Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Oliver De Candido"'
Publikováno v:
IEEE Transactions on Intelligent Vehicles. 8:1837-1851
Publikováno v:
IEEE Transactions on Intelligent Vehicles. :1-8
Publikováno v:
Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems.
Publikováno v:
MaxEnt 2022.
Parametric and non-parametric classifiers often have to deal with real-world data, where corruptions like noise, occlusions, and blur are unavoidable - posing significant challenges. We present a probabilistic approach to classify strongly corrupted
We investigate the distributional shifts between datasets which pose a challenge to validate safety critical driving functions which incorporate Machine Learning (ML)-based algorithms. First, we describe the possible distributional shifts which can o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05e0a8e61882a34854352e59b7001250
https://mediatum.ub.tum.de/1686444
https://mediatum.ub.tum.de/1686444
Publikováno v:
ITSC
This paper proposes an interpretable machine learning structure for the early prediction of lane changes. The interpretability relies on interpretable templates, as well as constrained weights during the training process of a neural network. It is sh
Autor:
Oliver De Candido, Moritz Sackmann, Wolfgang Utschick, Ulrich Hofmann, Fabian Konstantinidis, Jorn Thielecke
Publikováno v:
ITSC
Modeling other drivers' behavior in highly interactive traffic situations, such as roundabouts, is a challenging task. We address this task using a Multi-Agent Reinforcement Learning (MARL) approach that learns a driving policy based on a minimal set
Publikováno v:
2021 IEEE Intelligent Vehicles Symposium (IV).
In this paper, we address the challenge of employing Machine Learning (ML) algorithms in safety critical driving functions. Despite ML algorithms demonstrating good performance in various driving tasks, e.g., detecting when other vehicles are going t
Publikováno v:
IEEE Transactions on Wireless Communications. 18:254-267
In this contribution, we investigate a coarsely quantized Multi-User (MU)-Multiple Input Single Output (MISO) downlink communication system, where we assume 1-Bit Digital-to-Analog Converters (DACs) at the Base Station (BS) antennas. First, we analyz
Publikováno v:
KI 2021: Advances in Artificial Intelligence ISBN: 9783030876258
KI
KI
Given the increasing threat of adversarial attacks on deep neural networks (DNNs), research on efficient detection methods is more important than ever. In this work, we take a closer look at adversarial attack detection based on the class scores of a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b17b6b7ae4aeb386938c390a6b47b49
https://doi.org/10.1007/978-3-030-87626-5_17
https://doi.org/10.1007/978-3-030-87626-5_17