A Fast Uncertainty Estimation Method for Autonomous Driving Perception
In the visual perception task of autonomous driving,it is crucial to accurately and quickly extract the cognitive and accidental uncertainties to effectively resolve the Safety of the Intended Functionality(SOTIF)issues associated with autonomous driving.In traditional methods such as Monte Carlo dropout and deep ensembles,uncertainty is estimated by sampling the prediction results of different sub-models,which slows down the estimation and tends to occupy a large amount of memory in the processor during the model inference stage.A fast Monte Carlo dropout method and a technique for correcting subsequent detection results are proposed to address the issues of slow estimation of uncertainty in Monte Carlo dropout and the selection of subsequent detection results.This method uses a multi-head mechanism to replace the traditional multiple sampling mechanism in Monte Carlo dropout,thereby saving time in both sampling and inference throughout the uncertainty estimation process.
autonomous drivinguncertainty estimationobject detectionsafety of the intended functionality
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