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Reliable uncertainty estimation is crucial for perception systems in safeautonomous driving. Recently, many methods have been proposed to modeluncertainties in deep learning based object detectors. However, the estimatedprobabilities are often uncalibrated, which may lead to severe problems insafety critical scenarios. In this work, we identify such uncertaintymiscalibration problems in a probabilistic LiDAR 3D object detection network,and propose three practical methods to significantly reduce errors inuncertainty calibration. Extensive experiments on several datasets show thatour methods produce well-calibrated uncertainties, and generalize well betweendifferent datasets.

Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving