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In state-of-the-art deep learning for object recognition, SoftMax and Sigmoidfunctions are most commonly employed as the predictor outputs. Such layersoften produce overconfident predictions rather than proper probabilisticscores, which can thus harm the decision-making of `critical' perceptionsystems applied in autonomous driving and robotics. Given this, the experimentsin this work propose a probabilistic approach based on distributions calculatedout of the Logit layer scores of pre-trained networks. We demonstrate thatMaximum Likelihood (ML) and Maximum a-Posteriori (MAP) functions are moresuitable for probabilistic interpretations than SoftMax and Sigmoid-basedpredictions for object recognition. We explore distinct sensor modalities viaRGB images and LiDARs (RV: range-view) data from the KITTI and Lyft Level-5datasets, where our approach shows promising performance compared to the usualSoftMax and Sigmoid layers, with the benefit of enabling interpretableprobabilistic predictions. Another advantage of the approach introduced in thispaper is that the ML and MAP functions can be implemented in existing trainednetworks, that is, the approach benefits from the output of the Logit layer ofpre-trained networks. Thus, there is no need to carry out a new training phasesince the ML and MAP functions are used in the test/prediction phase.

Reducing Overconfidence Predictions for Autonomous Driving Perception