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pro vyhledávání: '"Schumann, Julian"'
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
Understanding traffic participants' behaviour is crucial for predicting their future trajectories, aiding in developing safe and reliable planning systems for autonomous vehicles. Integrating cognitive processes and machine learning models has shown
Externí odkaz:
http://arxiv.org/abs/2305.19678
Autor:
Schumann, Julian F., Srinivasan, Aravinda Ramakrishnan, Kober, Jens, Markkula, Gustav, Zgonnikov, Arkady
The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable models predicting human behavior are essential for overcoming this issu
Externí odkaz:
http://arxiv.org/abs/2305.15187
Autor:
Srinivasan, Aravinda Ramakrishnan, Schumann, Julian, Wang, Yueyang, Lin, Yi-Shin, Daly, Michael, Solernou, Albert, Zgonnikov, Arkady, Leonetti, Matteo, Billington, Jac, Markkula, Gustav
Accurate modelling of road user interaction has received lot of attention in recent years due to the advent of increasingly automated vehicles. To support such modelling, there is a need to complement naturalistic datasets of road user interaction wi
Externí odkaz:
http://arxiv.org/abs/2305.11909
Predicting the future behavior of human road users is an important aspect for the development of risk-aware autonomous vehicles. While many models have been developed towards this end, effectively capturing and predicting the variability inherent to
Externí odkaz:
http://arxiv.org/abs/2304.05166
Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous v
Externí odkaz:
http://arxiv.org/abs/2211.05455
Finding global optima in high-dimensional optimization problems is extremely challenging since the number of function evaluations required to sufficiently explore the search space increases exponentially with its dimensionality. Furthermore, multimod
Externí odkaz:
http://arxiv.org/abs/2110.14985
Akademický článek
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Predicting the future behavior of human road users remains an open challenge for the development of risk-aware autonomous vehicles. An important aspect of this challenge is effectively capturing the uncertainty inherent to human behavior. This paper
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9525b2b1b85905be1cb3652ee22186dc
http://arxiv.org/abs/2304.05166
http://arxiv.org/abs/2304.05166