Performance evaluation
Safety of the Intended Functionality (SOTIF) addresses sensor performance limitations and deep learning-based object detection insufficiencies to ensure the intended functionality of Automated Driving Systems (ADS). This paper presents a methodology examining the adaptability and performance evaluation of the 3D object detection methods on a LiDAR point cloud dataset generated by simulating a SOTIF-related Use Case.
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This dataset is an experimental result of the paper “Performance Evaluation for Geographically Distributed Blockchain-based Services in a Cloud Computing Environment”. The Geographically Distributed Cloud Performance Evaluation Ambassador (GDCPEA) is deployed on each Go Ethereum (Geth) node to measure the elapsed time from the start to the end of the Geth main operations.
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Data validation of the article "A framework for assessing, comparing and predicting the performance of autonomous RFID-based inventory robots for retail"
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This dataset contains the performance models, simulation and monitoring results, and analysis scripts that we used for our evaluation.
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