1 citations · 2 across the 4 of their papers we have counts for
4 papers
Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception
Gledson Melotti, Johann J. S. Bastos, Bruno L. S. da Silva +2
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…
Reducing Overconfidence Predictions for Autonomous Driving Perception
Gledson Melotti, Cristiano Premebida, Jordan J. Bird +2
In state-of-the-art deep learning for object recognition, SoftMax and Sigmoid functions are most commonly employed as the predictor outputs. Such layers often produce overconfident…
Probabilistic Approach for Road-Users Detection
G. Melotti, W. Lu, P. Conde +4
Object detection in autonomous driving applications implies that the detection and tracking of semantic objects are commonly native to urban driving environments, as pedestrians an…
Probabilistic Object Classification using CNN ML-MAP layers
G. Melotti, C. Premebida, J. J. Bird +2
Deep networks are currently the state-of-the-art for sensory perception in autonomous driving and robotics. However, deep models often generate overconfident predictions precluding…