Processing Acoustic Data with Siamese Neural Networks for Enhanced Road Roughness Classification

Gabrielli, Leonardo; Ambrosini, Livio; Vesperini, Fabio; Bruschi, Valeria; Squartini, Stefano; Cattani, Luca

doi:10.1109/ijcnn.2019.8852108

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…This indirect measurement method collects vehicle dynamics system parameters such as acceleration, displacement or noise of body and chassis, etc. as input variables, and uses neural networks [ 11 , 12 , 13 ], fuzzy rules [ 3 , 14 , 15 ], genetic algorithm [ 16 ], deep learning [ 17 , 18 , 19 ], etc., to establish nonlinear mapping models of the parameters to be estimated. Alternatively, the least squares method [ 20 , 21 , 22 ], Bayesian estimation [ 23 ], a Kalman filter [ 21 , 24 , 25 , 26 ], or other estimation criteria can be used to identify parameters, estimating the body mass, body side angle, road slope, slip rate, adhesion coefficient, driving style, and road roughness.…”

Section: Introductionmentioning

confidence: 99%

“…However, the network architecture used for road contour reconstruction requires a large amount of data for training to establish mapping relationships and rules, and online real-time control is difficult. In addition, acoustic sensors installed on vehicles are used to collect noise during vehicle running [ 13 , 19 ], and methods such as deep learning technology and support vector machine are used to classify road roughness, which can also realize road identification with high accuracy. However, the excessive number of road classifications requires too much algorithmic power to support.…”

Section: Introductionmentioning

confidence: 99%

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Study on Multi-Mode Switching Control Strategy of Active Suspension Based on Road Estimation

Liu

et al. 2023

Sensors

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In this paper, the least squares method is used to determine the vertical height of the road space domain. Based on the road estimation method, the active suspension control mode switching model is constructed, and the dynamic characteristics of the vehicle in comfort, safety, and integrated modes are analyzed. The vibration signal is collected by the sensor, and the parameters such as vehicle driving conditions are solved for in reverse. A control strategy for multiple mode switching under different road surfaces and speeds is constructed. At the same time, the particle swarm optimization algorithm (PSO) is used to optimize the weight coefficients of LQR control under different modes, and the dynamic performance of vehicle driving is comprehensively analyzed. The test and simulation results show that the road estimation results under different speeds in the same road section are very close to the results obtained by the detection ruler method, and the overall error is less than 2%. Compared with the active suspension controlled by passive and traditional LQR, the multi-mode switching strategy can achieve a better balance between driving comfort and handling safety and stability, and also improve the driving experience more intelligently and comprehensively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study on Multi-Mode Switching Control Strategy of Active Suspension Based on Road Estimation

Liu

et al. 2023

Sensors

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“…Intelligent control methods have been used to study the problem of road roughness detection. For example, Gabrielli et al [6] enhanced state of the art by introducing a Siamese Convolutional Neural Network architecture able to achieve improved results for the classification of the road surface roughness. Liu et al [7] proposed a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection to realize classification of different pavements.…”

Section: Introductionmentioning

confidence: 99%

Research on Road Roughness Based on NARX Neural Network

Liu

Cui

2021

Mathematical Problems in Engineering

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In order to solve the problem of road roughness identification, a study on the nonlinear autoregressive with exogenous inputs (NARX) neural network identification method was carried out in the paper. Firstly, a 7-DOF plane model of vehicle vibration system was established to obtain the vertical acceleration and elevation acceleration of the body, which were set as ideal input samples for the neural network. Then, based on the plane model, with common speed, the road roughness was solved as the ideal output sample of the NARX neural network, and the road roughness of B-level and C-level was identified. The results show that the proposed method has ideal identification accuracy and strong antinoise ability. The relative error of C-level road roughness is larger than that of B-level road roughness. The identified road roughness can provide a theoretical basis for analyzing the dynamic response of expressway roads.

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“…Continuando con este trabajo, Gabrielli et al (2019) incorporan una mejora en la clasicación supervisada del tipo de supercie mediante redes neuronales siamesas (SNN); alcanzando así una tasa de acierto promedio del 95, 5 % en la clasicación de la rugosidad de la supercie de la carretera. Posteriormente, Pepe et al (2019Pepe et al ( , 2021 ejecuta la clasicación simultanea de la vía en clases seco-mojado y rugosidad, fusionando los algoritmos desarrollados en un clasicador multitarea donde se logra un F1-score macro 94, 01 %.…”

Section: Confort Acústicounclassified

Exploración de las condiciones del pavimento a partir de la señal acústica producida por un vehículo circulando

Romero¹

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Hace ya casi cuatro años que inició este viaje académico, motivado por entender un poco más sobre un entorno que nunca deja de vibrar y sonar.Durante este proceso, siempre he contado con el apoyo incondicional y lleno de amor de Tatiana y Valentina. Gracias por estar conmigo, llenando mis días de vida y alegría, a pesar de lo apremiante que pudo llegar a ser esta travesía. Este logro es también suyo.Mi gratitud eterna es para mis Padres y Hermanos. Pese a que nos separan varios meridianos, esta etapa también es fruto de su tesón, guía, abnegación y entusiasmo.A toda mi familia, especialmente a mi primo Oscar. Su conanza también hizo posible que este camino se pudiera iniciar.Un agradecimiento especial a mi director, César Asensio. Ésta investigación ha requerido de él su paciencia e imprescindible dedicación.Quiero agradecer también al personal investigador y administrativo del Grupo de Investigación i2a2 por su asistencia y soporte, sin los cuales la consecución del presente trabajo habría visto jornadas muy complicadas. Gracias Guillermo, Ignacio y Eva. A los compañeros del Grupo i2a2: Pablo, Ricardo, Luis, Yolanda, Andrés, Daniel y Juan Manuel. Gracias por sus enseñanzas y su colaboración.Debo agradecer particularmente a Luis Sigcha, compañero desde las aulas del pregrado. Su don de gente, amistad y conocimiento han sido sin duda un regalo muy preciado.A los compañeros de Labenac por su soporte en las tareas instrumentales para la realización de varios experimentos.También agradezco a Mariana, Pepe, Nelson y Naty, quienes en más de una ocasión me brindaron el calor de su hogar con amabilidad y bondad.A los compañeros becarios, quienes han echo de esta estancia una experiencia más familiar, especialmente a Fredy, Zamir y Giovanny.

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Processing Acoustic Data with Siamese Neural Networks for Enhanced Road Roughness Classification

Cited by 10 publications

References 17 publications

Study on Multi-Mode Switching Control Strategy of Active Suspension Based on Road Estimation

Study on Multi-Mode Switching Control Strategy of Active Suspension Based on Road Estimation

Research on Road Roughness Based on NARX Neural Network

Exploración de las condiciones del pavimento a partir de la señal acústica producida por un vehículo circulando

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