  Object detection in autonomous driving applications implies that the
detection and tracking of semantic objects are commonly native to urban driving
environments, as pedestrians and vehicles. One of the major challenges in
state-of-the-art deep-learning based object detection are false positives which
occur with overconfident scores. This is highly undesirable in autonomous
driving and other critical robotic-perception domains because of safety
concerns. This paper proposes an approach to alleviate the problem of
overconfident predictions by introducing a novel probabilistic layer to deep
object detection networks in testing. The suggested approach avoids the
traditional Sigmoid or Softmax prediction layer which often produces
overconfident predictions. It is demonstrated that the proposed technique
reduces overconfidence in the false positives without degrading the performance
on the true positives. The approach is validated on the 2D-KITTI objection
detection through the YOLOV4 and SECOND (Lidar-based detector). The proposed
approach enables interpretable probabilistic predictions without the
requirement of re-training the network and therefore is very practical.
