  Object recognition is a crucial step in perception systems for autonomous and
intelligent vehicles, as evidenced by the numerous research works in the topic.
In this paper, object recognition is explored by using multisensory and
multimodality approaches, with the intention of reducing the false positive
rate (FPR). The reduction of the FPR becomes increasingly important in
perception systems since the misclassification of an object can potentially
cause accidents. In particular, this work presents a strategy through Bayesian
inference to reduce the FPR considering the likelihood function as a cumulative
distribution function from Gaussian kernel density estimations, and the prior
probabilities as cumulative functions of normalized histograms. The validation
of the proposed methodology is performed on the KITTI dataset using deep
networks (DenseNet, NasNet, and EfficientNet), and recent 3D point cloud
networks (PointNet, and PintNet++), by considering three object-categories
(cars, cyclists, pedestrians) and the RGB and LiDAR sensor modalities.

Computer Vision and Pattern Recognition;Artificial Intelligence