Information Density quantified by phase in eigen space and mutual information enables virtual sensing that replaces physical sensors with under 3.21% mean error on real Madrid smart-city data.
Semantic-aware video compres- sion for automotive cameras
2 Pith papers cite this work. Polarity classification is still indexing.
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The paper introduces a safety framework for datasets in autonomous driving that uses the AI Data Flywheel and lifecycle processes to identify hazards and ensure compliance with ISO/PAS 8800.
citing papers explorer
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Information Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits
Information Density quantified by phase in eigen space and mutual information enables virtual sensing that replaces physical sensors with under 3.21% mean error on real Madrid smart-city data.
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Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance
The paper introduces a safety framework for datasets in autonomous driving that uses the AI Data Flywheel and lifecycle processes to identify hazards and ensure compliance with ISO/PAS 8800.