ) per second. Through empirical tests and the results of the prior work described above, 0.38g m/s 2 is used as the threshold peak value to separate driving errors from normal driving behaviors (Chakravarty et al, ; Júnior et al, ; Zeeman & Booysen, ). Mean value of z‐axis gyroscope (mean z‐gyro) readings . The z ‐axis readings of the gyroscope reflect directly the vehicle rotation caused by performing turn or lane changes.…”

Section: Methodsmentioning

confidence: 99%

“…Threshold techniques have been used extensively in driving error detection. Chakravarty, Ghose, Bhaumik, and Chowdhury () created a mobile application (app) called MobiDriveScore to assess driving risk based on mobile‐sensed data. Three‐axis accelerometer and GPS data were utilized to capture and calculate the “jerk” energy caused by erratic driving behaviors such as hard cornering, stopping, or acceleration.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing driving safety: Discovering individualized hazardous driving scenes using GIS and mobile sensing

Goldberg

Chu

et al. 2019

Transactions in GIS

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Traffic collisions have been well acknowledged as a significant threat to public health, closely related to human driving errors. This study introduces an innovative approach to investigate spatiotemporal distributions of individualized driving errors and to characterize hazardous driving scenes, in which drivers are more prone to make driving mistakes. We first create a multi‐feature‐fusion framework to extract driving errors using smartphone sensors. Then, the detected errors are geo‐statistically analyzed with road networks and driving trajectories to identify driving error hotspots. We next construct a “scenic tuple” for representing the occurrence of driving errors. Finally, the individualized hazardous driving scenes are extracted by mining a long‐term collection of scenic tuples. Results demonstrate that our proposed approach can effectively identify driving errors. Additionally, the spatiotemporal patterns of driving mistakes can be identified from the individualized hazardous driving scenes, which has the potential to aid in reducing driving risks.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing driving safety: Discovering individualized hazardous driving scenes using GIS and mobile sensing

Goldberg

Chu

et al. 2019

Transactions in GIS

View full text Add to dashboard Cite

show abstract

“…The potential for orientation sensors to be used in a moving platform, like planes or vehicles, has been investigated based on static simulators [17]. A limited number of studies have also reported the use of small-scale inertia measurement units in a moving platform [20,21,22]. Solutions which use this approach to detect head movement, with an accuracy of <1° per axis, are commercially available [23], but they are costly and usually do not work on a moving platform.…”

Section: Methodsmentioning

confidence: 99%

An Orientation Sensor-Based Head Tracking System for Driver Behaviour Monitoring

Zhao

Görne

Yuen

et al. 2017

Sensors

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Although at present legislation does not allow drivers in a Level 3 autonomous vehicle to engage in a secondary task, there may become a time when it does. Monitoring the behaviour of drivers engaging in various non-driving activities (NDAs) is crucial to decide how well the driver will be able to take over control of the vehicle. One limitation of the commonly used face-based head tracking system, using cameras, is that sufficient features of the face must be visible, which limits the detectable angle of head movement and thereby measurable NDAs, unless multiple cameras are used. This paper proposes a novel orientation sensor based head tracking system that includes twin devices, one of which measures the movement of the vehicle while the other measures the absolute movement of the head. Measurement error in the shaking and nodding axes were less than 0.4°, while error in the rolling axis was less than 2°. Comparison with a camera-based system, through in-house tests and on-road tests, showed that the main advantage of the proposed system is the ability to detect angles larger than 20° in the shaking and nodding axes. Finally, a case study demonstrated that the measurement of the shaking and nodding angles, produced from the proposed system, can effectively characterise the drivers’ behaviour while engaged in the NDAs of chatting to a passenger and playing on a smartphone.

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“…Another approach is the grading of a driver on a “safety scale” based on driving style and driver behaviour analysis, and providing real-time information and feedback to assist in order to improve consciousness and promote safe driving [ 8 ]. The method of scoring a driver’s performance or risk is elaborated on further in other applications in particular insurance practices [ 27 , 42 , 43 ].…”

Section: Applications Assessing Driver Behaviourmentioning

confidence: 99%

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

Meiring

Myburgh

2015

Sensors

219

124

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In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

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MobiDriveScore — A system for mobile sensor based driving analysis: A risk assessment model for improving one's driving

Cited by 28 publications

References 8 publications

Enhancing driving safety: Discovering individualized hazardous driving scenes using GIS and mobile sensing

Enhancing driving safety: Discovering individualized hazardous driving scenes using GIS and mobile sensing

An Orientation Sensor-Based Head Tracking System for Driver Behaviour Monitoring

A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

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