Identification of Road Surface Friction for Vehicle Safety Systems

Arat, Mustafa Ali; Taheri, Saied

doi:10.4271/2014-01-0885

Cited by 3 publications

(8 citation statements)

References 25 publications

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“…Conversely, high-frequency methodologies pursue inferring the friction potential from the noise emitted by the rubber-road interface. Other solutions not included in these groups have been proposed to relate the tyre frequency responses (e.g., tyre circumferential acceleration) to the maximum friction potential [12,16,77]. In these cases, information from high and low-frequency vibration bands is employed.…”

Section: Vibration-based Road Friction Monitoringmentioning

confidence: 99%

“…These can be classified into longitudinal stiffness or slip slope-based approaches [6,7,17,44,91,92], friction model-based approaches [12,13,16,18,19,21,[93][94][95] and active force excitation approaches [71,96]. Despite slip slope approaches belong to the friction model-based group (i.e., linear longitudinal stiffness tyre model), a distinction has been made to treat them in greater detail.…”

Section: Longitudinal Dynamicsmentioning

confidence: 99%

“…The classifier, after post-processing, had an average precision and recall of respectively 95.1% and 90.6% and an accuracy of 91.8%. Finally in [12,16,77] a vibration-based approach employing low and high frequency information was proposed. A description of this approach is provided in [77] by Singh et al In this work, the authors instrumented a tyre with accelerometers attached to the inner tread.…”

Section: High Frequency Vibrationsmentioning

confidence: 99%

“…This will evidence the necessity of new approaches (e.g., tyre vibration-based) to estimate the maximum road grip during free-rolling conditions. Furthermore, this will facilitate the selection of the excitation thresholds required by future combined [17] or "friction fusion" strategies (e.g., vibration-based during coast down driving and slip-based during hard accelerations [12]). The next subsections follow the structure presented in Figure 12, and provide a comprehensive discussion on slip-based methods for longitudinal dynamics manoeuvres, lateral dynamics manoeuvres, and tyre self-alignment torque measurements.…”

Section: Slip-based Road Friction Monitoringmentioning

confidence: 99%

“…Specifically, it is envisaged that the accurate determination of such a coefficient can contribute to improve the performance of current vehicle systems such as Anti-Lock Braking System (ABS), Traction Control System (TCS), or Electronic Stability Program (ESP) [12][13][14]. An important drawback remarked by several authors is that a high excitation level (e.g., an emergency braking manoeuvre) is necessary in order to obtain an accurate estimation of the road grip potential when traditional slip-based approaches are employed [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

2017

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Abstract:In this paper, a review on road friction virtual sensing approaches is provided. In particular, this work attempts to address whether the road grip potential can be estimated accurately under regular driving conditions in which the vehicle responses remain within low longitudinal and lateral excitation levels. This review covers in detail the most relevant effect-based estimation methods; these are methods in which the road friction characteristics are inferred from the tyre responses: tyre slip, tyre vibration, and tyre noise. Slip-based approaches (longitudinal dynamics, lateral dynamics, and tyre self-alignment moment) are covered in the first part of the review, while low frequency and high frequency vibration-based works are presented in the following sections. Finally, a brief summary containing the main advantages and drawbacks derived from each estimation method and the future envisaged research lines are presented in the last sections of the paper.

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Section: Vibration-based Road Friction Monitoringmentioning

confidence: 99%

Section: Longitudinal Dynamicsmentioning

confidence: 99%

Section: High Frequency Vibrationsmentioning

confidence: 99%

Section: Slip-based Road Friction Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

2017

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A Multisensing Setup for the Intelligent Tire Monitoring

Coppo

Pepe

Roveri

et al. 2017

Sensors

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The present paper offers the chance to experimentally measure, for the first time, the internal tire strain by optical fiber sensors during the tire rolling in real operating conditions. The phenomena that take place during the tire rolling are in fact far from being completely understood. Despite several models available in the technical literature, there is not a correspondently large set of experimental observations. The paper includes the detailed description of the new multi-sensing technology for an ongoing vehicle measurement, which the research group has developed in the context of the project OPTYRE. The experimental apparatus is mainly based on the use of optical fibers with embedded Fiber Bragg Gratings sensors for the acquisition of the circumferential tire strain. Other sensors are also installed on the tire, such as a phonic wheel, a uniaxial accelerometer, and a dynamic temperature sensor. The acquired information is used as input variables in dedicated algorithms that allow the identification of key parameters, such as the dynamic contact patch, instantaneous dissipation and instantaneous grip. The OPTYRE project brings a contribution into the field of experimental grip monitoring of wheeled vehicles, with implications both on passive and active safety characteristics of cars and motorbikes.

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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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Identification of Road Surface Friction for Vehicle Safety Systems

Cited by 3 publications

References 25 publications

Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

A Multisensing Setup for the Intelligent Tire Monitoring

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

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