AI Summary & Key Takeaways
AI InsightThe stored data has created a high-level sensor fusion at the point of decision making with supervised learning artificial intelligence algorithms
📌 3 Key Takeaways
Abstract
Sensor fusion is a method of processing data from raw data to meaningful outputs and getting quality output. Architectures used in sensor fusion are chosen depending on the application. The sensor fusion architecture that is frequently used today was found by the directors of the United States Joint Laboratory (JDL). Sensor fusion has been realized with this architecture. Using the axial data of a car, inertial movements such as acceleration, deceleration and stationary are classified as controlled. At the classification point, low level and high-level methods are used in the sensor fusion application. By pre-processing the received data, joint high-quality data was obtained with complementary sensor modeling, and high-level sensor fusion methods were used after recording the obtained data. Artificial intelligence algorithms are preferred for high-level sensor fusion. Various algorithms such as "Decision Tree", "Gradient Boosting", "Multi-Layer Perceptron", "Regression Algorithm" have been used. Real-time acquired data is stored after preprocessing and raw data fusion. The stored data has created a high-level sensor fusion at the point of decision making with supervised learning artificial intelligence algorithms