Street Quality Mapper: Real-time Pothole Identification and Street Quality Mapping using Signal Processing

Belapurkar, Gayatri; Bellara, Chirag; Sahu, Gaurav; Sahu, Sahil; Nehete, Charusheela

doi:10.1109/icnte51185.2021.9487763

Cited by 1 publication

(3 citation statements)

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“…Numerous RPCM solutions have been proposed in the existing literature, utilizing acc vibration data as a key input [2], [11], [14], [15], [17], [42], [63]. For example, a vehicle-asa-Sensor solution presented in [2] demonstrates the ability to classify road pavement into different categories (e.g., smooth, rough, and bumpy) and detect road potholes, with the sensor data transmitted to a OneM2M cloud platform.…”

Section: A Threshold Based Monitoringmentioning

confidence: 99%

“…Similarly, an Android app solution proposed in [14] enables the streaming of road pothole information to a web application developed in Scala Play, providing valuable information to drivers. In [17], the study calculates the Road's Ride Quality Score by utilizing acc data from the x, y, and z axes, ensuring accuracy through data cleansing and time synchronization with GPS data. The ride quality score is then assigned to different road sections on a map and displayed on a web interface for commuters to access.…”

Section: A Threshold Based Monitoringmentioning

confidence: 99%

“…This placement facilitates the capture of vibrations and movements more accurately, providing a more reliable representation of the actual road surface condition. By directly integrating the sensing platform with the vehicle's suspension system, the readings can better reflect the dynamic nature of the road, enabling improved monitoring capabilities [17].…”

Section: ) Sensing Platform Placementmentioning

confidence: 99%

See 2 more Smart Citations

Crowdsensing for Road Pavement Condition Monitoring: Trends, Limitations, and Opportunities

Jan,

Khattak,

Khan

et al. 2023

IEEE Access

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RPCM forms a crucial element of preventive maintenance strategies, particularly in light of escalating vehicular pressures and the advent of extreme weather patterns. Consequently, there is a growing demand for cost-effective solutions that leverage emergent technologies such as the IoT, AI, and cloud computing. This research intends to articulate the evolutionary trajectory of road solutions while delineating the prevalent challenges and offering viable trajectories for future enhancements. To achieve this objective, a systematic literature review was executed using the Scopus and Web of Science databases, the aim of which was to discern the inherent challenges of existing solutions. Following a stringent elimination process of duplicates and irrelevant studies, a corpus of 74 research papers was assembled for review. Assessment criteria encompassed the sensing platforms and algorithms deployed, the variety of road deformities detected, and the overall accuracy of the proposed solutions. The analysis revealed a variety of methodologies applied to RPCM, each bearing distinct advantages and limitations. Notably, SPbased monitoring solutions utilizing ML techniques and improved data gathering methodologies exhibited superior outcomes relative to alternative approaches. To conclude, this research elucidates the wide-ranging methodologies in RPCM, critically examining their respective advantages and drawbacks. Among the methodologies surveyed, SP-based monitoring deploying ML techniques emerges as a compelling approach, demonstrating the potential for enhancing accuracy and data gathering techniques. These insights form a valuable foundation for the conception and development of future cost-effective and efficacious RPCM solutions.

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