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Workshop: Machine Learning for Autonomous Driving

Calibrated Perception Uncertainty Across Objects and Regions in Bird's-Eye-View

Markus Kängsepp · Meelis Kull


In driving scenarios with poor visibility or occlusions, it is important that the autonomous vehicle would take into account all the uncertainties when making driving decisions, including choice of a safe speed. The grid-based perception outputs such as occupancy grids and object-based outputs such as lists of detected objects must then be accompanied with well-calibrated uncertainty estimates. We highlight limitations in the state-of-the-art and propose a more complete set of uncertainties to be reported, particularly including undetected-object-ahead probabilities. We suggest a novel way to get these probabilistic outputs from bird’s-eye-view probabilistic semantic segmentation, in the example of the FIERY model. We demonstrate that the obtained probabilities are not calibrated out-of-the-box and propose methods to achieve well-calibrated uncertainties.

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