2 research outputs found
Probabilistic Approach for Road-Users Detection
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 is false positive which
occurrences 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 enabling interpretable probabilistic predictions without the
requirement of re-training the network and therefore is very practical.Comment: This work has been submitted to IEEE T-ITS for review and possible
publicatio
Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception
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.Comment: This paper has been submitted to the journal Pattern Recognition
Letter