Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception
This work addresses the critical issue of misclassification in autonomous vehicles to prevent accidents, though it appears incremental as it applies Bayesian inference to existing methods.
The paper tackles the problem of reducing false positive rates in autonomous driving perception systems by using Bayesian inference with multisensory and multimodality approaches, achieving validation on the KITTI dataset with deep networks and 3D point cloud networks for object categories like cars, cyclists, and pedestrians.
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.