Leveraging Monte Carlo Dropout for Uncertainty Quantification in Real-Time Object Detection of Autonomous Vehicles
With the recent advancements in machine learning technology, the accuracy of autonomous driving object detection models has significantly improved. However, due to the complexity and variability of real-world traffic scenarios, such as extreme weather conditions, unconventional lighting, and unknown...
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| Hoofdauteurs: | , , , , |
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| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
IEEE
2024-01-01
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| Reeks: | IEEE Access |
| Onderwerpen: | |
| Online toegang: | https://ieeexplore.ieee.org/document/10401930/ |
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