Development of an automotive data acquisition platform for analysis of driving behavior

Andria, G.; Attivissimo, Filippo; Nisio, Attilio Di; Lanzolla, Anna Maria Lucia; Pellegrino, A.

doi:10.1016/j.measurement.2016.07.035

Cited by 56 publications

(23 citation statements)

References 22 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%

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%

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

Menegazzo

Wangenheim

2020

SN COMPUT. SCI.

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“…Measurement ranges of the accelerometer as well as the gyroscope are programmable in order to track movement accurately. The optional range of the accelerometer can be selected as ±2 g, ±4 g, ±8 g or ±16 g, and this of the gyroscope as ±250°/s, ±500°/s, ±1000°/s or ±2000°/s respectively [14]. Selection of the ranges depends on both specified condition of operation as well as a speed of motion.…”

Section: B Depth Calculationmentioning

confidence: 99%

Detection of the Trench Depth Based on a Differential Inertial Sensor Technology

Wang¹,

Peng²,

Miedziński³

et al. 2018

ElAEE

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The article proposes the use of a differential inertial sensors system for detection of the trench depth under operation. The inertial sensors are employed to detect a posture of the trencher and as a result to determine an angle difference through inertial sensors located on a vehicle (as a reference) and on the trencher arm. In this way, the undesirable, disturbing effects caused by unevenness of the field etc. are significantly reduced or even eliminated. The trench depth calculation procedure combines the angle difference and relative dimensions of the trencher structure. It is displayed on a monitor in real time. To perform a remote monitoring, the system is equipped with wireless transmission module using an upper-computer. To decrease data oscillation under operation, the calculated angles need to be filtered. Therefore, simulation based on Kalman filter in MATLAB was performed and found Q/R rate takes an important role in filtering and filtering test verified it. In field test, error rate between trench depth measured and detected is within 5 % what confirms the practical usefulness of the developed measuring system for the trench depth detection.

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“…In recent years, MEMS (Micro Electro-Mechanical System on a chip) technology enabled low cost production and widespread use in mobile and automotive applications. With single board computers (SBC) becoming widely available at a reasonable cost in the early 2010's there have been numerous successful implementations of SBC-based connected and distributed systems for measuring and monitoring various variables in traffic [5][6][7][8], environmental sciences [9][10][11][12], manufacturing [13,14], and environment automation [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Raspberry Pi-Based Low-Cost Connected Device for Assessing Road Surface Friction

et al. 2019

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Measuring friction between the tyres of a vehicle and the road, often and on as many locations on the road network as possible, can be a valuable tool for ensuring traffic safety. Rather than by using specialised equipment for sequential measurements, this can be achieved by using several low-cost measuring devices on vehicles that travel on the road network as part of their daily assignments. The presented work proves the hypothesis that a low cost measuring device can be built and can provide measurement results comparable to those obtained from expensive specialised measuring devices. As a proof of concept, two copies of a prototype device, based on the Raspberry Pi single-board computer, have been developed, built and tested. They use accelerometers to measure vehicle braking deceleration and include a global positioning receiver for obtaining the geolocation of each test. They run custom-developed data acquisition software on the Linux operating system and provide automatic measurement data transfer to a server. The operation is controlled by an intuitive user interface consisting of two illuminated physical pushbuttons. The results show that for braking tests and friction coefficient measurements the developed prototypes compare favourably to a widely used professional vehicle performance computer.

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Development of an automotive data acquisition platform for analysis of driving behavior

Cited by 56 publications

References 22 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

Detection of the Trench Depth Based on a Differential Inertial Sensor Technology

Raspberry Pi-Based Low-Cost Connected Device for Assessing Road Surface Friction

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