Towards Trustworthy AI: Analyzing Model Uncertainty through Monte Carlo Dropout and Noise Injection
Autonomous vehicles require intelligent computer vision (CV) to perform critical navigational perception tasks. To achieve this, sensors such as camera, LiDAR and radar are utilized to provide data to artificial intelligence (AI) systems. Continuous monitoring of these intelligent CV systems is req...
Saved in:
| Main Authors: | Chern Chao Tai, Wesam Al Amiri, Abhijeet Solanki, Douglas Alan Talbert, Nan Guo, Syed Rafay Hasan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2025-05-01
|
| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/138945 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Monte Carlo Dropout Neural Networks for Forecasting Sinusoidal Time Series: Performance Evaluation and Uncertainty Quantification
by: Unyamanee Kummaraka, et al.
Published: (2025-04-01) -
MONTE-CARLO FORECASTING OF LIQUIDITY UNDER UNCERTAINTY
by: P. Timoshenko
Published: (2016-02-01) -
Survey of Navigational Perception Sensors’ Security in Autonomous Vehicles
by: Abhijeet Solanki, et al.
Published: (2025-01-01) -
Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports
by: Unyamanee Kummaraka, et al.
Published: (2024-08-01) -
Energy Hub Operation Under Uncertainty: Monte Carlo Risk Assessment Using Gaussian and KDE-Based Data
by: Spyros Giannelos, et al.
Published: (2025-03-01)