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To assure that an autonomous car is driving safely on public roads, itsobject detection module should not only work correctly, but show its predictionconfidence as well. Previous object detectors driven by deep learning do notexplicitly model uncertainties in the neural network. We tackle with thisproblem by presenting practical methods to capture uncertainties in a 3Dvehicle detector for Lidar point clouds. The proposed probabilistic detectorrepresents reliable epistemic uncertainty and aleatoric uncertainty inclassification and localization tasks. Experimental results show that theepistemic uncertainty is related to the detection accuracy, whereas thealeatoric uncertainty is influenced by vehicle distance and occlusion. Theresults also show that we can improve the detection performance by 1%-5% bymodeling the aleatoric uncertainty.

Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection