RoadCare: A Deep-learning Based Approach to Quantifying Road Surface Quality
TL;DR: This paper proposes a deep-learning based approach to road surface quality monitoring, using accelerometer and GPS sensor readings, which enables several useful smart-city applications such as spatio-temporal monitoring of the city's roads, early warning of bad road conditions, as well as choosing the "smoothest" road route to a destination.
Abstract: Roads form a critical part of any region's infrastructure. Their constant monitoring and maintenance is thus essential. Traditional monitoring mechanisms are heavy-weight, and hence have insufficient coverage. In this paper, we explore the use of crowd-sourced intelligent measurements from commuters' smart-phone sensors. Specifically, we propose a deep-learning based approach to road surface quality monitoring, using accelerometer and GPS sensor readings. Through extensive data collection of over 36 hours on different kinds of roads, and subsequent evaluation based on this, we show that the approach can achieve high accuracy (98.5%) in a three-way classification of road surface quality. We also show how the classification can be extended to a finer grained 11-point scale of road quality. The model is also efficient: it can be implemented on today's smart-phones, thus making it practical. Our approach, called RoadCare, enables several useful smart-city applications such as spatio-temporal monitoring of the city's roads, early warning of bad road conditions, as well as choosing the "smoothest" road route to a destination.
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TL;DR: Understanding of the occurrence and development of road traffic injuries will contribute to the prevention and control of crash and to the implementation of "everybody has the right to enjoy health" proposed by WHO.
Abstract: The appearance of cars has raised materialistic civilization and living standard to an unprecedented level. Today, it is hard to imagine how we human beings can live without cars. Yet, motor vehicles can cause a great number of deaths and injuries as well as considerable economic losses, which have constituted the global burden. Understanding of the occurrence and development of road traffic injuries will contribute to the prevention and control of crash and to the implementation of "everybody has the right to enjoy health" proposed by WHO.
312 citations
TL;DR: In this article, the authors proposed a short survey on the use of smartphone sensors in the detection of various kinds of anomalies in several fields namely environment, agriculture, healthcare and road/traffic conditions.
Abstract: With smartphones being so ubiquitous, more connected and largely fitted with several types of sensors such as GPS, microphones, cameras, magnetometers, accelerometers, etc; there is an increasing opportunity in the development of smartphone-based sensor systems. In this article, we propose a short survey on the use of smartphone sensors in the detection of various kinds of anomalies in several fields namely environment, agriculture, healthcare and road/traffic conditions. We also list the main advantages and limitations of the use of smartphone sensors systems in such fields.
36 citations
TL;DR: In this article , the authors describe the body of knowledge in smartphone-based roughness assessment, report knowledge gaps and cast light on future research directions. But, they focus on practical factors that are expected to affect the accuracy and robustness of smartphonebased methods, including data collection speed, vehicle type, smartphone specifications and mounting configuration.
11 citations
01 Mar 2018
8 citations
TL;DR: The results of this review show that road surface anomaly detection and classification performed through vibration-based methods have achieved relatively high performance, however, there are challenges related to the reproduction and heterogeneity of the results that have been reported that are influenced by the limited testing conditions, sample size, and lack of publicly available datasets.
Abstract: Road surfaces suffer from sources of deterioration, such as weather conditions, constant usage, loads, and the age of the infrastructure. These sources of decay generate anomalies that could cause harm to vehicle users and pedestrians and also develop a high cost to repair the irregularities. These drawbacks have motivated the development of systems that automatically detect and classify road anomalies. This study presents a narrative review focused on road surface anomaly detection and classification based on vibration-based techniques. Three methodologies were surveyed: threshold-based methods, feature extraction techniques, and deep learning techniques. Furthermore, datasets, signals, preprocessing steps, and feature extraction techniques are also presented. The results of this review show that road surface anomaly detection and classification performed through vibration-based methods have achieved relatively high performance. However, there are challenges related to the reproduction and heterogeneity of the results that have been reported that are influenced by the limited testing conditions, sample size, and lack of publicly available datasets. Finally, there is potential to standardize the features computed through the time or frequency domains and evaluate and compare the diverse set of settings of time-frequency methods used for feature extraction and signal representation.
6 citations
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TL;DR: The widespread DVRs are employed as distributed sensors with high mobility to conduct pervasive sensing of road anomalies and verify that the proposed system can effectively detect road anomalies in real time, showing its good feasibility in real-world environments.
Abstract: Advanced vehicle safety is an emerging issue appealed from the rapidly explosive population of car owners. Posing a remarkable safety threat, road anomalies not only damage vehicles but may also cause serious danger, especially at night or under bad visibility conditions. However, maintaining the quality of roadways has been a big challenge for municipalities around the world. Recently, the rapid development and reduced cost of digital cameras have made it economically feasible to deploy driving video recorders (DVRs) on vehicles. Thus, in this paper, we employ the widespread DVRs as distributed sensors with high mobility to conduct pervasive sensing of road anomalies. First, vehicle shakes are detected to infer the candidates of road anomalies. Then, we segment pavement regions, extract saliencies on the road surface, and classify whether a detected vehicle shake is caused by a road anomaly or an artificial speed bump. Experiments are conducted on a test data set collected by front-mounted DVRs, and the results verify that the proposed system can effectively detect road anomalies in real time, showing its good feasibility in real-world environments.
14 citations
"RoadCare: A Deep-learning Based App..." refers methods in this paper
...Chen et al [9, 10] have used Digital Video Recorders (DVRs) mounted at the front of the vehicle to develop a road anomaly detection and reporting system....
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01 Mar 2018
8 citations
01 Jan 2014
TL;DR: This study compared IRI from an inertial profiler to IRI calculated from a smartphone' s accelerometer over a 1 km test section of road in New Brunswick, Canada to evaluate the effects of varying the following experimental factors: vehicle type, device manufacturer, mounting arrangement and speed.
Abstract: International Roughness Index (IRI) is a widely used pavement performance measure collected with specially equipped vehicles; however, the cost of data collection may limit the ability of some road authorities to procure the data. Recent advances in smartphone technology have created interest in their potential to be low-cost mobile data collection platforms. This study compared IRI from an inertial profiler to IRI calculated from a smartphone' s accelerometer over a 1 km test section of road in New Brunswick, Canada. The study also included four scenario tests to evaluate the effects of varying the following experimental factors: vehicle type, device manufacturer, mounting arrangement and speed. The correlation between the smartphone' s results and those collected using the inertial profiler was found to be 88.9% for 100m increments along the section. The scenarios returned average IRI values ranging from 0.8% to 85% different than the average IRI of 2.60 m/km collected using the inertial profiler, though the smartphone configurations had higher coefficient of variations ranging from 2.05 to 9.11 compared to the inertial profiler's 1.12.
4 citations