Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning

Bustamante-Bello, Rogelio; García-Barba, Alec; Arce-Saenz, Luis A.; Curiel-Ramirez, Luis A.; Izquierdo-Reyes, Javier; Ramírez-Mendoza, Ricardo A.

doi:10.3390/s22020456

Cited by 20 publications

(3 citation statements)

References 38 publications

(34 reference statements)

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“…Smartphones X and Y, along with the Unex OBU and the RSU, operate at the edge of the hybrid network and process data within their respective local application. In addition, the ITS centre acts as an intermediary in the fog layer due to its proximity to the edge [17,48]. Table 6 describes each equipment computational environment.…”

Section: Experimental Evaluation and Results Analysismentioning

confidence: 99%

Protecting Hybrid ITS Networks: A Comprehensive Security Approach

Severino,

Simão,

Datia

et al. 2023

Future Internet

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Cooperative intelligent transport systems (C-ITS) continue to be developed to enhance transportation safety and sustainability. However, the communication of vehicle-to-everything (V2X) systems is inherently open, leading to vulnerabilities that attackers can exploit. This represents a threat to all road users, as security failures can lead to privacy violations or even fatalities. Moreover, a high fatality rate is correlated with soft-mobility road users. Therefore, when developing C-ITS systems, it is important to broaden the focus beyond connected vehicles to include soft-mobility users and legacy vehicles. This work presents a new approach developed in the context of emerging hybrid networks, combining intelligent transport systems operating in 5.9 GHz (ITS-G5) and radio-mobile cellular technologies. Two protocols were implemented and evaluated to introduce security guarantees (such as privacy and integrity) in communications within the developed C-ITS hybrid environment. As a result, this work securely integrates G5-connected ITS stations and soft-mobility users through a smartphone application via cellular networks. Commercial equipment was used for this goal, including on-board and roadside units. Computational, transmission and end-to-end latency were used to assess the system’s performance. Implemented protocols introduce an additional 11% end-to-end latency in hybrid communications. Moreover, workflows employing hybrid communications impose, on average, an extra 28.29 ms of end-to-end latency. The proposal shows promise, as it reaches end-to-end times below the latency requirements imposed in most C-ITS use cases.

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Section: Experimental Evaluation and Results Analysismentioning

confidence: 99%

Protecting Hybrid ITS Networks: A Comprehensive Security Approach

Severino,

Simão,

Datia

et al. 2023

Future Internet

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show abstract

“…Bustamante-Bello et al [31] introduce a novel approach for visualizing road pavement anomalies using fog computing in a vehicle-to-infrastructure (V2I) network and machine learning. In this research, they propose a method to efficiently process real-time road condition data and visualize surface anomalies promptly using fog computing, enhancing the ability to detect and respond to road anomalies.…”

Section: Vibration Sensor-based Machine Learning Methodsmentioning

confidence: 99%

A Road Defect Detection System Using Smartphones

Kim,

Kim

2024

Sensors

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We propose a novel approach to detecting road defects by leveraging smartphones. This approach presents an automatic data collection mechanism and a deep learning model for road defect detection on smartphones. The automatic data collection mechanism provides a practical and reliable way to collect and label data for road defect detection research, significantly facilitating the execution of investigations in this research field. By leveraging the automatically collected data, we designed a CNN-based model to classify speed bumps, manholes, and potholes, which outperforms conventional models in both accuracy and processing speed. The proposed system represents a highly practical and scalable technology that can be implemented using commercial smartphones, thereby presenting substantial promise for real-world applications.

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“…Multi-level SVM classifier, average TPR performance of 90% [37] Detect street anomalies using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network.…”

Section: Approach Challengesmentioning

confidence: 99%

Pothole and Plain Road Classification Using Adaptive Mutation Dipper Throated Optimization and Transfer Learning for Self Driving Cars

et al. 2022

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Self-driving car plays a crucial role in implementing traffic intelligence. Road smoothness in front of self-driving cars has a significant impact on the car's driving safety and comfort. Having potholes on the road may lead to several problems, including car damage and the occurrence of collisions. Therefore, self-driving cars should be able to change their driving behavior based on the real-time detection of road potholes. Various methods are followed to address this problem, including reporting to authorities, employing vibration-based sensors, and 3D laser imaging. However, limitations, such as expensive setup costs and the danger of discovery, affected these methods. Therefore, it is necessary to automate the process of potholes identification with sufficient precision and speed. A novel method based on adaptive mutation and dipper throated optimization (AMDTO) for feature selection and optimization of the random forest (RF) classifier is presented in this paper. In addition, we propose a new adaptive method for dataset balancing, referred to as optimized hashing SMOTE, to boost the performance of the optimized model. Data on potholes in different weather conditions and circumstances were collected and augmented before training the proposed model. The effectiveness of the proposed method is shown in experiments in classifying road potholes accurately. Eleven feature selection methods, including WOA, GWO, and PSO, and three machine learning classifiers were included in the conducted experiments to measure the superiority of the proposed method. The proposed method, AMDTO+RF, achieved a pothole classification accuracy of (99.795%), which outperforms the accuracy achieved by the other approaches, WOA+RF of 97.5%, GWO+RF of 98.6%, PSO+RF of 98.1%, and transfer learning approaches, AlexNet of 86.8%, VGG-19 of 87.3%, GoogLeNet of 90.4%, and ResNet-50 of 93.8%. In addition, an in-depth statistical analysis is performed on the recorded results to study the significance and stability of the proposed method.

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Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning

Cited by 20 publications

References 38 publications

Protecting Hybrid ITS Networks: A Comprehensive Security Approach

Protecting Hybrid ITS Networks: A Comprehensive Security Approach

A Road Defect Detection System Using Smartphones

Pothole and Plain Road Classification Using Adaptive Mutation Dipper Throated Optimization and Transfer Learning for Self Driving Cars

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