The simplest modeling approach is to set a linear trajectory motion with an additional uncertainty term. This assumption has important properties such as the ...
Jun 9, 2022 · A novel approach for vehicle tracking using a hybrid adaptive Kalman filter is proposed. The filter utilizes recurrent neural networks to learn the vehicle's ...
Dec 9, 2024 · Abstract. A novel approach for vehicle tracking using a hybrid adaptive Kalman filter is proposed. The filter utilizes recurrent neural networks ...
A novel approach for vehicle tracking using a hybrid adaptive Kalman filter is proposed. The filter utilizes recurrent neural networks to learn the ...
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In this study, we propose a novel framework that can model the uncertainty ... deep learning-based methods significantly outperform normal vehicle trajectory.
We are the first to model the multivariate predictive uncertainty in pedestrian trajectory prediction by incorporating mathematical conditions to ensure ...
Jun 17, 2024 · Trajectory prediction models that can infer both finite future trajectories and their associated uncertainties of the target vehicles in an ...
Fingerprint. Dive into the research topics of 'Learning vehicle trajectory uncertainty'. Together they form a unique fingerprint.
A crucial part of vehicle-related deep learning tasks is reasoning about the uncertainty of their outputs and knowing when the outputs are unreliable. A ...
This paper addresses the critical need to quantify vehicle trajectory uncertainty in autonomous driving under environmental variability.