Zobrazeno 1 - 10
of 120
pro vyhledávání: '"Zgonnikov, Arkady"'
Understanding human behavior in overtaking scenarios is crucial for enhancing road safety in mixed traffic with automated vehicles (AVs). Computational models of behavior play a pivotal role in advancing this understanding, as they can provide insigh
Externí odkaz:
http://arxiv.org/abs/2403.19637
Autor:
Bontje, Floor, Zgonnikov, Arkady
When a person makes a decision, it is automatically accompanied by a subjective probability judgment of the decision being correct, in other words, a confidence judgment. A better understanding of the mechanisms responsible for these confidence judgm
Externí odkaz:
http://arxiv.org/abs/2403.06496
Unprecedented possibilities of quadruped robots have driven much research on the technical aspects of these robots. However, the social perception and acceptability of quadruped robots so far remain poorly understood. This work investigates whether t
Externí odkaz:
http://arxiv.org/abs/2403.05400
Autor:
Caregnato-Neto, Angelo, Siebert, Luciano Cavalcante, Zgonnikov, Arkady, Maximo, Marcos Ricardo Omena de Albuquerque, Afonso, Rubens Junqueira Magalhães
One of the key issues in human-robot collaboration is the development of computational models that allow robots to predict and adapt to human behavior. Much progress has been achieved in developing such models, as well as control techniques that addr
Externí odkaz:
http://arxiv.org/abs/2402.19128
The use of partially automated driving systems raises concerns about potential responsibility issues, posing risk to the system safety, acceptance, and adoption of these technologies. The concept of meaningful human control has emerged in response to
Externí odkaz:
http://arxiv.org/abs/2402.08080
Autor:
Garzás-Villar, Alberto, Boersma, Caspar, Derumigny, Alexis, Zgonnikov, Arkady, Marchal-Crespo, Laura
The provision of robotic assistance during motor training has proven to be effective in enhancing motor learning in some healthy trainee groups as well as patients. Personalizing such robotic assistance can help further improve motor (re)learning out
Externí odkaz:
http://arxiv.org/abs/2402.06325
Autonomous vehicles rely on accurate trajectory prediction to inform decision-making processes related to navigation and collision avoidance. However, current trajectory prediction models show signs of overfitting, which may lead to unsafe or subopti
Externí odkaz:
http://arxiv.org/abs/2402.01397
The estimation of probability density functions is a fundamental problem in science and engineering. However, common methods such as kernel density estimation (KDE) have been demonstrated to lack robustness, while more complex methods have not been e
Externí odkaz:
http://arxiv.org/abs/2401.10566
One of the bottlenecks of automated driving technologies is safe and socially acceptable interactions with human-driven vehicles, for example during merging. Driver models that provide accurate predictions of joint and individual driver behaviour of
Externí odkaz:
http://arxiv.org/abs/2312.09776
Detecting abnormal driving behavior is critical for road traffic safety and the evaluation of drivers' behavior. With the advancement of machine learning (ML) algorithms and the accumulation of naturalistic driving data, many ML models have been adop
Externí odkaz:
http://arxiv.org/abs/2312.04610