Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach

Celaya-Padilla, José M.; Galván-Tejada, Carlos E.; López-Monteagudo, F. E.; Alonso-González, O.; Moreno-B́aez, Arturo; Martínez-Torteya, Antonio; Galván-Tejada, Jorge I.; Arceo-Olague, José G.; Luna-García, Huizilopoztli; Gamboa-Rosales, Hamurabi

doi:10.3390/s18020443

Cited by 72 publications

(36 citation statements)

References 23 publications

(19 reference statements)

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“…In order to improve the accuracy of road surface recognition, Wang [10] proposed a method by fusing the feature data from an acceleration sensor and camera to identify the abnormal road type. Celaya [11] installed sensors at the front of the vehicle to obtain the vehicle vibration response when the vehicle passed the speed bump and used the multivariate genetic algorithm to detect the road surface anomaly. This method can realize the recognition of abnormal road surfaces with a low false alarm rate, but the calculation is complicated, and a large number of statistical features such as mean, variance, peak, and standard deviation are needed for machine learning.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor

Qiu

Gao

et al. 2020

Sensors

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In order to identify the abnormal road surface condition efficiently and at low cost, a road surface condition recognition method is proposed based on the vibration acceleration generated by a smartphone when the vehicle passes through the abnormal road surface. The improved Gaussian background model is used to extract the features of the abnormal pavement, and the k-nearest neighbor (kNN) algorithm is used to distinguish the abnormal pavement types, including pothole and bump. Comparing with the existing works, the influence of vehicles with different suspension characteristics on the detection threshold is studied in this paper, and an adaptive adjustment mechanism based on vehicle speed is proposed. After comparing the field investigation results with the algorithm recognition results, the accuracy of the proposed algorithm is rigorously evaluated. The test results show that the vehicle vibration acceleration contains the road surface condition information, which can be used to identify the abnormal road conditions. The test result shows that the accuracy of the recognition of the road surface pothole is 96.03%, and the accuracy of the road surface bump is 94.12%. The proposed road surface recognition method can be utilized to replace the special patrol vehicle for timely and low-cost road maintenance.

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Section: Introductionmentioning

confidence: 99%

Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor

Qiu

Gao

et al. 2020

Sensors

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“…In Pothole lab [130], a new SVM(Z) and Swarm indices were developed to compare with the four thresholds in [62], Nericell, Pothole Patrol, and PERT [119]. Backward feature elimination was used in [131] to select the optimal set of features for different classification models while in [132] the forward selection and backwards elimination process was performed showing better performance than existing approaches.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Response-based methods to measure road surface irregularity: a state-of-the-art review

Nguyen

Lechner

Wong

2019

Eur. Transp. Res. Rev.

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Purpose: With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the stateof-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research. Methods: Available articles regarding response-based methods to measure road surface condition were collected mainly from "Scopus" database and partially from "Google Scholar". The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation. Results: The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking.

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“…The potholes had to be reported by more than five different users before publishing on the web map. Celaya-Padilla et al [21] utilized a different machine learning approach to check the existence of speed bumps. The authors first installed some hardware sensors (e.g., three-axis accelerometer and gyroscope) on a vehicle to measure vehicle vibration.…”

Section: Related Studiesmentioning

confidence: 99%

Embracing Crowdsensing: An Enhanced Mobile Sensing Solution for Road Anomaly Detection

Huo

Goldberg

et al. 2019

IJGI

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Road anomaly detection is essential in road maintenance and management; however, continuously monitoring road anomalies (such as bumps and potholes) with a low-cost and high-efficiency solution remains a challenging research question. In this study, we put forward an enhanced mobile sensing solution to detect road anomalies using mobile sensed data. We first create a smartphone app to detect irregular vehicle vibrations that usually imply road anomalies. Then, the mobile sensed signals are analyzed through continuous wavelet transform to identify road anomalies and estimate their sizes. Next, we innovatively utilize a spatial clustering method to group multiple driving tests’ results into clusters based on their spatial density patterns. Finally, the optimized detection results are obtained by synthesizing each cluster’s member points. Results demonstrate that our proposed solution can accurately detect road surface anomalies (94.44%) with a high positioning accuracy (within 3.29 meters in average) and an acceptable size estimation error (with a mean error of 14 cm). This study suggests that implementing a crowdsensing solution could substantially improve the effectiveness of traditional road monitoring systems.

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Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach

Cited by 72 publications

References 23 publications

Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor

Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor

Response-based methods to measure road surface irregularity: a state-of-the-art review

Embracing Crowdsensing: An Enhanced Mobile Sensing Solution for Road Anomaly Detection

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