A comparison of driving behaviour prediction algorithm using multi-sensory data on a smartphone

Pholprasit, Thunyasit; Choochaiwattana, Worasit; Saiprasert, Chalermpol

doi:10.1109/snpd.2015.7176249

Cited by 19 publications

(12 citation statements)

References 4 publications

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“…8, although other domains such as frequency and time-frequency have also been explored. Statistical approaches such as Gaussian [53], Skewness [4], Kurtosis [4], median [4,38], root mean square (RMS) [31,39,61], moving average/average [4,5,12,38,51,59,62], moving variance/variance [4,5] and standard deviation [5,10,28,40,51,54] were used in the time domain to smooth the signals and extract features in the vibration data. Among the papers that used signal preprocessing, about 60% of them used statistical techniques.…”

Section: Data Preprocessing Stepmentioning

confidence: 99%

“…Souza et al [64] used the techniques DTW, longest common subsequence similarity (LCSS), derivative dynamic time warping (DDDTW), derivative transform distance (DTDDTW) and complexity invariant distance (CID) to establish asphalt pavement regularity: regular or deteriorated; asphalt pavement type: flexible pavement, cobblestone streets and dirt roads; and asphalt obstacles: speed bump, vertical patch, raised pavement markers, and raised crosswalk. In proprioception, Pholprasit et al [51]…”

Section: Time Series Alignment Approachmentioning

confidence: 99%

“…Transformation matrix N/A [42] N/A N/A N/A [51] Simple moving average Standard deviation N/A N/A [56] N/A N/A N/A [57] N/A N/A N/A [61] Root mean square (RMS) Virtual reorientation N/A [68] N/A Euler angles N/A [71] Low-pass second-order Butterworth N/A N/A [72] Infinite impulse response Mapping the orientation N/A Table 13 Processing for proprioception…”

Section: Appendix 1: Summary Datamentioning

confidence: 99%

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Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

Menegazzo

Wangenheim

2020

SN COMPUT. SCI.

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In this paper, we present a structured literature mapping of the state-of-the-art of vehicular perception methods and approaches using inertial sensors. An in-depth investigation and classification were performed employing the results of a systematic literature review. The analysis focused on identifying methods that capture signals provided by inertial sensors such as accelerometers and gyroscopes to recognize transient or persistent events associated with the vehicle's movement. We classified these events into vehicular exteroception, associated with potholes, cracks, speed bumps, pavement type, conservation state; and vehicular proprioception, associated with lane change, braking, skidding, aquaplaning, turning right or left. Through the comprehensive study of publications in a 7-year time window, in addition to the methods, we have also identified their dependency factors, hardware platforms and applications.

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Section: Data Preprocessing Stepmentioning

confidence: 99%

Section: Time Series Alignment Approachmentioning

confidence: 99%

Section: Appendix 1: Summary Datamentioning

confidence: 99%

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Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

Menegazzo

Wangenheim

2020

SN COMPUT. SCI.

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“…[235] x x x x x Thresholding. [245] x x x x x x x Thresholding/DTW. [230] x x x x x x x Thresholding.…”

Section: Smartphone Sensorsmentioning

confidence: 99%

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Wahlström

Skog

Händel

2017

IEEE Trans. Intell. Transport. Syst.

157

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Abstract-Just like it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on userfriendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, cloud computing, vehicular ad hoc networks, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphonebased automotive navigation, and survey the state-of-the-art in smartphone-based transportation mode classification, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.

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“…Though there is no direct measure to detect the drowsiness but several indirect methods can be used for this purpose. In initial sections different types of methods for measuring the drowsiness of the driver are mentioned which includes Vehicle based measures, Physiological measures, Behavioral measures [3].Using those methods an intelligence system can be developed which would alert the driver incase drowsy condition and prevent accidents [4] [5]. Advantages and disadvantages corresponding to each and every systems are explained.…”

Section: Introductionmentioning

confidence: 99%

Driver’s Drowsiness Behaviour Detection by Using PSO/DPSO Algorithm for Urban Road System

J.Sudhakar¹,

Srinivasan²

2018

IJET

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In recent years driver fatigue is one of the major causes for vehicle accidents in the world. A direct way of measuring driver fatigue is measuring the state of the driver drowsiness. So it is very important to detect the drowsiness of the driver to save life and property. In our system, this aims to develop a prototype of drowsiness detection system. This system is a real time system which captures image continuously and measures the state of the eye according to the specified algorithm and gives warning if required. Though there are several methods for measuring the drowsiness but this approach is completely non-intrusive which does not affect the driver in any way, hence giving the exact condition of the driver. For detection of drowsiness the each closure value of eye is considered. So when the closure of eye exceeds a certain amount then the driver is identified to be sleepy. The entire system is implemented using PSO, DPSO and FODPSO algorithm and detection of drowsiness behaviour of driver different eye state level.

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A comparison of driving behaviour prediction algorithm using multi-sensory data on a smartphone

Cited by 19 publications

References 4 publications

Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

Driver’s Drowsiness Behaviour Detection by Using PSO/DPSO Algorithm for Urban Road System

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