Road-types classification using audio signal processing and SVM method

Dogan, Daghan

doi:10.1109/siu.2017.7960154

Cited by 12 publications

(3 citation statements)

References 5 publications

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“…Although TPIN noise characterisation is most commonly performed in the frequency domain through the 1/n-octave bands [5,25,26,29,35], there are alternatives for noise representation that are capable of handling the subjective impression of frequency, such as Mel's triangular filter bank.…”

Section: Dataset Designmentioning

confidence: 99%

Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering

Ramos-Romero

Asensio

Moreno

et al. 2022

Sensors

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The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes.

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Section: Dataset Designmentioning

confidence: 99%

Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering

Ramos-Romero

Asensio

Moreno

et al. 2022

Sensors

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“…Alonso et al (2015) proposed a road classification system based on the real-time acoustic analysis of tire-road noise; the system could accurately identify dry and wet asphalt pavements. Dogan (2017) inserted strain sensors into the tire tread to measure tire deformation and estimated the friction coefficient of the road by comparing the deformation of the center and the edge of the contact surface. Boyraz and Dogan (2013) designed an intelligent road condition estimator based on acoustic sensors; this estimator, named the "Acoustic Road-Type Estimation" system, could effectively distinguish asphalt, gravel, snow and ice.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of the unscented Kalman filter and particle filter estimation methods for the measurement of the road adhesion coefficient

Fan²,

Li³

2022

Mech. Sci.

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Abstract. The measurement of the road adhesion coefficient is of great significance for the vehicle active safety control system and is one of the key technologies for future autonomous driving. With a focus on the problems of interference uncertainty and system nonlinearity in the estimation of the road adhesion coefficient, this work adopts a vehicle model with 7 degrees of freedom (7-DOF) and the Dugoff tire model and uses these models to estimate the road adhesion coefficient in real time based on the particle filter (PF) algorithm. The estimations using the PF algorithm are verified by selecting typical working conditions, and they are compared with estimations using the unscented Kalman filter (UKF) algorithm. Simulation results show that the road adhesion coefficient estimator error based on the UKF algorithm is less than 7 %, whereas the road adhesion coefficient estimator error based on the PF algorithm is less than 0.1 %. Thus, compared with the UKF algorithm, the PF algorithm has a higher accuracy and control effect with respect to estimating the road adhesion coefficient under different road conditions. In order to verify the robustness of the road adhesion coefficient estimator, an automobile test platform based on a four-wheel-hub-motor car is built. According to the experimental results, the estimator based on the PF algorithm can realize the road surface identification with an error of less than 1 %, which verifies the feasibility and effectiveness of the algorithm with respect to estimating the road adhesion coefficient and shows good robustness.

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“…y piedra). La clasicación se realizó mediante ANN y SVM, logrando una ecacia en la clasicación entre 67 % y 97, 5 %(Boyraz y Do §an, 2013;Boyraz, 2014;Do §an, 2017;Do §an y Boyraz, 2019).…”

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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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Road-types classification using audio signal processing and SVM method

Cited by 12 publications

References 5 publications

Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering

Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering

A comparative study of the unscented Kalman filter and particle filter estimation methods for the measurement of the road adhesion coefficient

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

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