Description:FIELD OF DISCLOSURE
The present disclosure relates to road infrastructure monitoring systems, and more particularly to a system and method for road infrastructure monitoring using ultrasonic sensors.
BACKGROUND
Road infrastructure monitoring is a critical aspect of maintaining safe and efficient transportation systems. The condition of roads, particularly the presence of anomalies such as potholes, cracks, and other surface defects, can significantly impact vehicle safety, driver comfort, and overall transportation efficiency. Effective monitoring and timely detection of these road anomalies are essential for proactive maintenance and repair of road infrastructure.
Conventional methods for road infrastructure monitoring often rely on visual inspections conducted by human operators or specialized vehicles equipped with cameras and sensors. These methods can be time-consuming, labor-intensive, and may not provide real-time or continuous monitoring capabilities. Additionally, the accuracy and consistency of visual inspections can vary depending on the skill and experience of the operators. Some automated systems have been developed using technologies such as laser scanners, accelerometers, or image processing techniques. However, these systems may be complex, expensive to implement, or require significant computational resources for data analysis.
Furthermore, existing automated road monitoring systems may face challenges in accurately detecting and classifying various types of road anomalies under different environmental conditions. Factors such as varying lighting conditions, weather, and road surface materials can affect the reliability of detection methods. Moreover, many current systems may not provide localized information about detected anomalies, making it difficult for maintenance crews to quickly identify and address specific problem areas.
Therefore, there exists a need for a technical solution that solves the aforementioned problems of conventional systems and methods for road infrastructure monitoring.
SUMMARY
The summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. The summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
In an aspect of the present disclosure, a system for road infrastructure monitoring is disclosed. The system includes a rod (perforated) having a horizontal portion and upwardly bent ends at angles between 30 degrees to 45 degrees. The system further includes a plurality of ultrasonic sensors placed on the rod in different planes, including on the bent portions. The system further includes a database that stores predefined threshold values for each sensor of the plurality of sensors. The system further includes processing circuitry that is configured to receive distance values from each sensor of the plurality of sensors while a vehicle is moving. The processing circuitry further compares each received distance value with a corresponding prestored predefined threshold value. The processing circuitry further generates an alert when there is a variation between a received distance value and its corresponding prestored predefined threshold value. The alert indicates localized information about road anomalies.
In some aspects of the present disclosure, the processing circuitry is further configured to generate a report comprising information about detected road anomalies based on the alert generated.
In some aspects of the present disclosure, the plurality of ultrasonic sensors are arranged along the length of the rod to provide a wide coverage area for sensing road surface conditions.
In some aspects of the present disclosure, the predefined threshold values stored in the database indicate distances from the sensors to the road surface when the road is free of any anomalies.
In some aspects of the present disclosure, the processing circuitry is further configured to update the predefined threshold values in the database based on real-time measurements of road surface conditions.
In another aspect of the present disclosure, a method for road infrastructure monitoring is disclosed. The method includes providing a rod having a horizontal portion and upwardly bent ends at angles between 30 degrees to 45 degrees. The method further includes placing a plurality of ultrasonic sensors on the rod in different planes, including on the bent portions. The method further includes storing predefined threshold values for each sensor of the plurality of sensors in a database. The method further includes receiving distance values from each sensor of the plurality of sensors while a vehicle is moving. The method further includes comparing each received distance value with a corresponding prestored predefined threshold value. The method further includes checking for variation between each received distance value with the corresponding prestored predefined threshold value. The method further includes generating an alert when there is a variation between a received distance value and the corresponding prestored predefined threshold value. The alert indicates localized information about road anomalies.
In some aspects of the present disclosure, the method further includes generating a report comprising information about detected road anomalies based on the alerts generated.
In some aspects of the present disclosure, the predefined threshold values stored in the database indicate distances from the sensors to the road surface when the road does not have any anomalies.
In some aspects of the present disclosure, the method further includes updating the predefined threshold values in the database based on real-time measurements of road surface conditions.
In some aspects of the present disclosure, updating the predefined threshold values includes calculating an average of distance measurements from each sensor over a predetermined time period while the vehicle is moving on a road section known to be free of anomalies, and storing the calculated averages as updated predefined threshold values for each corresponding sensor in the database.
The foregoing general description of the illustrative aspects and the following detailed description thereof are merely exemplary aspects of the teachings of the disclosure and are not restrictive.
BRIEF DESCRIPTION OF FIGURES
The following detailed description of the preferred aspects of the present disclosure will be better understood when read in conjunction with the appended drawings. The present disclosure is illustrated by way of example, and not limited by the accompanying figures, in which like references indicate similar elements.
FIG. 1 illustrates a system for road infrastructure monitoring, according to aspects of the present disclosure;
FIG. 2 illustrates a block diagram of processing circuitry of the system of FIG. 1, according to aspects of the present disclosure; and
FIG. 3 illustrates a flowchart depicting a method for detecting road anomalies, according to aspects of the present disclosure.
DETAILED DESCRIPTION
The following description sets forth exemplary aspects of the present disclosure. It should be recognized, however, that such description is not intended as a limitation on the scope of the present disclosure. Rather, the description further encompasses combinations and modifications to those exemplary aspects described herein.
The present disclosure relates to a system and method for monitoring road infrastructure. The system may include a rod with upwardly bent ends at angles between 30 degrees to 45 degrees, providing an optimized placement for a plurality of ultrasonic sensors. The sensors are strategically positioned on different planes of the rod, including on the bent portions, to enhance the field of view for detecting road anomalies such as potholes much beyond the width of the vehicle on either side.
The system further may include a database that stores predefined threshold values for each sensor. The threshold values represent the distance from the sensors to the road surface when the road does not have any anomalies.
In addition, the system may include processing circuitry that receives distance values from each sensor while a vehicle may be moving. The processing circuitry may compare each received distance value with the corresponding prestored predefined threshold value. When there may be a variation between a received distance value and the corresponding prestored predefined threshold value, the processing circuitry may generate an alert. The alert may provide localized information about the detected road anomalies, enabling timely and targeted maintenance actions.
In some aspects, the processing circuitry may further generate a report comprising information about detected road anomalies based on the alerts generated. The report can provide valuable data for road maintenance planning and operations.
In some cases, the processing circuitry may further update the predefined threshold values in the database based on real-time measurements of road surface conditions. The feature may allow the system to adapt to changing road conditions and maintain accurate detection capabilities.
Overall, the system and method disclosed herein provide an efficient and cost-effective solution for road infrastructure monitoring, offering real-time detection of road anomalies and facilitating proactive road maintenance.
FIG. 1 illustrates a system 100 for road infrastructure monitoring, according to aspects of the present disclosure. The system 100 may include a rod 102, which has a horizontal portion and upwardly bent ends. In some aspects, the ends of the rod 102 may be bent at angles ranging from 30 degrees to 45 degrees. The distinctive configuration of the rod 102 may serve a crucial purpose in the road infrastructure monitoring system. The angled ends may provide an optimized placement for a plurality of ultrasonic sensors 108, enhancing their field of view and coverage area. By positioning the sensors on different planes, including the bent portions, the system may achieve a more comprehensive scan of the road surface. The design may allow for improved detection of road anomalies such as potholes, cracks, speed beakers, or other surface irregularities that may not be easily visible from a single angle. The strategic placement of sensors on the bent ends may further help to minimize blind spots and ensures a wider detection range, particularly for anomalies that may be located at the edges of the road or in areas that might be challenging to monitor with a purely horizontal sensor arrangement. Furthermore, the unique rod configuration may contribute to the system's efficiency and accuracy in real-time road condition monitoring, enabling more precise and timely detection of potential hazards or maintenance needs.
The plurality of ultrasonic sensors 108 may be strategically positioned on the rod 102 in various planes, including the bent portions at the ends. The innovative arrangement may serve multiple purposes in enhancing the system's effectiveness. By placing sensors on different planes, the system may achieve a more comprehensive coverage of the road surface, allowing for detection of anomalies that might be missed by a single-plane sensor array. The bent portions of the rod 102, angled between 30 to 45 degrees, may play a crucial role in the enhanced field of view. The angled sections may enable the sensors to capture data from a wider range of angles relative to the road surface, potentially identifying anomalies that would be challenging to detect from a purely horizontal perspective.
The distribution of sensors along the length of the rod 102 may further amplify the system's capabilities. The linear arrangement may ensure a broad lateral coverage of the road, allowing the system to monitor a significant width of the road surface simultaneously. Such extensive coverage may be particularly beneficial for detecting larger anomalies like potholes or widespread surface degradation that may span across the road. Additionally, the wide-area sensing capability may enables the system to maintain consistent monitoring even if the vehicle carrying the system deviates slightly from a straight path.
The combination of multi-plane positioning and longitudinal distribution of sensors may create a robust detection network. The network can effectively identify various types of road anomalies, from localized defects like small cracks or bumps to more extensive issues such as uneven surfaces or large potholes. The system's ability to provide comprehensive coverage may significantly enhance efficiency of the system in real-time road condition monitoring, enabling more accurate and timely detection of potential hazards or maintenance needs. The advanced sensor arrangement may be a key factor in the system's ability to generate precise, localized information about road anomalies, thereby facilitating targeted and efficient road maintenance efforts.
The system 100 further may include processing circuitry 110, which may be positioned centrally on the rod 102. The central positioning of the processing circuitry 110 may be strategically designed to optimize data collection and processing from the plurality of sensors 108. By placing the processing circuitry 110 at the center of the rod 102, the system ensures minimal signal degradation and latency in data transmission from the sensors to the processing circuitry 110, regardless of the position of sensors along the rod 102.
The processing circuitry 110 may be configured to perform several critical functions in real-time as the vehicle equipped with the system 100 may be in motion. Primarily, the processing circuitry 110 may receive distance values from each sensor of the plurality of sensors 108. The distance values represent the measured distances between each sensor of the plurality of sensors 108 and the road surface directly beneath each sensor of the plurality of sensors 108. The continuous stream of data from all sensors provides a comprehensive profile of the road surface as the vehicle travels.
Upon receiving the distance values, the processing circuitry 110 may engage in a crucial comparative analysis. The processing circuitry 110 may compare each received distance value with a corresponding prestored predefined threshold value. The threshold values, stored in the database 104, represent the expected distances from the sensors to the road surface under normal conditions, i.e., when the road does not have any anomalies. The comparison process may be fundamental to the system's ability to detect road anomalies.
If the processing circuitry 110 detects a variation between a received distance value and the corresponding prestored predefined threshold value, the processing circuitry 110 may interpret the variation as an indication of a potential road anomaly. The magnitude and nature of the variation can provide valuable information about the type and severity of the anomaly. For instance, a sudden, significant increase in the distance value might indicate a pothole, while a gradual, consistent deviation might suggest road surface degradation or unevenness.
Upon detecting such a variation, the processing circuitry 110 may be programmed to generate an alert. The alert may be not just a simple notification but contains localized information about the detected road anomalies. The localized nature of the information may be particularly valuable, as the localized nature of the information provides precise data about the location, extent, and potentially the type of the anomaly. The level of detail may be crucial for road maintenance teams, allowing them to quickly identify and address specific problem areas without the need for extensive manual inspections.
The alert generation capability of the processing circuitry 110 may transform the system from a passive monitoring tool into an active road infrastructure management solution. By providing real-time, localized alerts about road anomalies, the system may enable proactive maintenance approaches, potentially reducing repair costs and improving overall road safety.
The system 100 may employ ultrasonic sensors 108 as a key component for detecting road anomalies. The choice of sensor technology may offer several advantages in the context of road infrastructure monitoring. Ultrasonic sensors 108 may operate by emitting high-frequency sound waves that are inaudible to humans. The waves propagate through the air until they encounter an object or surface, in the present scenario, the road surface. Upon encountering the road surface, the sound waves are reflected back towards the sensor.
The ultrasonic sensors 108 may measure the time interval between the emission of the sound wave and the reception of its echo. The time-of-flight measurement may be then used to calculate the distance between the sensor and the road surface with high precision. The calculation may be based on the known speed of sound in air, which remains relatively constant under normal atmospheric conditions.
By continuously measuring the distances as the vehicle moves, the system 100 may create a detailed profile of the road surface. The processing circuitry 110 may analyze the stream of distance data in real-time and may compare the stream of distance data against the predefined threshold values stored in the database 104. Any significant deviations from the threshold values may indicate the presence of road anomalies such as potholes, cracks, or uneven surfaces.
The use of ultrasonic sensors 108 in the application offers several benefits. Firstly, they provide a cost-effective solution compared to more complex sensing technologies like LIDAR or high-resolution cameras. Ultrasonic sensors are relatively inexpensive to manufacture and maintain, making them suitable for widespread deployment in road monitoring systems.
Secondly, ultrasonic sensors offer reliable performance under various environmental conditions. They can operate effectively in low-light or dark conditions, which may be advantageous for round-the-clock monitoring. Additionally, they are less affected by visual obstructions such as dust, light rain, or fog compared to optical sensing methods.
The strategic placement of the ultrasonic sensors 108 on the rod 102, particularly on the bent portions, may further enhance the system's capabilities. The arrangement may allow for a wider field of view and more comprehensive coverage of the road surface, thereby enabling the detection of anomalies that might be missed by a single-plane sensor array.
By integrating the data from multiple ultrasonic sensors 108 positioned at different angles, the processing circuitry 110 may create a more accurate and detailed representation of the road surface. The multi-angle approach may improve the system's ability to detect and characterize various types of road anomalies, from small cracks to larger potholes, providing valuable information for timely road maintenance and repair.
The system 100 may represent a significant advancement in road infrastructure monitoring technology, offering a comprehensive and efficient solution to the challenges of detecting and managing road anomalies. At the core of the innovation may be the uniquely designed rod 102, featuring a horizontal portion with upwardly bent ends angled between 30 to 45 degrees. The distinctive configuration may serve a crucial purpose by optimizing the placement of the ultrasonic sensors 108, enhancing their field of view and coverage area.
The strategic positioning of the ultrasonic sensors 108 on different planes of the rod 102, including the bent portions, may allow for a more thorough and accurate assessment of the road surface. The multi-angle approach may enable the system to detect a wide range of anomalies, from small cracks to larger potholes, that might be missed by conventional monitoring methods. The arrangement of sensors along the length of the rod may further ensure broad lateral coverage of the road, further improving the system's ability to identify potential hazards or maintenance needs.
Central to the system's effectiveness may be the processing circuitry 110, positioned strategically on the rod 102. The centralized processing circuitry 110 receives and analyzes data from all sensors in real-time, comparing the incoming distance values with predefined threshold values stored in the database 104. The continuous, on-the-fly analysis allows for immediate detection of variations that may indicate road anomalies, enabling prompt alert generation with localized information about the detected issues.
The cost-effectiveness of the system 100 may be a significant advantage, particularly when compared to more complex and expensive road monitoring technologies. The use of ultrasonic sensors, known for their reliability and relatively low cost, may contribute to the system's affordability without compromising its performance. The use of ultrasonic sensors may therefore make the technology accessible for widespread deployment, potentially covering extensive road networks.
By facilitating real-time detection of road anomalies, the system 100 may enable a proactive approach to road maintenance. Rather than relying on scheduled inspections or reports of damage, road maintenance teams may receive immediate, localized alerts about developing issues. The timely information may allow for quicker response times, potentially preventing minor issues from escalating into more serious and costly problems.
In essence, the system 100 may represent a harmonious integration of innovative hardware design, strategic sensor placement, and intelligent data processing. The ability of the system 100 to provide continuous, real-time monitoring while remaining cost-effective positions the system 100 as a valuable tool for enhancing road safety, improving maintenance efficiency, and ultimately contributing to more sustainable and well-maintained road infrastructure.
Referring to FIG. 1, the system 100 further may include a database 104 and a communication network 106.
The database 104 may be a critical component of the road infrastructure monitoring system, designed to store and manage predefined threshold values for each sensor of the plurality of sensors 108. The threshold values serve as a baseline reference, representing the expected distances from the sensors 108 to the road surface under normal conditions, i.e., when the road may be free of anomalies such as potholes, cracks, or other surface irregularities. The baseline data may be essential for the system's ability to detect and identify road anomalies accurately.
The implementation of the database 104 may be flexible and may be adapted to various operational requirements. The database 104 may be configured as a local database, residing on the same hardware as the processing circuitry 110, which may provide faster access times and reduce reliance on network connectivity. Alternatively, the database 104 may be set up as a remote database, potentially allowing for centralized data management across multiple monitoring systems and facilitating data sharing and analysis on a larger scale.
The database 104 may leverage various data storage technologies to optimize performance and scalability. Relational databases may be employed for their robust data integrity and complex query capabilities, which could be beneficial for analyzing historical data and identifying trends in road conditions over time. NoSQL databases could be utilized for their flexibility in handling large volumes of sensor data and their ability to scale horizontally, which may be advantageous as the system expands to cover larger road networks. Distributed databases may be considered for deployments across vast geographical areas, enabling data replication and improving system resilience.
To enhance the system's efficiency and reliability, the database 104 may incorporate advanced data management features. Data indexing may significantly improve query performance, allowing for rapid retrieval of threshold values during real-time comparisons with sensor readings. Sophisticated query processing capabilities enable complex analysis of road condition data, potentially uncovering patterns or correlations that could inform predictive maintenance strategies. Data backup and recovery mechanisms ensure the integrity and availability of the threshold values and historical data, safeguarding against data loss due to hardware failures or other unforeseen events.
The database 104 may play a crucial role in the system's adaptability to changing road conditions. As the processing circuitry 110 may update the predefined threshold values based on real-time measurements, the database may efficiently handle frequent write operations while maintaining data consistency. The dynamic nature of the stored data may allow the system to refine its anomaly detection capabilities over time, adapting to gradual changes in road surfaces and environmental factors that may influence sensor readings.
The communication network 106 may play a crucial role in the system's functionality by enabling seamless data exchange between the processing circuitry 110 and the database 104. The network may be implemented using either wired or wireless technologies, offering flexibility in system deployment and integration. The support for various communication protocols, such as Ethernet, Wi-Fi, or cellular protocols, ensures compatibility with different infrastructure environments and allows for optimal data transmission based on specific deployment scenarios.
The wired network option may utilize Ethernet connections, providing high-speed, reliable data transfer within a fixed infrastructure. The wired network could be particularly useful in scenarios where the system may be installed in a permanent monitoring station or integrated into existing road management facilities. Wireless options like Wi-Fi offer greater flexibility in terms of system placement and mobility, potentially allowing for easier installation on vehicles or in areas where wired connections are impractical. Cellular protocols extend the flexibility further, enabling the system to operate over wide geographical areas and potentially facilitating real-time data transmission to centralized monitoring centers.
By facilitating the data exchange, the communication network 106 may enable two critical functions of the system. Firstly, the communication network 106 may allow the processing circuitry 110 to access the predefined threshold values stored in the database 104. The threshold values serve as the baseline for detecting road anomalies, representing the expected distances from the sensors to the road surface under normal conditions. The ability to quickly and reliably access the values may be essential for the real-time comparison and analysis performed by the processing circuitry 110.
Secondly, the communication network 106 may support the system's adaptive capabilities by enabling the processing circuitry 110 to update the threshold values in the database 104 based on real-time measurements of road surface conditions. The dynamic updating process may allow the system to maintain accuracy over time, adapting to gradual changes in road conditions, environmental factors, or sensor performance. The network's capacity to handle the bidirectional data flows may ensure that the system remains calibrated and effective in its road anomaly detection capabilities.
The robustness and reliability of the communication network 106 are paramount to the overall performance of the road infrastructure monitoring system. The communication network 106 must be capable of handling continuous data streams from multiple sensors, facilitating rapid data processing, and supporting timely alert generation. The choice of specific network technologies and protocols can be tailored to meet the particular requirements of each deployment scenario, balancing factors such as data transfer speed, reliability, range, and power consumption.
The processing circuitry 110 may be configured to receive distance values from each sensor of the plurality of sensors 108 via the communication network 106. The distance values may represent the measured distances from the sensors 108 to the road surface, and may be continuously updated as the vehicle moves. The processing circuitry 110 may compare each received distance value with the corresponding prestored predefined threshold value retrieved from the database 104. If there may be a variation between a received distance value and the corresponding prestored predefined threshold value, the processing circuitry 110 may generate an alert. The alert may provide localized information about the detected road anomalies, enabling timely and targeted maintenance actions.
In some aspects, the processing circuitry 110 may further update the predefined threshold values in the database 104 based on real-time measurements of road surface conditions. The feature may allow the system 100 to adapt to changing road conditions and maintain accurate detection capabilities. The processing circuitry 110 may calculate an average of distance measurements from each sensor 108 over a predetermined time period while the vehicle may be moving on a road section known to be free of anomalies, and store the calculated averages as updated predefined threshold values for each corresponding sensor 108 in the database 104. The dynamic updating of threshold values may enhance the system's ability to accurately detect road anomalies under varying road conditions.
Overall, the combination of the rod 102 with its unique configuration, the plurality of sensors 108 with their strategic placement, the database 104 with its stored threshold values, the communication network 106 facilitating data exchange, and the processing circuitry 110 performing real-time data analysis and alert generation, may form a comprehensive system 100 for efficient and accurate road infrastructure monitoring.
Referring to FIG. 2, the processing circuitry 110 of the system 100 may be illustrated in more detail. The processing circuitry 110 may include several interconnected components, each serving a specific function in the road infrastructure monitoring process.
The data collection engine 200 may be a key component of the processing circuitry 110. In some aspects, the data collection engine 200 may be configured to receive distance values from each sensor of the plurality of sensors 108 while a vehicle may be moving. The distance values, representing the measured distances from the sensors 108 to the road surface, may be continuously updated as the vehicle travels along the road. The data collection engine 200 may gather the stream of distance data from the sensors 108 and may forward the stream of distance data to an anomalies detection engine 202 for further processing.
The anomalies detection engine 202 may be another critical component of the processing circuitry 110. In some cases, the anomalies detection engine 202 may be configured to compare each received distance value with a corresponding prestored predefined threshold value retrieved from the database 104. The threshold values may represent the expected distances from the sensors 108 to the road surface under normal conditions, i.e., when the road does not have any anomalies. By comparing the real-time sensor readings with the threshold values, the anomalies detection engine 202 may identify variations that may indicate the presence of road anomalies such as potholes, cracks, or uneven surfaces.
Upon detecting a variation between a received distance value and its corresponding prestored predefined threshold value, the anomalies detection engine 202 may communicates the information to an alert generation engine 204. The alert generation engine 204 may be configured to generate an alert based on the detected variation. The alert may be not just a simple notification but contains localized information about the detected road anomalies. The localized nature of the information may be particularly valuable, as it provides precise data about the location, extent, and potentially the type of the anomaly. The level of detail may be crucial for road maintenance teams, allowing them to quickly identify and address specific problem areas without the need for extensive manual inspections.
Adjacent to the alert generation engine 204 may be the report generation engine 206. In some aspects, the report generation engine 206 may be configured to compile information about detected road anomalies into reports based on the alerts generated by the alert generation engine 204. The reports may provide valuable data for road maintenance planning and operations, offering a comprehensive overview of the road conditions and highlighting areas that require attention.
The components of the processing circuitry 110 may be interconnected by a data bus 208. The data bus 208 facilitates communication and data transfer between the different engines within the processing circuitry 110. The interconnected architecture may allow efficient data flow and processing within the system 100. Data collected by the data collection engine 200 may be analyzed by the anomalies detection engine 202. Based on the analysis, the alert generation engine 204 may produce alerts, while a report generation engine 206 may create detailed reports of the findings. The seamless integration of data collection, analysis, alert generation, and report creation may enhance the system's ability to monitor road infrastructure effectively and efficiently.
Referring to FIG. 3, a flowchart depicting a method 300 for detecting road anomalies using the ultrasonic sensor system may be illustrated. The method 300 begins with step 302, where a rod 102 with upwardly bent ends at angles between 30 degrees to 45 degrees and a horizontal portion may be provided. The rod 102 serves as the mounting structure for the plurality of ultrasonic sensors 108, that may be strategically placed on different planes of the rod 102, including on the bent portions, as indicated in step 304. The unique arrangement of sensors 108 may enhance the field of view for detecting road anomalies and may provide a comprehensive scan of the road surface.
In step 306, predefined threshold values for each sensor of the plurality of sensors 108 may be stored in a database 104. The threshold values represent the expected distances from the sensors 108 to the road surface when the road does not have any anomalies. The threshold values serve as a baseline for detecting variations in the road surface conditions.
The method 300 then advances to step 308, where distance values may be received from each sensor of the plurality of sensors 108 as the vehicle moves. The distance values, representing the measured distances from the sensors 108 to the road surface, are continuously updated as the vehicle travels along the road.
In step 310, the received distance values may be compared with the prestored predefined threshold values. The comparison may be performed by the processing circuitry 110, which analyzes the incoming sensor data in real-time. If there may be a variation between a received distance value and the corresponding prestored predefined threshold value, the variation may indicate the presence of a road anomaly.
A decision point may be reached at step 312, where the method 300 determines if there may be a variation between the measured and threshold values. If no variation may be detected, the process moves to step 318 to continue monitoring. The continuous monitoring may allow the system 100 to maintain a real-time profile of the road surface conditions, enabling timely detection of any emerging anomalies.
If a variation may be detected, the method 300 may proceed to step 314, where an alert may be generated. The alert may be generated by the processing circuitry 110 and may provide localized information about the detected road anomalies. The localized nature of the information may be particularly valuable, as it provides precise data about the location, extent, and potentially the type of the anomaly. The level of detail may be crucial for road maintenance teams, allowing them to quickly identify and address specific problem areas without the need for extensive manual inspections.
Further, at step 316, the report may be generated that provides localized information about detected road anomalies based on the alerts generated. The step may further enhance the system's ability to provide detailed and actionable information about road conditions, facilitating targeted and efficient road maintenance efforts.
In some aspects, the method 300 may further include a step 318 to calculate an average of distance measurements from each sensor 108 over a predetermined time period while the vehicle may be moving on a road section known to be free of anomalies. The calculated average may then be stored as an updated predefined threshold value for each corresponding sensor 108 in the database 104. The dynamic updating of threshold values may enhance the system's ability to accurately detect road anomalies under varying road conditions.
Overall, the method 300 may provide a comprehensive and efficient approach to road infrastructure monitoring, leveraging the unique configuration of the rod 102, the strategic placement of the sensors 108, and the real-time data processing capabilities of the processing circuitry 110. By continuously monitoring the road surface conditions and comparing the sensor readings with predefined threshold values, the method 300 may enable timely detection of road anomalies and facilitates proactive road maintenance.
In some aspects, the system 100 may integrate Arduino technology for data processing. Arduino is a popular open-source electronics platform that is known for its ease of use and flexibility, making it an ideal choice for implementing the processing circuitry 110 in the system 100. The Arduino platform may be programmed to perform a variety of tasks, including receiving and processing data from the plurality of sensors 108, comparing the received data with the prestored predefined threshold values, and generating alerts when anomalies are detected.
The integration of Arduino technology into the system 100 may enhance ability of the system 100 to process data in real-time. As the vehicle moves, the Arduino-based processing circuitry 110 may continuously receive distance values from each sensor of the plurality of sensors 108. The distance values, which represent the measured distances from the sensors 108 to the road surface, are processed immediately upon receipt. The real-time processing capability enables the system 100 to detect road anomalies as soon as they occur, providing timely alerts that may facilitate prompt maintenance actions.
In some cases, the Arduino-based processing circuitry 110 may further be configured to update the predefined threshold values in the database 104 based on real-time measurements of road surface conditions. The dynamic updating of threshold values may allow the system 100 to adapt to changing road conditions and maintain accurate detection capabilities. The Arduino platform's flexibility and programmability make it well-suited to handle the dynamic data processing tasks.
The use of Arduino technology may further enhance the overall efficiency of the system 100. Arduino boards are known for their low power consumption, which can be a significant advantage in a mobile system like the system 100 that may be powered by a vehicle's electrical system. Furthermore, the Arduino platform's open-source nature and extensive support community may facilitate the development and optimization of the processing circuitry 110, potentially leading to further improvements in system efficiency and performance.
In summary, the integration of Arduino technology into the system 100 may provide a flexible, efficient, and real-time data processing solution that enhances the system's ability to monitor road infrastructure and detect anomalies. The integration may represent a key aspect of the system 100's innovative approach to road infrastructure monitoring.
Thus, the system 100 and the method 300 provides several technical advantages:
The unique configuration of the rod with upwardly bent ends at 30-45 degree angles may enable optimized placement of ultrasonic sensors in multiple planes, enhancing the field of view and coverage area for detecting road anomalies. The strategic sensor arrangement may allow for more comprehensive and accurate scanning of road surfaces compared to single-plane sensor arrays.
Real-time processing of sensor data using Arduino technology may facilitate immediate detection and localization of road anomalies as the vehicle moves. The immediate detection and localization of road anomalies may further enable timely alerts and rapid response to potential hazards or maintenance needs, improving overall road safety and infrastructure management efficiency.
The system's ability to dynamically update predefined threshold values based on real-time measurements may allow the system to adapt to changing road conditions over time. The self-calibration feature enhances the accuracy and reliability of anomaly detection across varying environments and road types.
Integration of multiple ultrasonic sensors positioned at different angles creates a more detailed representation of the road surface. The multi-angle approach improves the system's capability to detect and characterize various types of road anomalies, from small cracks to larger potholes, providing valuable information for targeted maintenance.
The cost-effective nature of the system, utilizing affordable ultrasonic sensors and Arduino technology, makes it feasible for widespread deployment across extensive road networks. The accessibility enables comprehensive monitoring of road infrastructure, including rural and less-frequently maintained roads.
By providing continuous, real-time monitoring and generating localized alerts about road anomalies, the system may enable a proactive approach to road maintenance. The approach may potentially reduce repair costs, minimize traffic disruptions, and extend the lifespan of road infrastructure through timely interventions.
Aspects of the present disclosure are discussed here with reference to flowchart illustrations and block diagrams that depict methods, systems, and apparatus in accordance with various aspects of the present disclosure. Each block within these flowcharts and diagrams, as well as combinations of these blocks, can be executed by computer-readable program instructions. The various logical blocks, modules, circuits, and algorithm steps described in connection with the disclosed aspects may be implemented through electronic hardware, software, or a combination of both. To emphasize the interchangeability of hardware and software, the various components, blocks, modules, circuits, and steps are described generally in terms of their functionality. The decision to implement such functionality in hardware or software may be dependent on the specific application and design constraints imposed on the overall system. Person having ordinary skill in the art can implement the described functionality in different ways depending on the particular application, without deviating from the scope of the present disclosure.
The flowcharts and block diagrams presented in the figures depict the architecture, functionality, and operation of potential implementations of systems, methods, and apparatus according to different aspects of the present disclosure. Each block in the flowcharts or diagrams may represent an engine, segment, or portion of instructions comprising one or more executable instructions to perform the specified logical function(s). In some alternative implementations, the order of functions within the blocks may differ from what may be depicted. For instance, two blocks shown in sequence may be executed concurrently or in reverse order, depending on the required functionality. Each block, and combinations of blocks, can further be implemented using special-purpose hardware-based systems that perform the specified functions or tasks, or through a combination of specialized hardware and software instructions.
Although the preferred aspects have been detailed here, it should be apparent to those skilled in the relevant field that various modifications, additions, and substitutions can be made without departing from the scope of the disclosure. These variations are thus considered to be within the scope of the disclosure as defined in the following claims.
Features or functionalities described in certain example aspects may be combined and re-combined in or with other example aspects. Additionally, different aspects and elements of the disclosed example aspects may be similarly combined and re-combined. Further, some example aspects, individually or collectively, may form components of a larger system where other processes may take precedence or modify their application. Moreover, certain steps may be required before, after, or concurrently with the example aspects disclosed herein. It should be noted that any and all methods and processes disclosed herein can be performed in whole or in part by one or more entities or actors in any manner.
Although terms like "first," "second," etc., are used to describe various elements, components, regions, layers, and sections, these terms should not necessarily be interpreted as limiting. They are used solely to distinguish one element, component, region, layer, or section from another. For example, a "first" element discussed here could be referred to as a "second" element without departing from the teachings of the present disclosure.
The terminology used here may be intended to describe specific example aspects and should not be considered as limiting the disclosure. The singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "includes," "comprising," and "including," as used herein, indicate the presence of stated features, steps, elements, or components, but do not exclude the presence or addition of other features, steps, elements, or components.
As used herein, the term "or" may be intended to be inclusive, meaning that "X employs A or B" would be satisfied by X employing A, B, or both A and B. Unless specified otherwise or clearly understood from the context, the inclusive meaning applies to the term "or."
Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the relevant art. Terms should be interpreted consistently with their common usage in the context of the relevant art and should not be construed in an idealized or overly formal sense unless expressly defined here.
The terms "about" and "substantially," as used herein, refer to a variation of plus or minus 10% from the nominal value. The variation may be always included in any given measure.
In cases where other disclosures are incorporated by reference and there may be a conflict with the present disclosure, the present disclosure takes precedence to the extent of the conflict, or to provide a broader disclosure or definition of terms. If two disclosures conflict, the later-dated disclosure will take precedence.
The use of examples or exemplary language (such as "for example") may be intended to illustrate aspects of the invention and should not be seen as limiting the scope unless otherwise claimed. No language in the specification should be interpreted as implying that any non-claimed element may be essential to the practice of the invention.
While many alterations and modifications of the present invention will likely become apparent to those skilled in the art after reading the description, the specific aspects shown and described by way of illustration are not intended to be limiting in any way.
A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the scope of the disclosure. Accordingly, other implementations are within the scope of the following claims. , Claims:1. A system (100) for road infrastructure monitoring, the system (100) comprising:
a rod (102) having a horizontal portion and upwardly bent ends at angles between 30 degrees to 45 degrees;
a plurality of ultrasonic sensors (108) placed on the rod (102) in different planes, including on the upwardly bent ends;
a database (104) storing predefined threshold values for each sensor of the plurality of sensors (108); and
processing circuitry (110) configured to:
receive distance values from each sensor of the plurality of sensors (108) while a vehicle is moving;
compare each received distance value with a corresponding prestored predefined threshold value; and
generate an alert when there may be a variation between a received distance value and the corresponding prestored predefined threshold value, wherein the alert indicates localized information about road anomalies.

2. The system (100) as claimed in claim 1, wherein the processing circuitry (110) is further configured to generate a report comprising information about detected road anomalies based on the alert generated.

3. The system (100) as claimed in claim 1, wherein the plurality of ultrasonic sensors (108) are arranged along the length of the rod (102) to provide a wide coverage area for sensing road surface conditions.

4. The system (100) as claimed in claim 1, wherein the predefined threshold values stored in the database (104) indicate distances from the sensors (108) to the road surface when the road is free of any anomalies.
5. The system (100) as claimed in claim 4, wherein the processing circuitry (110) is further configured to update the predefined threshold values in the database (104) based on real-time measurements of road surface conditions.

6. A method (300) for road infrastructure monitoring, the method (300) comprising:
providing (302), a rod (102) having a horizontal portion and upwardly bent ends at angles between 30 degrees to 45 degrees;
placing (304), a plurality of ultrasonic sensors (108) on the rod (102) in different planes, including on the bent portions;
storing (306), predefined threshold values for each sensor of the plurality of sensors (108) in a database (104);
receiving (308), distance values from each sensor of the plurality of sensors (108) while a vehicle is moving;
comparing (310), each received distance value with a corresponding prestored predefined threshold value;
checking (312) for variation between each received distance value with the corresponding prestored predefined threshold value; and
generating (314), an alert when there is a variation between a received distance value and the corresponding prestored predefined threshold value, wherein the alert indicates localized information about road anomalies.

7. The method (300) as claimed in claim 6, further comprising:
generating (316), a report comprising information about detected road anomalies based on the alerts generated.

8. The method (300) as claimed in claim 6, wherein the predefined threshold values stored in the database (104) indicate distances from the sensors (108) to the road surface when the road does not have any anomalies.

9. The method (300) as claimed in claim 8, further comprising:
updating (318), the predefined threshold values in the database (104) based on real-time measurements of road surface conditions.

10. The method (300) as claimed in claim 9, wherein updating the predefined threshold values comprising:
calculating an average of distance measurements from each sensor (108) over a predetermined time period while the vehicle is moving on a road section known to be free of anomalies; and
storing the calculated averages as updated predefined threshold values for each corresponding sensor (108) in the database (104).