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A Compressive Sensing Approach for Connected Vehicle Data Capture and Recovery and its Impact on Travel Time Estimation
Connected vehicles (CVs) can capture and transmit detailed data such as ...
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Efficient Collection of Connected Vehicles Data with Precision Guarantees
Connected vehicles disseminate detailed data, including their position a...
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CSVideoNet: A Real-time End-to-end Learning Framework for High-frame-rate Video Compressive Sensing
This paper addresses the real-time encoding-decoding problem for high-fr...
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How to find real-world applications for compressive sensing
The potential of compressive sensing (CS) has spurred great interest in ...
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Real-Time Object Detection and Localization in Compressive Sensed Video on Embedded Hardware
Every day around the world, interminable terabytes of data are being cap...
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Extracting useful information from Basic Safety Message Data: An empirical study of driving volatility measures and crash frequency at intersections
With the emergence of high-frequency connected and automated vehicle dat...
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Extracting V2V Encountering Scenarios from Naturalistic Driving Database
It is necessary to thoroughly evaluate the effectiveness and safety of C...
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Efficient Collection of Connected Vehicle Data based on Compressive Sensing
Connected vehicles (CVs) can capture and transmit detailed data like vehicle position, speed and so on through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data provides new opportunities to improve the safety, mobility, and sustainability of transportation systems. However, the potential data explosion likely will overburden storage and communication systems. To solve this issue, we design a real-time compressive sensing (CS) approach which allows CVs to collect and compress data in real-time and can recover the original data accurately and efficiently when it is necessary. The CS approach is applied to recapture 10 million CV Basic Safety Message speed samples from the Safety Pilot Model Deployment program. With a compression ratio of 0.2, it is found that the CS approach can recover the original speed data with the root mean squared error as low as 0.05. The recovery performances of the CS approach are further explored by time-of-day and acceleration. The results show that the CS approach performs better in data recovery when CV speeds are steady or changing smoothly.
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