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

• URL: http://arxiv.org/abs/2211.04340v1
• Date: Tue, 8 Nov 2022 16:01:17 GMT
• Title: Calibrated Perception Uncertainty Across Objects and Regions in Bird's-Eye-View
• Authors: Markus K\"angsepp, Meelis Kull
• Abstract summary: We highlight limitations in the state-of-the-art and propose a more complete set of uncertainties to be reported. We demonstrate that the obtained probabilities are not calibrated out-of-the-box and propose methods to achieve well-calibrated uncertainties.
• Score: 2.3943776585660976
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 by 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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