Multiple trajectory prediction with deep temporal and spatial convolutional neural networks
Automated vehicles need to not only perceive their environment, but also predict the
possible future behavior of all detected traffic participants in order to safely navigate in …
possible future behavior of all detected traffic participants in order to safely navigate in …
Predicting motion of vulnerable road users using high-definition maps and efficient convnets
Following detection and tracking of traffic actors, prediction of their future motion is the next
critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate …
critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate …
Motioncnn: A strong baseline for motion prediction in autonomous driving
S Konev, K Brodt, A Sanakoyeu - arXiv preprint arXiv:2206.02163, 2022 - arxiv.org
To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of
other agents around it. Motion prediction is an extremely challenging task that recently …
other agents around it. Motion prediction is an extremely challenging task that recently …
Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving
We address one of the crucial aspects necessary for safe and efficient operations of
autonomous vehicles, namely predicting future state of traffic actors in the autonomous …
autonomous vehicles, namely predicting future state of traffic actors in the autonomous …
Trajectory prediction with graph-based dual-scale context fusion
Motion prediction for traffic participants is essential for a safe and robust automated driving
system, especially in cluttered urban environments. However, it is highly challenging due to …
system, especially in cluttered urban environments. However, it is highly challenging due to …
Decoder fusion rnn: Context and interaction aware decoders for trajectory prediction
Forecasting the future behavior of all traffic agents in the vicinity is a key task to achieve safe
and reliable autonomous driving systems. It is a challenging problem as agents adjust their …
and reliable autonomous driving systems. It is a challenging problem as agents adjust their …
Tpnet: Trajectory proposal network for motion prediction
Making accurate motion prediction of the surrounding traffic agents such as pedestrians,
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …
Hierarchical motion encoder-decoder network for trajectory forecasting
Q Xue, S Li, X Li, J Zhao, W Zhang - arXiv preprint arXiv:2111.13324, 2021 - arxiv.org
Trajectory forecasting plays a pivotal role in the field of intelligent vehicles or social robots.
Recent works focus on modeling spatial social impacts or temporal motion attentions, but …
Recent works focus on modeling spatial social impacts or temporal motion attentions, but …
Interacting vehicle trajectory prediction with convolutional recurrent neural networks
S Mukherjee, S Wang, A Wallace - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Anticipating the future trajectories of surrounding vehicles is a crucial and challenging task
in path planning for autonomy. We propose a novel Convolutional Long Short Term Memory …
in path planning for autonomy. We propose a novel Convolutional Long Short Term Memory …
Traffic agent trajectory prediction using social convolution and attention mechanism
The trajectory prediction is significant for the decision-making of autonomous driving
vehicles. In this paper, we propose a model to predict the trajectories of target agents around …
vehicles. In this paper, we propose a model to predict the trajectories of target agents around …