Accelerometer tyre to estimate the aquaplaning state of the tyre-road contact

Niskanen, Arto; Tuononen, Ari J.

doi:10.1109/ivs.2015.7225709

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…However, except for class 2, the trained network can classify with a meaningful level of accuracy (over 80%) and especially for classes 3 and 4, an accuracy of over 94% is obtained. This result shows that even for straight driving, the A y signal has information that can distinguish the road surface type, and some studies also confirm this result [31]. As seen in Figure A1, although the waveform of A y does not have distinct waveform characteristics for each road surface compared to A x or A z , it can be distinguished to some extent with respect to the size and frequency of the signal.…”

Section: Training Results Of Dnns Using Acceleration Dataset Of Each Axismentioning

confidence: 56%

Intelligent Tire Sensor-Based Real-Time Road Surface Classification Using an Artificial Neural Network

Lee

Kim

et al. 2021

Sensors

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Vehicles today have many advanced driver assistance control systems that improve vehicle safety and comfort. With the development of more sophisticated vehicle electronic control and autonomous driving technology, the need and effort to estimate road surface conditions is increasing. In this paper, a real-time road surface classification algorithm, based on a deep neural network, is developed using a database collected through an intelligent tire sensor system with a three-axis accelerometer installed inside the tire. Two representative types of network, fully connected neural network (FCNN) and convolutional neural network (CNN), are learned with each of the three-axis acceleration sensor signals, and their performances were compared to obtain an optimal learning network result. The learning results show that the road surface type can be classified in real-time with sufficient accuracy when the longitudinal and vertical axis acceleration signals are trained with the CNN. In order to improve classification accuracy, a CNN with multiple input that can simultaneously learn 2-axis or 3-axis acceleration signals is suggested. In addition, by analyzing how the accuracy of the network is affected by number of classes and length of input data, which is related to delay of classification, the appropriate network can be selected according to the application. The proposed real-time road surface classification algorithm is expected to be utilized with various vehicle electronic control systems and makes a contribution to improving vehicle performance.

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Section: Training Results Of Dnns Using Acceleration Dataset Of Each Axismentioning

confidence: 56%

Intelligent Tire Sensor-Based Real-Time Road Surface Classification Using an Artificial Neural Network

Lee

Kim

et al. 2021

Sensors

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“…Among the papers that used signal preprocessing, about 60% of them used statistical techniques. In the frequency domain, the filters infinite impulse response [72] and Butterworth [28,49,52,64,71] were used in the removal of signal components. Already the Fast Fourier Transform (FFT) [3,17,18] and Wavelets [10,19,20,25,62,69] were used in the noise removal and feature extraction.…”

Section: Data Preprocessing Stepmentioning

confidence: 99%

“…Regarding proprioception, Niskanen and Tuononen [49] used a threshold approach to measure tire contact length, water contact and identify full aquaplaning. Matilainen and Tuononen [45] identified contact length of the rotation tire on dry and wet roads, useful information for aquaplaning recognition.…”

Section: Threshold-based Approachesmentioning

confidence: 99%

“…Euler angles Linear interpolation [40] Standard deviation Android library N/A [41] N/A N/A N/A [45] Low-pass zero-phase N/A N/A [48] N/A Rotation matrix N/A [49] Band-pass third-order Butterworth zero-phase N/A N/A [50] N/A Euler angles N/A [52] High-pass 3rd order Butterworth N/A N/A [53] Gaussian low pass N/A N/A [54] Standard deviation N/A N/A [55] N/A N/A N/A [59] Moving average N/A N/A [60] N/A N/A N/A [62] Low pass High pass Simple moving average (SMA) Lifting wavelet transform (LWT)…”

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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“…The result of the dynamic simulation is given in Figure 24, where the evolution of contact area with speed is calculated. In literature, the evolution of the contact area with respect to speed was presented experimentally by (Niskanen and Tuononen 2015), which concluded that there is no change in the contact area in dry condition. Similar results were obtained with multiphysical tyre model.…”

Section: Figure 23 Contact Patch Deflection At Different Road Roughnessmentioning

confidence: 99%

Multi-physical model for tyre–road contact – the effect of surface texture

Sharma

Bouteldja

Cerezo

2020

International Journal of Pavement Engineering

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The purpose of this paper is to develop a multi-physical tyre model, which can take into account the influence of temperature as well as the macro-texture of the road surface. The aim is to contribute to a better understanding and modeling of the tyre-road pavement interaction. This tyre-road interaction model can be used for friction estimation as well as rolling resistance estiomation. Indeed, tyre-road friction has an undeniable influence on the rolling tyre, an accurate measurement of the tyre-road interaction is necessary for better understanding of tyre design and simulations. The developed approach comprises mechanical and thermal models and gives dynamic forces and tyre temperatures as outputs. The comparison of simulation with experimental results is discussed to validate the model. Special focus is given to study the impact of macrotexture of road surface; the numerical and experimental results are discussed. The experiments were done on IFSTTAR test tracks.

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Accelerometer tyre to estimate the aquaplaning state of the tyre-road contact

Cited by 6 publications

References 9 publications

Intelligent Tire Sensor-Based Real-Time Road Surface Classification Using an Artificial Neural Network

Intelligent Tire Sensor-Based Real-Time Road Surface Classification Using an Artificial Neural Network

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

Multi-physical model for tyre–road contact – the effect of surface texture

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