A survey on state-of-the-art road surface monitoring techniques for intelligent transportation systems

Chhabra, Rishu; Krishna, C. Rama; Verma, Seema

doi:10.1504/ijsnet.2021.118485

Cited by 10 publications

(4 citation statements)

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“…Current research on vibration signal recognition focuses on feature extraction and classification algorithms, such as IMF, wavelet analysis, and convolutional neural networks [3,8,[14][15][16][17][18]; however, the complex processing algorithms place high demands on the processing platform. For the application scenario of Intelligent manhole cover, the recognition method proposed in this paper has very little computation and can achieve digital IC autonomous data fusion, which effectively reduces the calculation of MCU, avoids the contradiction between the need for MCU to permanently fuse motion sensor data and the low-power requirement of the device, and reduces the power consumption of the system based on the recognition of target events.…”

Section: Manhole Coversmentioning

confidence: 99%

Research and Implementation of Low-Power Anomaly Recogni-Tion Method for Intelligent Manhole Covers

Guo¹,

Wang²,

Sun³

et al. 2023

Preprint

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This paper addresses the difficulty of balancing real-time response and low power consumption in intelligent manhole cover application scenarios. It proposes a method to distinguish normal and abnormal events by segmenting the boundary where the acceleration of the intelligent manhole cover deviates from a set threshold and lasts for a certain period, based on the difference in vibration patterns of the intelligent manhole cover when a normal event and an abnormal event occur. This paper uses digital output motion sensor data autonomous data fusion to implement the pattern mentioned above recognition algorithm, which reduces the MCU computing and working time and the overall power consumption of the system while meeting the real-time response requirements. The test results demonstrate that the method has a high rate of anomaly recognition accuracy. The method ensures the system's real-time response capability, and the actual low power consumption test demonstrates that the device can operate continuously for 9.5 years. The low power consumption index exceeds the requirements of the existing national standard, thereby resolving the issue that it is challenging to balance intelligent manhole cover abnormality recognition and low power consumption.

show abstract

Section: Manhole Coversmentioning

confidence: 99%

Research and Implementation of Low-Power Anomaly Recogni-Tion Method for Intelligent Manhole Covers

Guo¹,

Wang²,

Sun³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Current research on vibration signal recognition focuses on feature extraction and classification algorithms, such as IMF, wavelet analysis, and convolutional neural networks [3,8,[14][15][16][17][18]; however, the complex processing algorithms place high demands on the processing platform. For the application scenario of an intelligent manhole cover, the recognition method proposed in this paper has very little computation and can achieve digital IC autonomous data fusion, which effectively reduces the calculation of the MCU, avoids the contradiction between the need for the MCU to permanently fuse motion sensor data and the low-power requirement of the device, and reduces the power consumption of the system based on the recognition of target events.…”

Section: Manhole Coversmentioning

confidence: 99%

Research and Implementation of Low-Power Anomaly Recognition Method for Intelligent Manhole Covers

et al. 2023

View full text Add to dashboard Cite

This paper addresses the difficulty of balancing a real-time response and low power consumption in intelligent manhole cover application scenarios. It proposes a method to distinguish normal and abnormal events by segmenting the boundary at which the acceleration of the intelligent manhole cover deviates from a set threshold and lasts for a certain period, based on the difference in the intelligent manhole cover’s vibration patterns when a normal event and an abnormal event occur. This paper uses the autonomous data fusion of digital output motion sensor data to implement a pattern recognition algorithm for the above-mentioned pattern, which reduces the MCU computing and working time and the overall power consumption of the system while meeting real-time response requirements. The test results demonstrate that the method has a high rate of anomaly recognition accuracy. The method ensures the system’s real-time response capability, and the actual low power consumption test demonstrates that the device can operate continuously for 9.5 years. The low power consumption index exceeds the requirements of the existing national standard, thereby resolving the issue that it is challenging to balance intelligent manhole cover abnormality recognition and low power consumption.

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“…Traffic safety and efficiency are the main issues in the field of transportation [1,2]. An Intelligent Transportation System (ITS) effectively improves traffic safety and solves traffic congestion [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Traffic Sign Based Point Cloud Data Registration with Roadside LiDARs in Complex Traffic Environments

et al. 2022

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The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data on the roadside of intelligent roads. Furthermore, a fusion of multiple LiDARs has become a current hot spot to extend the data collection range and improve detection accuracy. This paper focuses on point cloud registration in a complex traffic environment and proposes a three-dimensional (3D) registration method based on traffic signs and prior knowledge of traffic scenes. Traffic signs with their reflective films are used as reference targets to register 3D point cloud data from roadside LiDARs. The proposed method consists of a vertical registration and a horizontal registration. For the vertical registration, we propose a panel rotation algorithm to rotate the initial point cloud to register it vertically, converting the 3D point cloud registration into a two-dimensional (2D) rigid body transformation. For the vertical registration, our system registers traffic signs from different LiDARs. Our method has been verified in some actual scenarios. Compared with previous methods, the proposed method is automatic and does not need to search reference targets manually. Furthermore, it is suitable for actual engineering use and can be applied to sparse point cloud data from LiDAR with few beams, realizing point cloud registration of large disparity.

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A survey on state-of-the-art road surface monitoring techniques for intelligent transportation systems

Cited by 10 publications

References 0 publications

Research and Implementation of Low-Power Anomaly Recogni-Tion Method for Intelligent Manhole Covers

Research and Implementation of Low-Power Anomaly Recogni-Tion Method for Intelligent Manhole Covers

Research and Implementation of Low-Power Anomaly Recognition Method for Intelligent Manhole Covers

Traffic Sign Based Point Cloud Data Registration with Roadside LiDARs in Complex Traffic Environments

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