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This solution focuses on real-time vehicle tracking using GNSS and OBU data, leveraging various techniques like data filtering, map alignment, and machine learning. The system collects data from GNSS for geographical positions and OBU for vehicle diagnostics. The data undergoes preprocessing (e.g., Kalman filtering for noise reduction), and then it’s integrated and aligned with maps using GIS tools. A machine learning model is trained on this aligned data to predict vehicle movements, which is later applied to process real-time data. Additionally, the solution incorporates encryption for data privacy and displays the vehicle's locations on a map using visualization tools. This combination of preprocessing, real-time processing, and secure data handling ensures accurate and efficient vehicle tracking. You can also extend this approach to other technologies, similar to your interest in AI/ML for space technology, where similar models can track satellite or spacecraft data.
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This solution focuses on real-time vehicle tracking using GNSS and OBU data, leveraging various techniques like data filtering, map alignment, and machine learning. The system collects data from GNSS for geographical positions and OBU for vehicle diagnostics. The data undergoes preprocessing (e.g., Kalman filtering for noise reduction), and then it’s integrated and aligned with maps using GIS tools. A machine learning model is trained on this aligned data to predict vehicle movements, which is later applied to process real-time data.
Additionally, the solution incorporates encryption for data privacy and displays the vehicle's locations on a map using visualization tools. This combination of preprocessing, real-time processing, and secure data handling ensures accurate and efficient vehicle tracking.
You can also extend this approach to other technologies, similar to your interest in AI/ML for space technology, where similar models can track satellite or spacecraft data.
new NASA (1).pdf